Health systems readiness and management of febrile outpatients under low malaria transmission in Vanuatu.

PubMed

Zurovac, Dejan; Guintran, Jean-Olivier; Donald, Wesley; Naket, Esau; Malinga, Josephine; Taleo, George

2015-12-02

Vanuatu, an archipelago country in Western Pacific harbouring low Plasmodium falciparum and Plasmodium vivax malaria transmission, has been implementing a malaria case management policy, recommending parasitological testing of patients with fever and anti-malarial treatment for test-positive only patients. A health facility survey to evaluate the health systems readiness to implement the policy and the quality of outpatient management for patients with fever was undertaken. A cross-sectional, cluster sample survey, using a range of quality-of-care methods, included all health centres and hospitals in Vanuatu. The main outcome measures were coverage of health facilities and health workers with commodities and support interventions, adherence to test and treatment recommendations, and factors influencing malaria testing. The survey was undertaken in 2014 during the low malaria season and included 41 health facilities, 67 health workers and 226 outpatient consultations for patients with fever. All facilities had capacity for parasitological diagnosis, 95.1 % stocked artemether-lumefantrine and 63.6 % primaquine. The coverage of health workers with support interventions ranged from 50 to 70 %. Health workers' knowledge was high only regarding treatment policy for uncomplicated P. falciparum malaria (83.4 %). History taking and clinical examination practices were sub-optimal. Some 35.0 % (95 % CI 23.4-48.6) of patients with fever were tested for malaria, of which all results were negative and only one patient received anti-malarial treatment. Testing was significantly higher for patients age 5 years and older (OR = 2.33; 95 % CI 1.48-5.02), seen by less qualified health workers (OR = 2.73; 95 % CI 1.48-5.02), health workers who received malaria case management training (OR = 2.39; 95 % CI 1.28-4.47) and patients with increased temperature (OR = 2.56; 95 % CI 1.17-5.57), main complaint of fever (OR = 5.82; 95 % CI 1.26-26.87) and without runny nose (OR = 3.75; 95 % CI 1.36-10.34). Antibiotic use was very high (77.4 %) with sub-optimal dispensing and counselling practices. Health facility and health worker readiness to implement policy is higher for falciparum than vivax malaria. Clinical and malaria testing practices are sub-optimal, however adherence to test negative results is nearly universal. Use of antibiotics is irrational. Quantitative and qualitative improvements of ongoing interventions are needed to re-inforce clinical practices in this area characterized by difficult access, human resource shortages but aspiring towards malaria elimination.

Robust stochastic optimization for reservoir operation

NASA Astrophysics Data System (ADS)

Pan, Limeng; Housh, Mashor; Liu, Pan; Cai, Ximing; Chen, Xin

2015-01-01

Optimal reservoir operation under uncertainty is a challenging engineering problem. Application of classic stochastic optimization methods to large-scale problems is limited due to computational difficulty. Moreover, classic stochastic methods assume that the estimated distribution function or the sample inflow data accurately represents the true probability distribution, which may be invalid and the performance of the algorithms may be undermined. In this study, we introduce a robust optimization (RO) approach, Iterative Linear Decision Rule (ILDR), so as to provide a tractable approximation for a multiperiod hydropower generation problem. The proposed approach extends the existing LDR method by accommodating nonlinear objective functions. It also provides users with the flexibility of choosing the accuracy of ILDR approximations by assigning a desired number of piecewise linear segments to each uncertainty. The performance of the ILDR is compared with benchmark policies including the sampling stochastic dynamic programming (SSDP) policy derived from historical data. The ILDR solves both the single and multireservoir systems efficiently. The single reservoir case study results show that the RO method is as good as SSDP when implemented on the original historical inflows and it outperforms SSDP policy when tested on generated inflows with the same mean and covariance matrix as those in history. For the multireservoir case study, which considers water supply in addition to power generation, numerical results show that the proposed approach performs as well as in the single reservoir case study in terms of optimal value and distributional robustness.

Spatial targeting of agri-environmental policy using bilevel evolutionary optimization

USDA-ARS?s Scientific Manuscript database

In this study we describe the optimal designation of agri-environmental policy as a bilevel optimization problem and propose an integrated solution method using a hybrid genetic algorithm. The problem is characterized by a single leader, the agency, that establishes a policy with the goal of optimiz...

Optimal Limited Contingency Planning

NASA Technical Reports Server (NTRS)

Meuleau, Nicolas; Smith, David E.

2003-01-01

For a given problem, the optimal Markov policy over a finite horizon is a conditional plan containing a potentially large number of branches. However, there are applications where it is desirable to strictly limit the number of decision points and branches in a plan. This raises the question of how one goes about finding optimal plans containing only a limited number of branches. In this paper, we present an any-time algorithm for optimal k-contingency planning. It is the first optimal algorithm for limited contingency planning that is not an explicit enumeration of possible contingent plans. By modelling the problem as a partially observable Markov decision process, it implements the Bellman optimality principle and prunes the solution space. We present experimental results of applying this algorithm to some simple test cases.

Coordination of a supply chain with consumer return under vendor-managed consignment inventory and stochastic demand

NASA Astrophysics Data System (ADS)

Wu, Zhihui; Chen, Dongyan; Yu, Hui

2016-07-01

In this paper, the problem of the coordination policy is investigated for vendor-managed consignment inventory supply chain subject to consumer return. Here, the market demand is assumed to be affected by promotional effort and consumer return policy. The optimal consignment inventory and the optimal promotional effort level are proposed under the decentralized and centralized decisions. Based on the optimal decision conditions, the markdown allowance-promotional cost-sharing contract is investigated to coordinate the supply chain. Subsequently, the comparison between the two extreme policies shows that full-refund policy dominates the no-return policy when the returning cost and the positive effect of return policy are satisfied certain conditions. Finally, a numerical example is provided to illustrate the impacts of consumer return policy on the coordination contract and optimal profit as well as the effectiveness of the proposed supply chain decision.

Financing and funding health care: Optimal policy and political implementability.

PubMed

Nuscheler, Robert; Roeder, Kerstin

2015-07-01

Health care financing and funding are usually analyzed in isolation. This paper combines the corresponding strands of the literature and thereby advances our understanding of the important interaction between them. We investigate the impact of three modes of health care financing, namely, optimal income taxation, proportional income taxation, and insurance premiums, on optimal provider payment and on the political implementability of optimal policies under majority voting. Considering a standard multi-task agency framework we show that optimal health care policies will generally differ across financing regimes when the health authority has redistributive concerns. We show that health care financing also has a bearing on the political implementability of optimal health care policies. Our results demonstrate that an isolated analysis of (optimal) provider payment rests on very strong assumptions regarding both the financing of health care and the redistributive preferences of the health authority. Copyright © 2015 Elsevier B.V. All rights reserved.

The complete proof on the optimal ordering policy under cash discount and trade credit

NASA Astrophysics Data System (ADS)

Chung, Kun-Jen

2010-04-01

Huang ((2005), 'Buyer's Optimal Ordering Policy and Payment Policy under Supplier Credit', International Journal of Systems Science, 36, 801-807) investigates the buyer's optimal ordering policy and payment policy under supplier credit. His inventory model is correct and interesting. Basically, he uses an algebraic method to locate the optimal solution of the annual total relevant cost TRC(T) and ignores the role of the functional behaviour of TRC(T) in locating the optimal solution of it. However, as argued in this article, Huang needs to explore the functional behaviour of TRC(T) to justify his solution. So, from the viewpoint of logic, the proof about Theorem 1 in Huang has some shortcomings such that the validity of Theorem 1 in Huang is questionable. The main purpose of this article is to remove and correct those shortcomings in Huang and present the complete proofs for Huang.

Extreme Trust Region Policy Optimization for Active Object Recognition.

PubMed

Liu, Huaping; Wu, Yupei; Sun, Fuchun; Huaping Liu; Yupei Wu; Fuchun Sun; Sun, Fuchun; Liu, Huaping; Wu, Yupei

2018-06-01

In this brief, we develop a deep reinforcement learning method to actively recognize objects by choosing a sequence of actions for an active camera that helps to discriminate between the objects. The method is realized using trust region policy optimization, in which the policy is realized by an extreme learning machine and, therefore, leads to efficient optimization algorithm. The experimental results on the publicly available data set show the advantages of the developed extreme trust region optimization method.

Optimizing model: insemination, replacement, seasonal production, and cash flow.

PubMed

DeLorenzo, M A; Spreen, T H; Bryan, G R; Beede, D K; Van Arendonk, J A

1992-03-01

Dynamic programming to solve the Markov decision process problem of optimal insemination and replacement decisions was adapted to address large dairy herd management decision problems in the US. Expected net present values of cow states (151,200) were used to determine the optimal policy. States were specified by class of parity (n = 12), production level (n = 15), month of calving (n = 12), month of lactation (n = 16), and days open (n = 7). Methodology optimized decisions based on net present value of an individual cow and all replacements over a 20-yr decision horizon. Length of decision horizon was chosen to ensure that optimal policies were determined for an infinite planning horizon. Optimization took 286 s of central processing unit time. The final probability transition matrix was determined, in part, by the optimal policy. It was estimated iteratively to determine post-optimization steady state herd structure, milk production, replacement, feed inputs and costs, and resulting cash flow on a calendar month and annual basis if optimal policies were implemented. Implementation of the model included seasonal effects on lactation curve shapes, estrus detection rates, pregnancy rates, milk prices, replacement costs, cull prices, and genetic progress. Other inputs included calf values, values of dietary TDN and CP per kilogram, and discount rate. Stochastic elements included conception (and, thus, subsequent freshening), cow milk production level within herd, and survival. Validation of optimized solutions was by separate simulation model, which implemented policies on a simulated herd and also described herd dynamics during transition to optimized structure.

Electricity market design for generator revenue sufficiency with increased variable generation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Levin, Todd; Botterud, Audun

Here, we present a computationally efficient mixed-integer program (MIP) that determines optimal generator expansion decisions, and hourly unit commitment and dispatch in a power system. The impact of increasing wind power capacity on the optimal generation mix and generator profitability is analyzed for a test case that approximates the electricity market in Texas (ERCOT). We analyze three market policies that may support resource adequacy: Operating Reserve Demand Curves (ORDC), Fixed Reserve Scarcity Prices (FRSP) and fixed capacity payments (CP). Optimal expansion plans are comparable between the ORDC and FRSP implementations, while capacity payments may result in additional new capacity. Themore » FRSP policy leads to frequent reserves scarcity events and corresponding price spikes, while the ORDC implementation results in more continuous energy prices. Average energy prices decrease with increasing wind penetration under all policies, as do revenues for baseload and wind generators. Intermediate and peak load plants benefit from higher reserve prices and are less exposed to reduced energy prices. All else equal, an ORDC approach may be preferred to FRSP as it results in similar expansion and revenues with less extreme energy prices. A fixed CP leads to additional new flexible NGCT units, but lower profits for other technologies.« less

Electricity market design for generator revenue sufficiency with increased variable generation

DOE PAGES

Levin, Todd; Botterud, Audun

2015-10-01

Here, we present a computationally efficient mixed-integer program (MIP) that determines optimal generator expansion decisions, and hourly unit commitment and dispatch in a power system. The impact of increasing wind power capacity on the optimal generation mix and generator profitability is analyzed for a test case that approximates the electricity market in Texas (ERCOT). We analyze three market policies that may support resource adequacy: Operating Reserve Demand Curves (ORDC), Fixed Reserve Scarcity Prices (FRSP) and fixed capacity payments (CP). Optimal expansion plans are comparable between the ORDC and FRSP implementations, while capacity payments may result in additional new capacity. Themore » FRSP policy leads to frequent reserves scarcity events and corresponding price spikes, while the ORDC implementation results in more continuous energy prices. Average energy prices decrease with increasing wind penetration under all policies, as do revenues for baseload and wind generators. Intermediate and peak load plants benefit from higher reserve prices and are less exposed to reduced energy prices. All else equal, an ORDC approach may be preferred to FRSP as it results in similar expansion and revenues with less extreme energy prices. A fixed CP leads to additional new flexible NGCT units, but lower profits for other technologies.« less

Optimal harvesting policy of predator-prey model with free fishing and reserve zones

NASA Astrophysics Data System (ADS)

Toaha, Syamsuddin; Rustam

2017-03-01

The present paper deals with an optimal harvesting of predator-prey model in an ecosystem that consists of two zones, namely the free fishing and prohibited zones. The dynamics of prey population in the ecosystem can migrate from the free fishing to the prohibited zone and vice versa. The predator and prey populations in the free fishing zone are then harvested with constant efforts. The existence of the interior equilibrium point is analyzed and its stability is determined using Routh-Hurwitz stability test. The stable interior equilibrium point is then related to the problem of maximum profit and the problem of present value of net revenue. We follow the Pontryagin's maximal principle to get the optimal harvesting policy of the present value of the net revenue. From the analysis, we found a critical point of the efforts that makes maximum profit. There also exists certain conditions of the efforts that makes the present value of net revenue becomes maximal. In addition, the interior equilibrium point is locally asymptotically stable which means that the optimal harvesting is reached and the unharvested prey, harvested prey, and harvested predator populations remain sustainable. Numerical examples are given to verify the analytical results.

Quantum-enhanced reinforcement learning for finite-episode games with discrete state spaces

NASA Astrophysics Data System (ADS)

Neukart, Florian; Von Dollen, David; Seidel, Christian; Compostella, Gabriele

2017-12-01

Quantum annealing algorithms belong to the class of metaheuristic tools, applicable for solving binary optimization problems. Hardware implementations of quantum annealing, such as the quantum annealing machines produced by D-Wave Systems, have been subject to multiple analyses in research, with the aim of characterizing the technology's usefulness for optimization and sampling tasks. Here, we present a way to partially embed both Monte Carlo policy iteration for finding an optimal policy on random observations, as well as how to embed n sub-optimal state-value functions for approximating an improved state-value function given a policy for finite horizon games with discrete state spaces on a D-Wave 2000Q quantum processing unit (QPU). We explain how both problems can be expressed as a quadratic unconstrained binary optimization (QUBO) problem, and show that quantum-enhanced Monte Carlo policy evaluation allows for finding equivalent or better state-value functions for a given policy with the same number episodes compared to a purely classical Monte Carlo algorithm. Additionally, we describe a quantum-classical policy learning algorithm. Our first and foremost aim is to explain how to represent and solve parts of these problems with the help of the QPU, and not to prove supremacy over every existing classical policy evaluation algorithm.

Long-term soil nutrient dynamics comparison under smallholding land and farmland policy in northeast of China.

PubMed

Ouyang, Wei; Wei, Xinfeng; Hao, Fanghua

2013-04-15

There are two kinds of land policies, the smallholding land policy (SLP) and the farmland policy (FLP) in China. The farmland nutrient dynamics under the two land policies were analysed with the soil system budget method. The averaged nitrogen (N) input of the SLP and the FLP over sixteen years increased about 23.9% and 33.3%, respectively and the phosphorus (P) input climbed about 39.1% and 42.3%, respectively. The statistical analysis showed that the land policies had significant impacts on N and P input from fertilizer and manure, but did not obviously affect the N input from seeds and biological N fixation. The efficiency percentage of N of the SLP and the FLP climbed about 54.5% and 59.4%, respectively, and the P efficiency improved by 52.7% and 82.6%, respectively. About the nutrient output, the F-test analysis indicated that the land polices had remarkable impacts on N output by crop uptake, ammonia volatilisation, denitrification, leaching and runoff, and P output by uptake, runoff, and leach. The balance showed that the absolute loss of N from land deceased about 43.6% and 46.0%, respectively, in the SLP and the FLP, and P discharge reduced about 34.2% and 75.2%, respectively. The F-test analysis of N and P efficiency and balance of between two polices both indicated that the FLP had significant impact on nutrient dynamic. With the Mitscherlich model, the correlations between nutrient input and crop uptake, usage efficiency and loss were analysed and showed that was a threshold value for the optimal nutrient input with the highest efficiency rate. For the optimal nutrient efficiency, the space for extra P addition was bigger than the N input. The FLP have more advantage than the SLP on the crop yield, nutrient efficiency and environmental discharge. Copyright © 2013 Elsevier B.V. All rights reserved.

Policy tree optimization for adaptive management of water resources systems

NASA Astrophysics Data System (ADS)

Herman, Jonathan; Giuliani, Matteo

2017-04-01

Water resources systems must cope with irreducible uncertainty in supply and demand, requiring policy alternatives capable of adapting to a range of possible future scenarios. Recent studies have developed adaptive policies based on "signposts" or "tipping points" that suggest the need of updating the policy. However, there remains a need for a general method to optimize the choice of the signposts to be used and their threshold values. This work contributes a general framework and computational algorithm to design adaptive policies as a tree structure (i.e., a hierarchical set of logical rules) using a simulation-optimization approach based on genetic programming. Given a set of feature variables (e.g., reservoir level, inflow observations, inflow forecasts), the resulting policy defines both the optimal reservoir operations and the conditions under which such operations should be triggered. We demonstrate the approach using Folsom Reservoir (California) as a case study, in which operating policies must balance the risk of both floods and droughts. Numerical results show that the tree-based policies outperform the ones designed via Dynamic Programming. In addition, they display good adaptive capacity to the changing climate, successfully adapting the reservoir operations across a large set of uncertain climate scenarios.

Assessing healthcare quality using routine data: evaluating the performance of the national tuberculosis programme in South Africa.

PubMed

McLaren, Zoë M; Sharp, Alana R; Zhou, Jifang; Wasserman, Sean; Nanoo, Ananta

2017-02-01

To assess the performance of healthcare facilities by means of indicators based on guidelines for clinical care of TB, which is likely a good measure of overall facility quality. We assessed quality of care in all public health facilities in South Africa using graphical, correlation and locally weighted kernel regression analysis of routine TB test data. Facility performance falls short of national standards of care. Only 74% of patients with TB provided a second specimen for testing, 18% received follow-up testing and 14% received drug resistance testing. Only resistance testing rates improved over time, tripling between 2004 and 2011. National awareness campaigns and changes in clinical guidelines had only a transient impact on testing rates. The poorest performing facilities remained at the bottom of the rankings over the period of study. The optimal policy strategy requires both broad-based policies and targeted resources to poor performers. This approach to assessing facility quality of care can be adapted to other contexts and also provides a low-cost method for evaluating the effectiveness of proposed interventions. Devising targeted policies based on routine data is a cost-effective way to improve the quality of public health care provided. © 2016 John Wiley & Sons Ltd.

Optimal pricing policies for services with consideration of facility maintenance costs

NASA Astrophysics Data System (ADS)

Yeh, Ruey Huei; Lin, Yi-Fang

2012-06-01

For survival and success, pricing is an essential issue for service firms. This article deals with the pricing strategies for services with substantial facility maintenance costs. For this purpose, a mathematical framework that incorporates service demand and facility deterioration is proposed to address the problem. The facility and customers constitute a service system driven by Poisson arrivals and exponential service times. A service demand with increasing price elasticity and a facility lifetime with strictly increasing failure rate are also adopted in modelling. By examining the bidirectional relationship between customer demand and facility deterioration in the profit model, the pricing policies of the service are investigated. Then analytical conditions of customer demand and facility lifetime are derived to achieve a unique optimal pricing policy. The comparative statics properties of the optimal policy are also explored. Finally, numerical examples are presented to illustrate the effects of parameter variations on the optimal pricing policy.

An optimal repartitioning decision policy

NASA Technical Reports Server (NTRS)

Nicol, D. M.; Reynolds, P. F., Jr.

1986-01-01

A central problem to parallel processing is the determination of an effective partitioning of workload to processors. The effectiveness of any given partition is dependent on the stochastic nature of the workload. The problem of determining when and if the stochastic behavior of the workload has changed enough to warrant the calculation of a new partition is treated. The problem is modeled as a Markov decision process, and an optimal decision policy is derived. Quantification of this policy is usually intractable. A heuristic policy which performs nearly optimally is investigated empirically. The results suggest that the detection of change is the predominant issue in this problem.

Optimal harvesting policy of a stochastic two-species competitive model with Lévy noise in a polluted environment

NASA Astrophysics Data System (ADS)

Zhao, Yu; Yuan, Sanling

2017-07-01

As well known that the sudden environmental shocks and toxicant can affect the population dynamics of fish species, a mechanistic understanding of how sudden environmental change and toxicant influence the optimal harvesting policy requires development. This paper presents the optimal harvesting of a stochastic two-species competitive model with Lévy noise in a polluted environment, where the Lévy noise is used to describe the sudden climate change. Due to the discontinuity of the Lévy noise, the classical optimal harvesting methods based on the explicit solution of the corresponding Fokker-Planck equation are invalid. The object of this paper is to fill up this gap and establish the optimal harvesting policy. By using of aggregation and ergodic methods, the approximation of the optimal harvesting effort and maximum expectation of sustainable yields are obtained. Numerical simulations are carried out to support these theoretical results. Our analysis shows that the Lévy noise and the mean stress measure of toxicant in organism may affect the optimal harvesting policy significantly.

Reinforcement learning solution for HJB equation arising in constrained optimal control problem.

PubMed

Luo, Biao; Wu, Huai-Ning; Huang, Tingwen; Liu, Derong

2015-11-01

The constrained optimal control problem depends on the solution of the complicated Hamilton-Jacobi-Bellman equation (HJBE). In this paper, a data-based off-policy reinforcement learning (RL) method is proposed, which learns the solution of the HJBE and the optimal control policy from real system data. One important feature of the off-policy RL is that its policy evaluation can be realized with data generated by other behavior policies, not necessarily the target policy, which solves the insufficient exploration problem. The convergence of the off-policy RL is proved by demonstrating its equivalence to the successive approximation approach. Its implementation procedure is based on the actor-critic neural networks structure, where the function approximation is conducted with linearly independent basis functions. Subsequently, the convergence of the implementation procedure with function approximation is also proved. Finally, its effectiveness is verified through computer simulations. Copyright © 2015 Elsevier Ltd. All rights reserved.

Methods, systems, and computer program products for network firewall policy optimization

DOEpatents

Fulp, Errin W [Winston-Salem, NC; Tarsa, Stephen J [Duxbury, MA

2011-10-18

Methods, systems, and computer program products for firewall policy optimization are disclosed. According to one method, a firewall policy including an ordered list of firewall rules is defined. For each rule, a probability indicating a likelihood of receiving a packet matching the rule is determined. The rules are sorted in order of non-increasing probability in a manner that preserves the firewall policy.

Optimal management of a stochastically varying population when policy adjustment is costly.

PubMed

Boettiger, Carl; Bode, Michael; Sanchirico, James N; Lariviere, Jacob; Hastings, Alan; Armsworth, Paul R

2016-04-01

Ecological systems are dynamic and policies to manage them need to respond to that variation. However, policy adjustments will sometimes be costly, which means that fine-tuning a policy to track variability in the environment very tightly will only sometimes be worthwhile. We use a classic fisheries management problem, how to manage a stochastically varying population using annually varying quotas in order to maximize profit, to examine how costs of policy adjustment change optimal management recommendations. Costs of policy adjustment (changes in fishing quotas through time) could take different forms. For example, these costs may respond to the size of the change being implemented, or there could be a fixed cost any time a quota change is made. We show how different forms of policy costs have contrasting implications for optimal policies. Though it is frequently assumed that costs to adjusting policies will dampen variation in the policy, we show that certain cost structures can actually increase variation through time. We further show that failing to account for adjustment costs has a consistently worse economic impact than would assuming these costs are present when they are not.

Radon, smoking, and lung cancer: the need to refocus radon control policy.

PubMed

Lantz, Paula M; Mendez, David; Philbert, Martin A

2013-03-01

Exposure to radon is the second leading cause of lung cancer, and the risk is significantly higher for smokers than for nonsmokers. More than 85% of radon-induced lung cancer deaths are among smokers. The most powerful approach for reducing the public health burden of radon is shaped by 2 overarching principles: public communication efforts that promote residential radon testing and remediation will be the most cost effective if they are primarily directed at current and former smokers; and focusing on smoking prevention and cessation is the optimal strategy for reducing radon-induced lung cancer in terms of both public health gains and economic efficiency. Tobacco control policy is the most promising route to the public health goals of radon control policy.

Development and Implementation of an Optimization Model for Hydropower and Total Dissolved Gas in the Mid-Columbia River System

DOE PAGES

Witt, Adam; Magee, Timothy; Stewart, Kevin; ...

2017-08-10

Managing energy, water, and environmental priorities and constraints within a cascade hydropower system is a challenging multiobjective optimization effort that requires advanced modeling and forecasting tools. Within the mid-Columbia River system, there is currently a lack of specific solutions for predicting how coordinated operational decisions can mitigate the impacts of total dissolved gas (TDG) supersaturation while satisfying multiple additional policy and hydropower generation objectives. In this study, a reduced-order TDG uptake equation is developed that predicts tailrace TDG at seven hydropower facilities on the mid-Columbia River. The equation is incorporated into a general multiobjective river, reservoir, and hydropower optimization toolmore » as a prioritized operating goal within a broader set of system-level objectives and constraints. A test case is presented to assess the response of TDG and hydropower generation when TDG supersaturation is optimized to remain under state water-quality standards. Satisfaction of TDG as an operating goal is highly dependent on whether constraints that limit TDG uptake are implemented at a higher priority than generation requests. According to the model, an opportunity exists to reduce TDG supersaturation and meet hydropower generation requirements by shifting spillway flows to different time periods. In conclusion, a coordinated effort between all project owners is required to implement systemwide optimized solutions that satisfy the operating policies of all stakeholders.« less

Development and Implementation of an Optimization Model for Hydropower and Total Dissolved Gas in the Mid-Columbia River System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Witt, Adam; Magee, Timothy; Stewart, Kevin

Managing energy, water, and environmental priorities and constraints within a cascade hydropower system is a challenging multiobjective optimization effort that requires advanced modeling and forecasting tools. Within the mid-Columbia River system, there is currently a lack of specific solutions for predicting how coordinated operational decisions can mitigate the impacts of total dissolved gas (TDG) supersaturation while satisfying multiple additional policy and hydropower generation objectives. In this study, a reduced-order TDG uptake equation is developed that predicts tailrace TDG at seven hydropower facilities on the mid-Columbia River. The equation is incorporated into a general multiobjective river, reservoir, and hydropower optimization toolmore » as a prioritized operating goal within a broader set of system-level objectives and constraints. A test case is presented to assess the response of TDG and hydropower generation when TDG supersaturation is optimized to remain under state water-quality standards. Satisfaction of TDG as an operating goal is highly dependent on whether constraints that limit TDG uptake are implemented at a higher priority than generation requests. According to the model, an opportunity exists to reduce TDG supersaturation and meet hydropower generation requirements by shifting spillway flows to different time periods. In conclusion, a coordinated effort between all project owners is required to implement systemwide optimized solutions that satisfy the operating policies of all stakeholders.« less

Policy-Relevant Nonconvexities in the Production of Multiple Forest Benefits?

Treesearch

Stephen K. Swallow; Peter J. Parks; David N. Wear

1990-01-01

This paper challenges common assumptions about convexity in forest rotation models which optimize timber plus nontimber benefits. If a local optimum occurs earlier than the globally optimal age, policy based on marginal incentives may achieve suboptimal results. Policy-relevant nonconvexities are more likely if (i) nontimber benefits dominate for young stands while...

Can market-based policies accomplish the optimal floodplain management? A gap between static and dynamic models.

PubMed

Mori, Koichiro

2009-02-01

The purpose of this short article is to set static and dynamic models for optimal floodplain management and to compare policy implications from the models. River floodplains are important multiple resources in that they provide various ecosystem services. It is fundamentally significant to consider environmental externalities that accrue from ecosystem services of natural floodplains. There is an interesting gap between static and dynamic models about policy implications for floodplain management, although they are based on the same assumptions. Essentially, we can derive the same optimal conditions, which imply that the marginal benefits must equal the sum of the marginal costs and the social external costs related to ecosystem services. Thus, we have to internalise the external costs by market-based policies. In this respect, market-based policies seem to be effective in a static model. However, they are not sufficient in the context of a dynamic model because the optimal steady state turns out to be unstable. Based on a dynamic model, we need more coercive regulation policies.

Optimal reservoir operation policies using novel nested algorithms

NASA Astrophysics Data System (ADS)

Delipetrev, Blagoj; Jonoski, Andreja; Solomatine, Dimitri

2015-04-01

Historically, the two most widely practiced methods for optimal reservoir operation have been dynamic programming (DP) and stochastic dynamic programming (SDP). These two methods suffer from the so called "dual curse" which prevents them to be used in reasonably complex water systems. The first one is the "curse of dimensionality" that denotes an exponential growth of the computational complexity with the state - decision space dimension. The second one is the "curse of modelling" that requires an explicit model of each component of the water system to anticipate the effect of each system's transition. We address the problem of optimal reservoir operation concerning multiple objectives that are related to 1) reservoir releases to satisfy several downstream users competing for water with dynamically varying demands, 2) deviations from the target minimum and maximum reservoir water levels and 3) hydropower production that is a combination of the reservoir water level and the reservoir releases. Addressing such a problem with classical methods (DP and SDP) requires a reasonably high level of discretization of the reservoir storage volume, which in combination with the required releases discretization for meeting the demands of downstream users leads to computationally expensive formulations and causes the curse of dimensionality. We present a novel approach, named "nested" that is implemented in DP, SDP and reinforcement learning (RL) and correspondingly three new algorithms are developed named nested DP (nDP), nested SDP (nSDP) and nested RL (nRL). The nested algorithms are composed from two algorithms: 1) DP, SDP or RL and 2) nested optimization algorithm. Depending on the way we formulate the objective function related to deficits in the allocation problem in the nested optimization, two methods are implemented: 1) Simplex for linear allocation problems, and 2) quadratic Knapsack method in the case of nonlinear problems. The novel idea is to include the nested optimization algorithm into the state transition that lowers the starting problem dimension and alleviates the curse of dimensionality. The algorithms can solve multi-objective optimization problems, without significantly increasing the complexity and the computational expenses. The algorithms can handle dense and irregular variable discretization, and are coded in Java as prototype applications. The three algorithms were tested at the multipurpose reservoir Knezevo of the Zletovica hydro-system located in the Republic of Macedonia, with eight objectives, including urban water supply, agriculture, ensuring ecological flow, and generation of hydropower. Because the Zletovica hydro-system is relatively complex, the novel algorithms were pushed to their limits, demonstrating their capabilities and limitations. The nSDP and nRL derived/learned the optimal reservoir policy using 45 (1951-1995) years historical data. The nSDP and nRL optimal reservoir policy was tested on 10 (1995-2005) years historical data, and compared with nDP optimal reservoir operation in the same period. The nested algorithms and optimal reservoir operation results are analysed and explained.

Policy Tree Optimization for Adaptive Management of Water Resources Systems

NASA Astrophysics Data System (ADS)

Herman, J. D.; Giuliani, M.

2016-12-01

Water resources systems must cope with irreducible uncertainty in supply and demand, requiring policy alternatives capable of adapting to a range of possible future scenarios. Recent studies have developed adaptive policies based on "signposts" or "tipping points", which are threshold values of indicator variables that signal a change in policy. However, there remains a need for a general method to optimize the choice of indicators and their threshold values in a way that is easily interpretable for decision makers. Here we propose a conceptual framework and computational algorithm to design adaptive policies as a tree structure (i.e., a hierarchical set of logical rules) using a simulation-optimization approach based on genetic programming. We demonstrate the approach using Folsom Reservoir, California as a case study, in which operating policies must balance the risk of both floods and droughts. Given a set of feature variables, such as reservoir level, inflow observations and forecasts, and time of year, the resulting policy defines the conditions under which flood control and water supply hedging operations should be triggered. Importantly, the tree-based rule sets are easy to interpret for decision making, and can be compared to historical operating policies to understand the adaptations needed under possible climate change scenarios. Several remaining challenges are discussed, including the empirical convergence properties of the method, and extensions to irreversible decisions such as infrastructure. Policy tree optimization, and corresponding open-source software, provide a generalizable, interpretable approach to designing adaptive policies under uncertainty for water resources systems.

Optimizing preventive maintenance policy: A data-driven application for a light rail braking system.

PubMed

Corman, Francesco; Kraijema, Sander; Godjevac, Milinko; Lodewijks, Gabriel

2017-10-01

This article presents a case study determining the optimal preventive maintenance policy for a light rail rolling stock system in terms of reliability, availability, and maintenance costs. The maintenance policy defines one of the three predefined preventive maintenance actions at fixed time-based intervals for each of the subsystems of the braking system. Based on work, maintenance, and failure data, we model the reliability degradation of the system and its subsystems under the current maintenance policy by a Weibull distribution. We then analytically determine the relation between reliability, availability, and maintenance costs. We validate the model against recorded reliability and availability and get further insights by a dedicated sensitivity analysis. The model is then used in a sequential optimization framework determining preventive maintenance intervals to improve on the key performance indicators. We show the potential of data-driven modelling to determine optimal maintenance policy: same system availability and reliability can be achieved with 30% maintenance cost reduction, by prolonging the intervals and re-grouping maintenance actions.

Optimizing preventive maintenance policy: A data-driven application for a light rail braking system

PubMed Central

Corman, Francesco; Kraijema, Sander; Godjevac, Milinko; Lodewijks, Gabriel

2017-01-01

This article presents a case study determining the optimal preventive maintenance policy for a light rail rolling stock system in terms of reliability, availability, and maintenance costs. The maintenance policy defines one of the three predefined preventive maintenance actions at fixed time-based intervals for each of the subsystems of the braking system. Based on work, maintenance, and failure data, we model the reliability degradation of the system and its subsystems under the current maintenance policy by a Weibull distribution. We then analytically determine the relation between reliability, availability, and maintenance costs. We validate the model against recorded reliability and availability and get further insights by a dedicated sensitivity analysis. The model is then used in a sequential optimization framework determining preventive maintenance intervals to improve on the key performance indicators. We show the potential of data-driven modelling to determine optimal maintenance policy: same system availability and reliability can be achieved with 30% maintenance cost reduction, by prolonging the intervals and re-grouping maintenance actions. PMID:29278245

Optimal pricing and replenishment policies for instantaneous deteriorating items with backlogging and trade credit under inflation

NASA Astrophysics Data System (ADS)

Sundara Rajan, R.; Uthayakumar, R.

2017-12-01

In this paper we develop an economic order quantity model to investigate the optimal replenishment policies for instantaneous deteriorating items under inflation and trade credit. Demand rate is a linear function of selling price and decreases negative exponentially with time over a finite planning horizon. Shortages are allowed and partially backlogged. Under these conditions, we model the retailer's inventory system as a profit maximization problem to determine the optimal selling price, optimal order quantity and optimal replenishment time. An easy-to-use algorithm is developed to determine the optimal replenishment policies for the retailer. We also provide optimal present value of profit when shortages are completely backlogged as a special case. Numerical examples are presented to illustrate the algorithm provided to obtain optimal profit. And we also obtain managerial implications from numerical examples to substantiate our model. The results show that there is an improvement in total profit from complete backlogging rather than the items being partially backlogged.

Simulation optimization of PSA-threshold based prostate cancer screening policies

PubMed Central

Zhang, Jingyu; Denton, Brian T.; Shah, Nilay D.; Inman, Brant A.

2013-01-01

We describe a simulation optimization method to design PSA screening policies based on expected quality adjusted life years (QALYs). Our method integrates a simulation model in a genetic algorithm which uses a probabilistic method for selection of the best policy. We present computational results about the efficiency of our algorithm. The best policy generated by our algorithm is compared to previously recommended screening policies. Using the policies determined by our model, we present evidence that patients should be screened more aggressively but for a shorter length of time than previously published guidelines recommend. PMID:22302420

Selected Topics on Decision Making for Electric Vehicles

NASA Astrophysics Data System (ADS)

Sweda, Timothy Matthew

Electric vehicles (EVs) are an attractive alternative to conventional gasoline-powered vehicles due to their lower emissions, fuel costs, and maintenance costs. Range anxiety, or the fear of running out of charge prior to reaching one's destination, remains a significant concern, however. In this dissertation, we address the issue of range anxiety by developing a set of decision support tools for both charging infrastructure providers and EV drivers. In Chapter 1, we present an agent-based information system for identifying patterns in residential EV ownership and driving activities to enable strategic deployment of new charging infrastructure. Driver agents consider their own driving activities within the simulated environment, in addition to the presence of charging stations and the vehicle ownership of others in their social networks, when purchasing a new vehicle. The Chicagoland area is used as a case study to demonstrate the model, and several deployment scenarios are analyzed. In Chapter 2, we address the problem of finding an optimal recharging policy for an EV along a given path. The path consists of a sequence of nodes, each representing a charging station, and the driver must decide where to stop and how much to recharge at each stop. We present efficient algorithms for finding an optimal policy in general instances with deterministic travel costs and homogeneous charging stations, and also for two specialized cases. In addition, we develop two heuristic procedures that we characterize analytically and explore empirically. We further analyze and test our solution methods on model variations that include stochastic travel costs and nonhomogeneous charging stations. In Chapter 3, we study the problem of finding an optimal routing and recharging policy for an electric vehicle in a grid network. Each node in the network represents a charging station and has an associated probability of being available at any point in time or occupied by another vehicle. We present an efficient algorithm for finding an optimal a priori route and recharging policy as well as heuristic methods for finding adaptive policies. We conduct numerical experiments to demonstrate the empirical performance of our solutions.

A Multiobjective Optimization Framework for Online Stochastic Optimal Control in Hybrid Electric Vehicles

DOE PAGES

Malikopoulos, Andreas

2015-01-01

The increasing urgency to extract additional efficiency from hybrid propulsion systems has led to the development of advanced power management control algorithms. In this paper we address the problem of online optimization of the supervisory power management control in parallel hybrid electric vehicles (HEVs). We model HEV operation as a controlled Markov chain and we show that the control policy yielding the Pareto optimal solution minimizes online the long-run expected average cost per unit time criterion. The effectiveness of the proposed solution is validated through simulation and compared to the solution derived with dynamic programming using the average cost criterion.more » Both solutions achieved the same cumulative fuel consumption demonstrating that the online Pareto control policy is an optimal control policy.« less

Radon, Smoking, and Lung Cancer: The Need to Refocus Radon Control Policy

PubMed Central

Mendez, David; Philbert, Martin A.

2013-01-01

Exposure to radon is the second leading cause of lung cancer, and the risk is significantly higher for smokers than for nonsmokers. More than 85% of radon-induced lung cancer deaths are among smokers. The most powerful approach for reducing the public health burden of radon is shaped by 2 overarching principles: public communication efforts that promote residential radon testing and remediation will be the most cost effective if they are primarily directed at current and former smokers; and focusing on smoking prevention and cessation is the optimal strategy for reducing radon-induced lung cancer in terms of both public health gains and economic efficiency. Tobacco control policy is the most promising route to the public health goals of radon control policy. PMID:23327258

A duality framework for stochastic optimal control of complex systems

DOE PAGES

Malikopoulos, Andreas A.

2016-01-01

In this study, we address the problem of minimizing the long-run expected average cost of a complex system consisting of interactive subsystems. We formulate a multiobjective optimization problem of the one-stage expected costs of the subsystems and provide a duality framework to prove that the control policy yielding the Pareto optimal solution minimizes the average cost criterion of the system. We provide the conditions of existence and a geometric interpretation of the solution. For practical situations having constraints consistent with those studied here, our results imply that the Pareto control policy may be of value when we seek to derivemore » online the optimal control policy in complex systems.« less

Uncertainty quantification and optimal decisions

PubMed Central

2017-01-01

A mathematical model can be analysed to construct policies for action that are close to optimal for the model. If the model is accurate, such policies will be close to optimal when implemented in the real world. In this paper, the different aspects of an ideal workflow are reviewed: modelling, forecasting, evaluating forecasts, data assimilation and constructing control policies for decision-making. The example of the oil industry is used to motivate the discussion, and other examples, such as weather forecasting and precision agriculture, are used to argue that the same mathematical ideas apply in different contexts. Particular emphasis is placed on (i) uncertainty quantification in forecasting and (ii) how decisions are optimized and made robust to uncertainty in models and judgements. This necessitates full use of the relevant data and by balancing costs and benefits into the long term may suggest policies quite different from those relevant to the short term. PMID:28484343

Error bounds of adaptive dynamic programming algorithms for solving undiscounted optimal control problems.

PubMed

Liu, Derong; Li, Hongliang; Wang, Ding

2015-06-01

In this paper, we establish error bounds of adaptive dynamic programming algorithms for solving undiscounted infinite-horizon optimal control problems of discrete-time deterministic nonlinear systems. We consider approximation errors in the update equations of both value function and control policy. We utilize a new assumption instead of the contraction assumption in discounted optimal control problems. We establish the error bounds for approximate value iteration based on a new error condition. Furthermore, we also establish the error bounds for approximate policy iteration and approximate optimistic policy iteration algorithms. It is shown that the iterative approximate value function can converge to a finite neighborhood of the optimal value function under some conditions. To implement the developed algorithms, critic and action neural networks are used to approximate the value function and control policy, respectively. Finally, a simulation example is given to demonstrate the effectiveness of the developed algorithms.

Optimal dynamic pricing and replenishment policy for perishable items with inventory-level-dependent demand

NASA Astrophysics Data System (ADS)

Lu, Lihao; Zhang, Jianxiong; Tang, Wansheng

2016-04-01

An inventory system for perishable items with limited replenishment capacity is introduced in this paper. The demand rate depends on the stock quantity displayed in the store as well as the sales price. With the goal to realise profit maximisation, an optimisation problem is addressed to seek for the optimal joint dynamic pricing and replenishment policy which is obtained by solving the optimisation problem with Pontryagin's maximum principle. A joint mixed policy, in which the sales price is a static decision variable and the replenishment rate remains to be a dynamic decision variable, is presented to compare with the joint dynamic policy. Numerical results demonstrate the advantages of the joint dynamic one, and further show the effects of different system parameters on the optimal joint dynamic policy and the maximal total profit.

Event-Triggered Distributed Approximate Optimal State and Output Control of Affine Nonlinear Interconnected Systems.

PubMed

Narayanan, Vignesh; Jagannathan, Sarangapani

2017-06-08

This paper presents an approximate optimal distributed control scheme for a known interconnected system composed of input affine nonlinear subsystems using event-triggered state and output feedback via a novel hybrid learning scheme. First, the cost function for the overall system is redefined as the sum of cost functions of individual subsystems. A distributed optimal control policy for the interconnected system is developed using the optimal value function of each subsystem. To generate the optimal control policy, forward-in-time, neural networks are employed to reconstruct the unknown optimal value function at each subsystem online. In order to retain the advantages of event-triggered feedback for an adaptive optimal controller, a novel hybrid learning scheme is proposed to reduce the convergence time for the learning algorithm. The development is based on the observation that, in the event-triggered feedback, the sampling instants are dynamic and results in variable interevent time. To relax the requirement of entire state measurements, an extended nonlinear observer is designed at each subsystem to recover the system internal states from the measurable feedback. Using a Lyapunov-based analysis, it is demonstrated that the system states and the observer errors remain locally uniformly ultimately bounded and the control policy converges to a neighborhood of the optimal policy. Simulation results are presented to demonstrate the performance of the developed controller.

Benchmarks for Enhanced Network Performance: Hands-On Testing of Operating System Solutions to Identify the Optimal Application Server Platform for the Graduate School of Business and Public Policy

DTIC Science & Technology

2010-09-01

for Applied Mathematics. Kennedy, R. C. (2009a). Clocking Windows netbook performance. Retrieved on 08/14/2010, from http...podcasts.infoworld.com/d/hardware/clocking-windows- netbook -performance-883?_kip_ipx=1177119066-1281460794 Kennedy, R. C. (2009b). OfficeBench 7: A cool new way to

A proactive transfer policy for critical patient flow management.

PubMed

González, Jaime; Ferrer, Juan-Carlos; Cataldo, Alejandro; Rojas, Luis

2018-02-17

Hospital emergency departments are often overcrowded, resulting in long wait times and a public perception of poor attention. Delays in transferring patients needing further treatment increases emergency department congestion, has negative impacts on their health and may increase their mortality rates. A model built around a Markov decision process is proposed to improve the efficiency of patient flows between the emergency department and other hospital units. With each day divided into time periods, the formulation estimates bed demand for the next period as the basis for determining a proactive rather than reactive transfer decision policy. Due to the high dimensionality of the optimization problem involved, an approximate dynamic programming approach is used to derive an approximation of the optimal decision policy, which indicates that a certain number of beds should be kept free in the different units as a function of the next period demand estimate. Testing the model on two instances of different sizes demonstrates that the optimal number of patient transfers between units changes when the emergency patient arrival rate for transfer to other units changes at a single unit, but remains stable if the change is proportionally the same for all units. In a simulation using real data for a hospital in Chile, significant improvements are achieved by the model in key emergency department performance indicators such as patient wait times (reduction higher than 50%), patient capacity (21% increase) and queue abandonment (from 7% down to less than 1%).

Optimizing desalinated sea water blending with other sources to meet magnesium requirements for potable and irrigation waters.

PubMed

Avni, Noa; Eben-Chaime, Moshe; Oron, Gideon

2013-05-01

Sea water desalination provides fresh water that typically lacks minerals essential to human health and to agricultural productivity. Thus the rising proportion of desalinated sea water consumed by both the domestic and agricultural sectors constitutes a public health risk. Research on low-magnesium water irrigation showed that crops developed magnesium deficiency symptoms that could lead to plant death, and tomato yields were reduced by 10-15%. The World Health Organization (WHO) reported on a relationship between sudden cardiac death rates and magnesium intake deficits. An optimization model, developed and tested to provide recommendations for Water Distribution System (WDS) quality control in terms of meeting optimal water quality requirements, was run in computational experiments based on an actual regional WDS. The expected magnesium deficit due to the operation of a large Sea Water Desalination Plant (SWDP) was simulated, and an optimal operation policy, in which remineralization at the SWDP was combined with blending desalinated and natural water to achieve the required quality, was generated. The effects of remineralization costs and WDS physical layout on the optimal policy were examined by sensitivity analysis. As part of the sensitivity blending natural and desalinated water near the treatment plants will be feasible up to 16.2 US cents/m(3), considering all expenses. Additional chemical injection was used to meet quality criteria when blending was not feasible. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Optimization of ACC system spacing policy on curved highway

NASA Astrophysics Data System (ADS)

Ma, Jun; Qian, Kun; Gong, Zaiyan

2017-05-01

The paper optimizes the original spacing policy when adopting VTH (Variable Time Headway), proposes to introduce the road curve curvature K to the spacing policy to cope with following the wrong vehicle or failing to follow the vehicle owing to the radar limitation of curve in ACC system. By utilizing MATLAB/Simulink, automobile longitudinal dynamics model is established. At last, the paper sets up such three common cases as the vehicle ahead runs at a uniform velocity, an accelerated velocity and hits the brake suddenly, simulates these cases on the curve with different curvature, analyzes the curve spacing policy in the perspective of safety and vehicle following efficiency and draws the conclusion whether the optimization scheme is effective or not.

On jointly optimising the changes of seasonable goods and inventory replenishment

NASA Astrophysics Data System (ADS)

Li, Zhaolin; Tao, Feng; Sun, Daewon

2012-06-01

Retailers often need to replace soon-to-be-unseasonable products with new seasonable goods when the season changes. The trade-off for such activities involves choosing between the salvage loss of the unseasonable product and the profit of selling the seasonable product early. This article develops a periodic-review inventory model for planning the changes of seasonable goods with state-dependent demand and cost parameters. We show that the single-period optimal policy for product changes is a threshold policy based on the initial inventory of the unseasonable goods. The corresponding optimal inventory policy follows a Purchase-Keep-Dispose policy if the incumbent product is kept or a base-stock policy if the incumbent product is replaced. Numerically, we find that the structure of the multi-period optimal policy resembles that of the single-period model. We propose a heuristic to solve the multi-period model and demonstrate its effectiveness. Our research provides insights into dynamically managing seasonable goods.

Information-theoretic approach to interactive learning

NASA Astrophysics Data System (ADS)

Still, S.

2009-01-01

The principles of statistical mechanics and information theory play an important role in learning and have inspired both theory and the design of numerous machine learning algorithms. The new aspect in this paper is a focus on integrating feedback from the learner. A quantitative approach to interactive learning and adaptive behavior is proposed, integrating model- and decision-making into one theoretical framework. This paper follows simple principles by requiring that the observer's world model and action policy should result in maximal predictive power at minimal complexity. Classes of optimal action policies and of optimal models are derived from an objective function that reflects this trade-off between prediction and complexity. The resulting optimal models then summarize, at different levels of abstraction, the process's causal organization in the presence of the learner's actions. A fundamental consequence of the proposed principle is that the learner's optimal action policies balance exploration and control as an emerging property. Interestingly, the explorative component is present in the absence of policy randomness, i.e. in the optimal deterministic behavior. This is a direct result of requiring maximal predictive power in the presence of feedback.

The optimal inventory policy for EPQ model under trade credit

NASA Astrophysics Data System (ADS)

Chung, Kun-Jen

2010-09-01

Huang and Huang [(2008), 'Optimal Inventory Replenishment Policy for the EPQ Model Under Trade Credit without Derivatives International Journal of Systems Science, 39, 539-546] use the algebraic method to determine the optimal inventory replenishment policy for the retailer in the extended model under trade credit. However, the algebraic method has its limit of application such that validities of proofs of Theorems 1-4 in Huang and Huang (2008) are questionable. The main purpose of this article is not only to indicate shortcomings but also to present the accurate proofs for Huang and Huang (2008).

Attitude control system testing on SCOLE

NASA Technical Reports Server (NTRS)

Shenhar, J.; Sparks, D., Jr.; Williams, J. P.; Montgomery, R. C.

1988-01-01

This paper presents implementation of two control policies on SCOLE (Space Control Laboratory Experiment), a laboratory apparatus representing an offset-feed antenna attached to the Space Shuttle by a flexible mast. In the first case, the flexible mast was restrained by cables, permitting modeling of SCOLE as a rigid-body. Starting from an arbitrary state, SCOLE was maneuvered to a specified terminal state using rigid-body minimum-time control law. In the second case, the so called single step optimal control (SSOC) theory is applied to suppress vibrations of the flexible mast mounted as a cantilever beam. Based on the SSOC theory, two parameter optimization algorithms were developed.

Implications of Preference and Problem Formulation on the Operating Policies of Complex Multi-Reservoir Systems

NASA Astrophysics Data System (ADS)

Quinn, J.; Reed, P. M.; Giuliani, M.; Castelletti, A.

2016-12-01

Optimizing the operations of multi-reservoir systems poses several challenges: 1) the high dimension of the problem's states and controls, 2) the need to balance conflicting multi-sector objectives, and 3) understanding how uncertainties impact system performance. These difficulties motivated the development of the Evolutionary Multi-Objective Direct Policy Search (EMODPS) framework, in which multi-reservoir operating policies are parameterized in a given family of functions and then optimized for multiple objectives through simulation over a set of stochastic inputs. However, properly framing these objectives remains a severe challenge and a neglected source of uncertainty. Here, we use EMODPS to optimize operating policies for a 4-reservoir system in the Red River Basin in Vietnam, exploring the consequences of optimizing to different sets of objectives related to 1) hydropower production, 2) meeting multi-sector water demands, and 3) providing flood protection to the capital city of Hanoi. We show how coordinated operation of the reservoirs can differ markedly depending on how decision makers weigh these concerns. Moreover, we illustrate how formulation choices that emphasize the mean, tail, or variability of performance across objective combinations must be evaluated carefully. Our results show that these choices can significantly improve attainable system performance, or yield severe unintended consequences. Finally, we show that satisfactory validation of the operating policies on a set of out-of-sample stochastic inputs depends as much or more on the formulation of the objectives as on effective optimization of the policies. These observations highlight the importance of carefully considering how we abstract stakeholders' objectives and of iteratively optimizing and visualizing multiple problem formulation hypotheses to ensure that we capture the most important tradeoffs that emerge from different stakeholder preferences.

Generalized networking engineering: optimal pricing and routing in multiservice networks

NASA Astrophysics Data System (ADS)

Mitra, Debasis; Wang, Qiong

2002-07-01

One of the functions of network engineering is to allocate resources optimally to forecasted demand. We generalize the mechanism by incorporating price-demand relationships into the problem formulation, and optimizing pricing and routing jointly to maximize total revenue. We consider a network, with fixed topology and link bandwidths, that offers multiple services, such as voice and data, each having characteristic price elasticity of demand, and quality of service and policy requirements on routing. Prices, which depend on service type and origin-destination, determine demands, that are routed, subject to their constraints, so as to maximize revenue. We study the basic properties of the optimal solution and prove that link shadow costs provide the basis for both optimal prices and optimal routing policies. We investigate the impact of input parameters, such as link capacities and price elasticities, on prices, demand growth, and routing policies. Asymptotic analyses, in which network bandwidth is scaled to grow, give results that are noteworthy for their qualitative insights. Several numerical examples illustrate the analyses.

System, apparatus and methods to implement high-speed network analyzers

DOEpatents

Ezick, James; Lethin, Richard; Ros-Giralt, Jordi; Szilagyi, Peter; Wohlford, David E

2015-11-10

Systems, apparatus and methods for the implementation of high-speed network analyzers are provided. A set of high-level specifications is used to define the behavior of the network analyzer emitted by a compiler. An optimized inline workflow to process regular expressions is presented without sacrificing the semantic capabilities of the processing engine. An optimized packet dispatcher implements a subset of the functions implemented by the network analyzer, providing a fast and slow path workflow used to accelerate specific processing units. Such dispatcher facility can also be used as a cache of policies, wherein if a policy is found, then packet manipulations associated with the policy can be quickly performed. An optimized method of generating DFA specifications for network signatures is also presented. The method accepts several optimization criteria, such as min-max allocations or optimal allocations based on the probability of occurrence of each signature input bit.

Design an optimum safety policy for personnel safety management - A system dynamic approach

NASA Astrophysics Data System (ADS)

Balaji, P.

2014-10-01

Personnel safety management (PSM) ensures that employee's work conditions are healthy and safe by various proactive and reactive approaches. Nowadays it is a complex phenomenon because of increasing dynamic nature of organisations which results in an increase of accidents. An important part of accident prevention is to understand the existing system properly and make safety strategies for that system. System dynamics modelling appears to be an appropriate methodology to explore and make strategy for PSM. Many system dynamics models of industrial systems have been built entirely for specific host firms. This thesis illustrates an alternative approach. The generic system dynamics model of Personnel safety management was developed and tested in a host firm. The model was undergone various structural, behavioural and policy tests. The utility and effectiveness of model was further explored through modelling a safety scenario. In order to create effective safety policy under resource constraint, DOE (Design of experiment) was used. DOE uses classic designs, namely, fractional factorials and central composite designs. It used to make second order regression equation which serve as an objective function. That function was optimized under budget constraint and optimum value used for safety policy which shown greatest improvement in overall PSM. The outcome of this research indicates that personnel safety management model has the capability for acting as instruction tool to improve understanding of safety management and also as an aid to policy making.

Policy Gradient Adaptive Dynamic Programming for Data-Based Optimal Control.

PubMed

Luo, Biao; Liu, Derong; Wu, Huai-Ning; Wang, Ding; Lewis, Frank L

2017-10-01

The model-free optimal control problem of general discrete-time nonlinear systems is considered in this paper, and a data-based policy gradient adaptive dynamic programming (PGADP) algorithm is developed to design an adaptive optimal controller method. By using offline and online data rather than the mathematical system model, the PGADP algorithm improves control policy with a gradient descent scheme. The convergence of the PGADP algorithm is proved by demonstrating that the constructed Q -function sequence converges to the optimal Q -function. Based on the PGADP algorithm, the adaptive control method is developed with an actor-critic structure and the method of weighted residuals. Its convergence properties are analyzed, where the approximate Q -function converges to its optimum. Computer simulation results demonstrate the effectiveness of the PGADP-based adaptive control method.

Robust optimal control of material flows in demand-driven supply networks

NASA Astrophysics Data System (ADS)

Laumanns, Marco; Lefeber, Erjen

2006-04-01

We develop a model based on stochastic discrete-time controlled dynamical systems in order to derive optimal policies for controlling the material flow in supply networks. Each node in the network is described as a transducer such that the dynamics of the material and information flows within the entire network can be expressed by a system of first-order difference equations, where some inputs to the system act as external disturbances. We apply methods from constrained robust optimal control to compute the explicit control law as a function of the current state. For the numerical examples considered, these control laws correspond to certain classes of optimal ordering policies from inventory management while avoiding, however, any a priori assumptions about the general form of the policy.

Large Scale Multi-area Static/Dynamic Economic Dispatch using Nature Inspired Optimization

NASA Astrophysics Data System (ADS)

Pandit, Manjaree; Jain, Kalpana; Dubey, Hari Mohan; Singh, Rameshwar

2017-04-01

Economic dispatch (ED) ensures that the generation allocation to the power units is carried out such that the total fuel cost is minimized and all the operating equality/inequality constraints are satisfied. Classical ED does not take transmission constraints into consideration, but in the present restructured power systems the tie-line limits play a very important role in deciding operational policies. ED is a dynamic problem which is performed on-line in the central load dispatch centre with changing load scenarios. The dynamic multi-area ED (MAED) problem is more complex due to the additional tie-line, ramp-rate and area-wise power balance constraints. Nature inspired (NI) heuristic optimization methods are gaining popularity over the traditional methods for complex problems. This work presents the modified particle swarm optimization (PSO) based techniques where parameter automation is effectively used for improving the search efficiency by avoiding stagnation to a sub-optimal result. This work validates the performance of the PSO variants with traditional solver GAMS for single as well as multi-area economic dispatch (MAED) on three test cases of a large 140-unit standard test system having complex constraints.

Handling Practicalities in Agricultural Policy Optimization for Water Quality Improvements

EPA Science Inventory

Bilevel and multi-objective optimization methods are often useful to spatially target agri-environmental policy throughout a watershed. This type of problem is complex and is comprised of a number of practicalities: (i) a large number of decision variables, (ii) at least two inte...

Towards a hierarchical optimization framework for spatially targeting incentive policies to promote green infrastructure amidst multiple objectives and uncertainty

EPA Science Inventory

We introduce a hierarchical optimization framework for spatially targeting green infrastructure (GI) incentive policies in order to meet objectives related to cost and environmental effectiveness. The framework explicitly simulates the interaction between multiple levels of polic...

The Dopaminergic Midbrain Encodes the Expected Certainty about Desired Outcomes.

PubMed

Schwartenbeck, Philipp; FitzGerald, Thomas H B; Mathys, Christoph; Dolan, Ray; Friston, Karl

2015-10-01

Dopamine plays a key role in learning; however, its exact function in decision making and choice remains unclear. Recently, we proposed a generic model based on active (Bayesian) inference wherein dopamine encodes the precision of beliefs about optimal policies. Put simply, dopamine discharges reflect the confidence that a chosen policy will lead to desired outcomes. We designed a novel task to test this hypothesis, where subjects played a "limited offer" game in a functional magnetic resonance imaging experiment. Subjects had to decide how long to wait for a high offer before accepting a low offer, with the risk of losing everything if they waited too long. Bayesian model comparison showed that behavior strongly supported active inference, based on surprise minimization, over classical utility maximization schemes. Furthermore, midbrain activity, encompassing dopamine projection neurons, was accurately predicted by trial-by-trial variations in model-based estimates of precision. Our findings demonstrate that human subjects infer both optimal policies and the precision of those inferences, and thus support the notion that humans perform hierarchical probabilistic Bayesian inference. In other words, subjects have to infer both what they should do as well as how confident they are in their choices, where confidence may be encoded by dopaminergic firing. © The Author 2014. Published by Oxford University Press.

The Dopaminergic Midbrain Encodes the Expected Certainty about Desired Outcomes

PubMed Central

Schwartenbeck, Philipp; FitzGerald, Thomas H. B.; Mathys, Christoph; Dolan, Ray; Friston, Karl

2015-01-01

Dopamine plays a key role in learning; however, its exact function in decision making and choice remains unclear. Recently, we proposed a generic model based on active (Bayesian) inference wherein dopamine encodes the precision of beliefs about optimal policies. Put simply, dopamine discharges reflect the confidence that a chosen policy will lead to desired outcomes. We designed a novel task to test this hypothesis, where subjects played a “limited offer” game in a functional magnetic resonance imaging experiment. Subjects had to decide how long to wait for a high offer before accepting a low offer, with the risk of losing everything if they waited too long. Bayesian model comparison showed that behavior strongly supported active inference, based on surprise minimization, over classical utility maximization schemes. Furthermore, midbrain activity, encompassing dopamine projection neurons, was accurately predicted by trial-by-trial variations in model-based estimates of precision. Our findings demonstrate that human subjects infer both optimal policies and the precision of those inferences, and thus support the notion that humans perform hierarchical probabilistic Bayesian inference. In other words, subjects have to infer both what they should do as well as how confident they are in their choices, where confidence may be encoded by dopaminergic firing. PMID:25056572

State dependent optimization of measurement policy

NASA Astrophysics Data System (ADS)

Konkarikoski, K.

2010-07-01

Measurements are the key to rational decision making. Measurement information generates value, when it is applied in the decision making. An investment cost and maintenance costs are associated with each component of the measurement system. Clearly, there is - under a given set of scenarios - a measurement setup that is optimal in expected (discounted) utility. This paper deals how the measurement policy optimization is affected by different system states and how this problem can be tackled.

The impact of uncertainty on optimal emission policies

NASA Astrophysics Data System (ADS)

Botta, Nicola; Jansson, Patrik; Ionescu, Cezar

2018-05-01

We apply a computational framework for specifying and solving sequential decision problems to study the impact of three kinds of uncertainties on optimal emission policies in a stylized sequential emission problem.We find that uncertainties about the implementability of decisions on emission reductions (or increases) have a greater impact on optimal policies than uncertainties about the availability of effective emission reduction technologies and uncertainties about the implications of trespassing critical cumulated emission thresholds. The results show that uncertainties about the implementability of decisions on emission reductions (or increases) call for more precautionary policies. In other words, delaying emission reductions to the point in time when effective technologies will become available is suboptimal when these uncertainties are accounted for rigorously. By contrast, uncertainties about the implications of exceeding critical cumulated emission thresholds tend to make early emission reductions less rewarding.

Identifying Cost-Effective Dynamic Policies to Control Epidemics

PubMed Central

Yaesoubi, Reza; Cohen, Ted

2016-01-01

We describe a mathematical decision model for identifying dynamic health policies for controlling epidemics. These dynamic policies aim to select the best current intervention based on accumulating epidemic data and the availability of resources at each decision point. We propose an algorithm to approximate dynamic policies that optimize the population’s net health benefit, a performance measure which accounts for both health and monetary outcomes. We further illustrate how dynamic policies can be defined and optimized for the control of a novel viral pathogen, where a policy maker must decide (i) when to employ or lift a transmission-reducing intervention (e.g. school closure) and (ii) how to prioritize population members for vaccination when a limited quantity of vaccines first become available. Within the context of this application, we demonstrate that dynamic policies can produce higher net health benefit than more commonly described static policies that specify a pre-determined sequence of interventions to employ throughout epidemics. PMID:27449759

Valuing hydrological alteration in multi-objective water resources management

NASA Astrophysics Data System (ADS)

Bizzi, Simone; Pianosi, Francesca; Soncini-Sessa, Rodolfo

2012-11-01

SummaryThe management of water through the impoundment of rivers by dams and reservoirs is necessary to support key human activities such as hydropower production, agriculture and flood risk mitigation. Advances in multi-objective optimization techniques and ever growing computing power make it possible to design reservoir operating policies that represent Pareto-optimal tradeoffs between multiple interests. On the one hand, such optimization methods can enhance performances of commonly targeted objectives (such as hydropower production or water supply), on the other hand they risk strongly penalizing all the interests not directly (i.e. mathematically) included in the optimization algorithm. The alteration of the downstream hydrological regime is a well established cause of ecological degradation and its evaluation and rehabilitation is commonly required by recent legislation (as the Water Framework Directive in Europe). However, it is rarely embedded in reservoir optimization routines and, even when explicitly considered, the criteria adopted for its evaluation are doubted and not commonly trusted, undermining the possibility of real implementation of environmentally friendly policies. The main challenges in defining and assessing hydrological alterations are: how to define a reference state (referencing); how to define criteria upon which to build mathematical indicators of alteration (measuring); and finally how to aggregate the indicators in a single evaluation index (valuing) that can serve as objective function in the optimization problem. This paper aims to address these issues by: (i) discussing the benefits and constrains of different approaches to referencing, measuring and valuing hydrological alteration; (ii) testing two alternative indices of hydrological alteration, one based on the established framework of Indicators of Hydrological Alteration (Richter et al., 1996), and one satisfying the mathematical properties required by widely used optimization methods based on dynamic programming; (iii) demonstrating and discussing these indices by application River Ticino, in Italy; (iv) providing a framework to effectively include hydrological alteration within reservoir operation optimization.

Optimizing adherence to antiretroviral therapy

PubMed Central

Sahay, Seema; Reddy, K. Srikanth; Dhayarkar, Sampada

2011-01-01

HIV has now become a manageable chronic disease. However, the treatment outcomes may get hampered by suboptimal adherence to ART. Adherence optimization is a concrete reality in the wake of ‘universal access’ and it is imperative to learn lessons from various studies and programmes. This review examines current literature on ART scale up, treatment outcomes of the large scale programmes and the role of adherence therein. Social, behavioural, biological and programme related factors arise in the context of ART adherence optimization. While emphasis is laid on adherence, retention of patients under the care umbrella emerges as a major challenge. An in-depth understanding of patients’ health seeking behaviour and health care delivery system may be useful in improving adherence and retention of patients in care continuum and programme. A theoretical framework to address the barriers and facilitators has been articulated to identify problematic areas in order to intervene with specific strategies. Empirically tested objective adherence measurement tools and approaches to assess adherence in clinical/ programme settings are required. Strengthening of ART programmes would include appropriate policies for manpower and task sharing, integrating traditional health sector, innovations in counselling and community support. Implications for the use of theoretical model to guide research, clinical practice, community involvement and policy as part of a human rights approach to HIV disease is suggested. PMID:22310817

People adopt optimal policies in simple decision-making, after practice and guidance.

PubMed

Evans, Nathan J; Brown, Scott D

2017-04-01

Organisms making repeated simple decisions are faced with a tradeoff between urgent and cautious strategies. While animals can adopt a statistically optimal policy for this tradeoff, findings about human decision-makers have been mixed. Some studies have shown that people can optimize this "speed-accuracy tradeoff", while others have identified a systematic bias towards excessive caution. These issues have driven theoretical development and spurred debate about the nature of human decision-making. We investigated a potential resolution to the debate, based on two factors that routinely differ between human and animal studies of decision-making: the effects of practice, and of longer-term feedback. Our study replicated the finding that most people, by default, are overly cautious. When given both practice and detailed feedback, people moved rapidly towards the optimal policy, with many participants reaching optimality with less than 1 h of practice. Our findings have theoretical implications for cognitive and neural models of simple decision-making, as well as methodological implications.

Optimal replenishment and credit policy in supply chain inventory model under two levels of trade credit with time- and credit-sensitive demand involving default risk

NASA Astrophysics Data System (ADS)

Mahata, Puspita; Mahata, Gour Chandra; Kumar De, Sujit

2018-03-01

Traditional supply chain inventory modes with trade credit usually only assumed that the up-stream suppliers offered the down-stream retailers a fixed credit period. However, in practice the retailers will also provide a credit period to customers to promote the market competition. In this paper, we formulate an optimal supply chain inventory model under two levels of trade credit policy with default risk consideration. Here, the demand is assumed to be credit-sensitive and increasing function of time. The major objective is to determine the retailer's optimal credit period and cycle time such that the total profit per unit time is maximized. The existence and uniqueness of the optimal solution to the presented model are examined, and an easy method is also shown to find the optimal inventory policies of the considered problem. Finally, numerical examples and sensitive analysis are presented to illustrate the developed model and to provide some managerial insights.

Decentralized stabilization for a class of continuous-time nonlinear interconnected systems using online learning optimal control approach.

PubMed

Liu, Derong; Wang, Ding; Li, Hongliang

2014-02-01

In this paper, using a neural-network-based online learning optimal control approach, a novel decentralized control strategy is developed to stabilize a class of continuous-time nonlinear interconnected large-scale systems. First, optimal controllers of the isolated subsystems are designed with cost functions reflecting the bounds of interconnections. Then, it is proven that the decentralized control strategy of the overall system can be established by adding appropriate feedback gains to the optimal control policies of the isolated subsystems. Next, an online policy iteration algorithm is presented to solve the Hamilton-Jacobi-Bellman equations related to the optimal control problem. Through constructing a set of critic neural networks, the cost functions can be obtained approximately, followed by the control policies. Furthermore, the dynamics of the estimation errors of the critic networks are verified to be uniformly and ultimately bounded. Finally, a simulation example is provided to illustrate the effectiveness of the present decentralized control scheme.

Bionomic Exploitation of a Ratio-Dependent Predator-Prey System

ERIC Educational Resources Information Center

Maiti, Alakes; Patra, Bibek; Samanta, G. P.

2008-01-01

The present article deals with the problem of combined harvesting of a Michaelis-Menten-type ratio-dependent predator-prey system. The problem of determining the optimal harvest policy is solved by invoking Pontryagin's Maximum Principle. Dynamic optimization of the harvest policy is studied by taking the combined harvest effort as a dynamic…

Dynamic remapping of parallel computations with varying resource demands

NASA Technical Reports Server (NTRS)

Nicol, D. M.; Saltz, J. H.

1986-01-01

A large class of computational problems is characterized by frequent synchronization, and computational requirements which change as a function of time. When such a problem must be solved on a message passing multiprocessor machine, the combination of these characteristics lead to system performance which decreases in time. Performance can be improved with periodic redistribution of computational load; however, redistribution can exact a sometimes large delay cost. We study the issue of deciding when to invoke a global load remapping mechanism. Such a decision policy must effectively weigh the costs of remapping against the performance benefits. We treat this problem by constructing two analytic models which exhibit stochastically decreasing performance. One model is quite tractable; we are able to describe the optimal remapping algorithm, and the optimal decision policy governing when to invoke that algorithm. However, computational complexity prohibits the use of the optimal remapping decision policy. We then study the performance of a general remapping policy on both analytic models. This policy attempts to minimize a statistic W(n) which measures the system degradation (including the cost of remapping) per computation step over a period of n steps. We show that as a function of time, the expected value of W(n) has at most one minimum, and that when this minimum exists it defines the optimal fixed-interval remapping policy. Our decision policy appeals to this result by remapping when it estimates that W(n) is minimized. Our performance data suggests that this policy effectively finds the natural frequency of remapping. We also use the analytic models to express the relationship between performance and remapping cost, number of processors, and the computation's stochastic activity.

Realistic nurse-led policy implementation, optimization and evaluation: novel methodological exemplar.

PubMed

Noyes, Jane; Lewis, Mary; Bennett, Virginia; Widdas, David; Brombley, Karen

2014-01-01

To report the first large-scale realistic nurse-led implementation, optimization and evaluation of a complex children's continuing-care policy. Health policies are increasingly complex, involve multiple Government departments and frequently fail to translate into better patient outcomes. Realist methods have not yet been adapted for policy implementation. Research methodology - Evaluation using theory-based realist methods for policy implementation. An expert group developed the policy and supporting tools. Implementation and evaluation design integrated diffusion of innovation theory with multiple case study and adapted realist principles. Practitioners in 12 English sites worked with Consultant Nurse implementers to manipulate the programme theory and logic of new decision-support tools and care pathway to optimize local implementation. Methods included key-stakeholder interviews, developing practical diffusion of innovation processes using key-opinion leaders and active facilitation strategies and a mini-community of practice. New and existing processes and outcomes were compared for 137 children during 2007-2008. Realist principles were successfully adapted to a shorter policy implementation and evaluation time frame. Important new implementation success factors included facilitated implementation that enabled 'real-time' manipulation of programme logic and local context to best-fit evolving theories of what worked; using local experiential opinion to change supporting tools to more realistically align with local context and what worked; and having sufficient existing local infrastructure to support implementation. Ten mechanisms explained implementation success and differences in outcomes between new and existing processes. Realistic policy implementation methods have advantages over top-down approaches, especially where clinical expertise is low and unlikely to diffuse innovations 'naturally' without facilitated implementation and local optimization. © 2013 John Wiley & Sons Ltd.

Optimal contant time injection policy for enhanced oil recovery and characterization of optimal viscous profiles

NASA Astrophysics Data System (ADS)

Daripa, Prabir

2011-11-01

We numerically investigate the optimal viscous profile in constant time injection policy of enhanced oil recovery. In particular, we investigate the effect of a combination of interfacial and layer instabilities in three-layer porous media flow on the overall growth of instabilities and thereby characterize the optimal viscous profile. Results based on monotonic and non-monotonic viscous profiles will be presented. Time permitting. we will also present results on multi-layer porous media flows for Newtonian and non-Newtonian fluids and compare the results. The support of Qatar National Fund under a QNRF Grant is acknowledged.

A model of interaction between anticorruption authority and corruption groups

DOE Office of Scientific and Technical Information (OSTI.GOV)

Neverova, Elena G.; Malafeyef, Oleg A.

The paper provides a model of interaction between anticorruption unit and corruption groups. The main policy functions of the anticorruption unit involve reducing corrupt practices in some entities through an optimal approach to resource allocation and effective anticorruption policy. We develop a model based on Markov decision-making process and use Howard’s policy-improvement algorithm for solving an optimal decision strategy. We examine the assumption that corruption groups retaliate against the anticorruption authority to protect themselves. This model was implemented through stochastic game.

The importance of environmental variability and management control error to optimal harvest policies

USGS Publications Warehouse

Hunter, C.M.; Runge, M.C.

2004-01-01

State-dependent strategies (SDSs) are the most general form of harvest policy because they allow the harvest rate to depend, without constraint, on the state of the system. State-dependent strategies that provide an optimal harvest rate for any system state can be calculated, and stochasticity can be appropriately accommodated in this optimization. Stochasticity poses 2 challenges to harvest policies: (1) the population will never be at the equilibrium state; and (2) stochasticity induces uncertainty about future states. We investigated the effects of 2 types of stochasticity, environmental variability and management control error, on SDS harvest policies for a white-tailed deer (Odocoileus virginianus) model, and contrasted these with a harvest policy based on maximum sustainable yield (MSY). Increasing stochasticity resulted in more conservative SDSs; that is, higher population densities were required to support the same harvest rate, but these effects were generally small. As stochastic effects increased, SDSs performed much better than MSY. Both deterministic and stochastic SDSs maintained maximum mean annual harvest yield (AHY) and optimal equilibrium population size (Neq) in a stochastic environment, whereas an MSY policy could not. We suggest 3 rules of thumb for harvest management of long-lived vertebrates in stochastic systems: (1) an SDS is advantageous over an MSY policy, (2) using an SDS rather than an MSY is more important than whether a deterministic or stochastic SDS is used, and (3) for SDSs, rankings of the variability in management outcomes (e.g., harvest yield) resulting from parameter stochasticity can be predicted by rankings of the deterministic elasticities.

Reducing stock-outs of essential tuberculosis medicines: a system dynamics modelling approach to supply chain management.

PubMed

Bam, L; McLaren, Z M; Coetzee, E; von Leipzig, K H

2017-10-01

The under-performance of supply chains presents a significant hindrance to disease control in developing countries. Stock-outs of essential medicines lead to treatment interruption which can force changes in patient drug regimens, drive drug resistance and increase mortality. This study is one of few to quantitatively evaluate the effectiveness of supply chain policies in reducing shortages and costs. This study develops a systems dynamics simulation model of the downstream supply chain for amikacin, a second-line tuberculosis drug using 10 years of South African data. We evaluate current supply chain performance in terms of reliability, responsiveness and agility, following the widely-used Supply Chain Operation Reference framework. We simulate 141 scenarios that represent different combinations of supplier characteristics, inventory management strategies and demand forecasting methods to identify the Pareto optimal set of management policies that jointly minimize the number of shortages and total cost. Despite long supplier lead times and unpredictable demand, the amikacin supply chain is 98% reliable and agile enough to accommodate a 20% increase in demand without a shortage. However, this is accomplished by overstocking amikacin by 167%, which incurs high holding costs. The responsiveness of suppliers is low: only 57% of orders are delivered to the central provincial drug depot within one month. We identify three Pareto optimal safety stock management policies. Short supplier lead time can produce Pareto optimal outcomes even in the absence of other optimal policies. This study produces concrete, actionable guidelines to cost-effectively reduce stock-outs by implementing optimal supply chain policies. Preferentially selecting drug suppliers with short lead times accommodates unexpected changes in demand. Optimal supply chain management should be an essential component of national policy to reduce the mortality rate. © The Author 2017. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Different approaches for centralized and decentralized water system management in multiple decision makers' problems

NASA Astrophysics Data System (ADS)

Anghileri, D.; Giuliani, M.; Castelletti, A.

2012-04-01

There is a general agreement that one of the most challenging issues related to water system management is the presence of many and often conflicting interests as well as the presence of several and independent decision makers. The traditional approach to multi-objective water systems management is a centralized management, in which an ideal central regulator coordinates the operation of the whole system, exploiting all the available information and balancing all the operating objectives. Although this approach allows to obtain Pareto-optimal solutions representing the maximum achievable benefit, it is based on assumptions which strongly limits its application in real world contexts: 1) top-down management, 2) existence of a central regulation institution, 3) complete information exchange within the system, 4) perfect economic efficiency. A bottom-up decentralized approach seems therefore to be more suitable for real case applications since different reservoir operators may maintain their independence. In this work we tested the consequences of a change in the water management approach moving from a centralized toward a decentralized one. In particular we compared three different cases: the centralized management approach, the independent management approach where each reservoir operator takes the daily release decision maximizing (or minimizing) his operating objective independently from each other, and an intermediate approach, leading to the Nash equilibrium of the associated game, where different reservoir operators try to model the behaviours of the other operators. The three approaches are demonstrated using a test case-study composed of two reservoirs regulated for the minimization of flooding in different locations. The operating policies are computed by solving one single multi-objective optimal control problem, in the centralized management approach; multiple single-objective optimization problems, i.e. one for each operator, in the independent case; using techniques related to game theory for the description of the interaction between the two operators, in the last approach. Computational results shows that the Pareto-optimal control policies obtained in the centralized approach dominate the control policies of both the two cases of decentralized management and that the so called price of anarchy increases moving toward the independent management approach. However, the Nash equilibrium solution seems to be the most promising alternative because it represents a good compromise in maximizing management efficiency without limiting the behaviours of the reservoir operators.

Testing the price and affordability of healthy and current (unhealthy) diets and the potential impacts of policy change in Australia.

PubMed

Lee, Amanda J; Kane, Sarah; Ramsey, Rebecca; Good, Elizabeth; Dick, Mathew

2016-04-12

Price and affordability of foods are important determinants of health. Targeted food pricing policies may help improve population diets. However, methods producing comparable data to inform relevant policy decisions are lacking in Australia and globally. The objective was to develop and pilot standardised methods to assess the price, relative price and affordability of healthy (recommended) and current (unhealthy) diets and test impacts of a potential policy change. Methods followed the optimal approach proposed by INFORMAS using recent Australian dietary intake data and guidelines. Draft healthy and current (unhealthy) diet baskets were developed for five household structures. Food prices were collected in stores in a high and low SES location in Brisbane, Australia. Diet prices were calculated and compared with household incomes, and with potential changes to the Australian Taxation System. Wilcoxen-signed rank tests were used to compare differences in price. The draft tools and protocols were deemed acceptable at household level, but methods could be refined. All households spend more on current (unhealthy) diets than required to purchase healthy (recommended) diets, with the majority (53-64 %) of the food budget being spent on 'discretionary' choices, including take-away foods and alcohol. A healthy diet presently costs between 20-31 % of disposable income of low income households, but would become unaffordable for these families under proposed changes to expand the GST to apply to all foods in Australia. Results confirmed that diet pricing methods providing meaningful, comparable data to inform potential fiscal and health policy actions can be developed, but draft tools should be refined. Results suggest that healthy diets can be more affordable than current (unhealthy) diets in Australia, but other factors may be as important as price in determining food choices.

Design an optimum safety policy for personnel safety management - A system dynamic approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Balaji, P.

2014-10-06

Personnel safety management (PSM) ensures that employee's work conditions are healthy and safe by various proactive and reactive approaches. Nowadays it is a complex phenomenon because of increasing dynamic nature of organisations which results in an increase of accidents. An important part of accident prevention is to understand the existing system properly and make safety strategies for that system. System dynamics modelling appears to be an appropriate methodology to explore and make strategy for PSM. Many system dynamics models of industrial systems have been built entirely for specific host firms. This thesis illustrates an alternative approach. The generic system dynamicsmore » model of Personnel safety management was developed and tested in a host firm. The model was undergone various structural, behavioural and policy tests. The utility and effectiveness of model was further explored through modelling a safety scenario. In order to create effective safety policy under resource constraint, DOE (Design of experiment) was used. DOE uses classic designs, namely, fractional factorials and central composite designs. It used to make second order regression equation which serve as an objective function. That function was optimized under budget constraint and optimum value used for safety policy which shown greatest improvement in overall PSM. The outcome of this research indicates that personnel safety management model has the capability for acting as instruction tool to improve understanding of safety management and also as an aid to policy making.« less

Nonlinear optimal control policies for buoyancy-driven flows in the built environment

NASA Astrophysics Data System (ADS)

Nabi, Saleh; Grover, Piyush; Caulfield, Colm

2017-11-01

We consider optimal control of turbulent buoyancy-driven flows in the built environment, focusing on a model test case of displacement ventilation with a time-varying heat source. The flow is modeled using the unsteady Reynolds-averaged equations (URANS). To understand the stratification dynamics better, we derive a low-order partial-mixing ODE model extending the buoyancy-driven emptying filling box problem to the case of where both the heat source and the (controlled) inlet flow are time-varying. In the limit of a single step-change in the heat source strength, our model is consistent with that of Bower et al.. Our model considers the dynamics of both `filling' and `intruding' added layers due to a time-varying source and inlet flow. A nonlinear direct-adjoint-looping optimal control formulation yields time-varying values of temperature and velocity of the inlet flow that lead to `optimal' time-averaged temperature relative to appropriate objective functionals in a region of interest.

Essays on the Economics of Climate Change, Biofuel and Food Prices

NASA Astrophysics Data System (ADS)

Seguin, Charles

Climate change is likely to be the most important global pollution problem that humanity has had to face so far. In this dissertation, I tackle issues directly and indirectly related to climate change, bringing my modest contribution to the body of human creativity trying to deal with climate change. First, I look at the impact of non-convex feedbacks on the optimal climate policy. Second, I try to derive the optimal biofuel policy acknowledging the potential negative impacts that biofuel production might have on food supply. Finally, I test empirically for the presence of loss aversion in food purchases, which might play a role in the consumer response to food price changes brought about by biofuel production. Non-convexities in feedback processes are increasingly found to be important in the climate system. To evaluate their impact on the optimal greenhouse gas (GHG) abate- ment policy, I introduce non-convex feedbacks in a stochastic pollution control model. I numerically calibrate the model to represent the mitigation of greenhouse gas (GHG) emissions contributing to global climate change. This approach makes two contributions to the literature. First, it develops a framework to tackle stochastic non-convex pollu- tion management problems. Second, it applies this framework to the problem of climate change. This approach is in contrast to most of the economic literature on climate change that focuses either on linear feedbacks or environmental thresholds. I find that non-convex feedbacks lead to a decision threshold in the optimal mitigation policy, and I characterize how this threshold depends on feedback parameters and stochasticity. There is great hope that biofuel can help reduce greenhouse gas emissions from fossil fuel. However, there are some concerns that biofuel would increase food prices. In an optimal control model, a co-author and I look at the optimal biofuel production when it competes for land with food production. In addition oil is not exhaustible and output is subject to climate change induced damages. We find that the competitive outcome does not necessarily yield an underproduction of biofuels, but when it does, second best policies like subsidies and mandates can improve welfare. In marketing, there has been extensive empirical research to ascertain whether there is evidence of loss aversion as predicted by several reference price preference theories. Most of that literature finds that there is indeed evidence of loss aversion for many different goods. I argue that it is possible that some of that evidence seemingly supporting loss aversion arises because price endogeneity is not properly taken into account. Using scanner data I study four product categories: bread, chicken, corn and tortilla chips, and pasta. Taking prices as exogenous, I find evidence of loss aversion for bread and corn and tortilla chips. However, when instrumenting prices, the "loss aversion evidence" disappears.

Assessing the Optimal Length of Parental Leave for Child and Parental Well-Being: How Can Research Inform Policy?

ERIC Educational Resources Information Center

Galtry, Judith; Callister, Paul

2005-01-01

Parental leave is a complex area of public policy. Concerns include health protection for working mothers, equal employment opportunities for women, access to adequate antenatal and birthing care, maternal recovery, optimal nutrition for infants, and gender equality within families. Given this complexity, the design of parental leave schemes,…

Continuous Linguistic Rhetorical Education as a Means of Optimizing Language Policy in Russian Multinational Regions

ERIC Educational Resources Information Center

Vorozhbitova, Alexandra A.; Konovalova, Galina M.; Ogneva, Tatiana N.; Chekulaeva, Natalia Y.

2017-01-01

Drawing on the function of Russian as a state language the paper proposes a concept of continuous linguistic rhetorical (LR) education perceived as a means of optimizing language policy in Russian multinational regions. LR education as an innovative pedagogical system shapes a learner's readiness for self-projection as a strong linguistic…

Optimal screening and donor management in a public stool bank.

PubMed

Kazerouni, Abbas; Burgess, James; Burns, Laura J; Wein, Lawrence M

2015-12-17

Fecal microbiota transplantation is an effective treatment for recurrent Clostridium difficile infection and is being investigated as a treatment for other microbiota-associated diseases. To facilitate these activities, an international public stool bank has been created, which screens donors and processes stools in a standardized manner. The goal of this research is to use mathematical modeling and analysis to optimize screening and donor management at the stool bank. Compared to the current policy of screening active donors every 60 days before releasing their quarantined stools for sale, costs can be reduced by 10.3 % by increasing the screening frequency to every 36 days. In addition, the stool production rate varies widely across donors, and using donor-specific screening, where higher producers are screened more frequently, also reduces costs, as does introducing an interim (i.e., between consecutive regular tests) stool test for just rotavirus and C. difficile. We also derive a donor release (i.e., into the system) policy that allows the supply to approximately match an exponentially increasing deterministic demand. More frequent screening, interim screening for rotavirus and C. difficile, and donor-specific screening, where higher stool producers are screened more frequently, are all cost-reducing measures. If screening costs decrease in the future (e.g., as a result of bringing screening in house), a bottleneck for implementing some of these recommendations may be the reluctance of donors to undergo serum screening more frequently than monthly.

A hybrid inventory management system respondingto regular demand and surge demand

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mohammad S. Roni; Mingzhou Jin; Sandra D. Eksioglu

2014-06-01

This paper proposes a hybrid policy for a stochastic inventory system facing regular demand and surge demand. The combination of two different demand patterns can be observed in many areas, such as healthcare inventory and humanitarian supply chain management. The surge demand has a lower arrival rate but higher demand volume per arrival. The solution approach proposed in this paper incorporates the level crossing method and mixed integer programming technique to optimize the hybrid inventory policy with both regular orders and emergency orders. The level crossing method is applied to obtain the equilibrium distributions of inventory levels under a givenmore » policy. The model is further transformed into a mixed integer program to identify an optimal hybrid policy. A sensitivity analysis is conducted to investigate the impact of parameters on the optimal inventory policy and minimum cost. Numerical results clearly show the benefit of using the proposed hybrid inventory model. The model and solution approach could help healthcare providers or humanitarian logistics providers in managing their emergency supplies in responding to surge demands.« less

Optimization of cooling strategy and seeding by FBRM analysis of batch crystallization

NASA Astrophysics Data System (ADS)

Zhang, Dejiang; Liu, Lande; Xu, Shijie; Du, Shichao; Dong, Weibing; Gong, Junbo

2018-03-01

A method is presented for optimizing the cooling strategy and seed loading simultaneously. Focused beam reflectance measurement (FBRM) was used to determine the approximating optimal cooling profile. Using these results in conjunction with constant growth rate assumption, modified Mullin-Nyvlt trajectory could be calculated. This trajectory could suppress secondary nucleation and has the potential to control product's polymorph distribution. Comparing with linear and two step cooling, modified Mullin-Nyvlt trajectory have a larger size distribution and a better morphology. Based on the calculating results, the optimized seed loading policy was also developed. This policy could be useful for guiding the batch crystallization process.

An Optimization Model for Expired Drug Recycling Logistics Networks and Government Subsidy Policy Design Based on Tri-level Programming

PubMed Central

Huang, Hui; Li, Yuyu; Huang, Bo; Pi, Xing

2015-01-01

In order to recycle and dispose of all people’s expired drugs, the government should design a subsidy policy to stimulate users to return their expired drugs, and drug-stores should take the responsibility of recycling expired drugs, in other words, to be recycling stations. For this purpose it is necessary for the government to select the right recycling stations and treatment stations to optimize the expired drug recycling logistics network and minimize the total costs of recycling and disposal. This paper establishes a tri-level programming model to study how the government can optimize an expired drug recycling logistics network and the appropriate subsidy policies. Furthermore, a Hybrid Genetic Simulated Annealing Algorithm (HGSAA) is proposed to search for the optimal solution of the model. An experiment is discussed to illustrate the good quality of the recycling logistics network and government subsides obtained by the HGSAA. The HGSAA is proven to have the ability to converge on the global optimal solution, and to act as an effective algorithm for solving the optimization problem of expired drug recycling logistics network and government subsidies. PMID:26184252

An Optimization Model for Expired Drug Recycling Logistics Networks and Government Subsidy Policy Design Based on Tri-level Programming.

PubMed

Huang, Hui; Li, Yuyu; Huang, Bo; Pi, Xing

2015-07-09

In order to recycle and dispose of all people's expired drugs, the government should design a subsidy policy to stimulate users to return their expired drugs, and drug-stores should take the responsibility of recycling expired drugs, in other words, to be recycling stations. For this purpose it is necessary for the government to select the right recycling stations and treatment stations to optimize the expired drug recycling logistics network and minimize the total costs of recycling and disposal. This paper establishes a tri-level programming model to study how the government can optimize an expired drug recycling logistics network and the appropriate subsidy policies. Furthermore, a Hybrid Genetic Simulated Annealing Algorithm (HGSAA) is proposed to search for the optimal solution of the model. An experiment is discussed to illustrate the good quality of the recycling logistics network and government subsides obtained by the HGSAA. The HGSAA is proven to have the ability to converge on the global optimal solution, and to act as an effective algorithm for solving the optimization problem of expired drug recycling logistics network and government subsidies.

Optimal combinations of control strategies and cost-effective analysis for visceral leishmaniasis disease transmission.

PubMed

Biswas, Santanu; Subramanian, Abhishek; ELMojtaba, Ibrahim M; Chattopadhyay, Joydev; Sarkar, Ram Rup

2017-01-01

Visceral leishmaniasis (VL) is a deadly neglected tropical disease that poses a serious problem in various countries all over the world. Implementation of various intervention strategies fail in controlling the spread of this disease due to issues of parasite drug resistance and resistance of sandfly vectors to insecticide sprays. Due to this, policy makers need to develop novel strategies or resort to a combination of multiple intervention strategies to control the spread of the disease. To address this issue, we propose an extensive SIR-type model for anthroponotic visceral leishmaniasis transmission with seasonal fluctuations modeled in the form of periodic sandfly biting rate. Fitting the model for real data reported in South Sudan, we estimate the model parameters and compare the model predictions with known VL cases. Using optimal control theory, we study the effects of popular control strategies namely, drug-based treatment of symptomatic and PKDL-infected individuals, insecticide treated bednets and spray of insecticides on the dynamics of infected human and vector populations. We propose that the strategies remain ineffective in curbing the disease individually, as opposed to the use of optimal combinations of the mentioned strategies. Testing the model for different optimal combinations while considering periodic seasonal fluctuations, we find that the optimal combination of treatment of individuals and insecticide sprays perform well in controlling the disease for the time period of intervention introduced. Performing a cost-effective analysis we identify that the same strategy also proves to be efficacious and cost-effective. Finally, we suggest that our model would be helpful for policy makers to predict the best intervention strategies for specific time periods and their appropriate implementation for elimination of visceral leishmaniasis.

Evolutional Optimization on Material Ordering and Inventory Control of Supply Chain through Incentive Scheme

NASA Astrophysics Data System (ADS)

Prasertwattana, Kanit; Shimizu, Yoshiaki; Chiadamrong, Navee

This paper studied the material ordering and inventory control of supply chain systems. The effect of controlling policies is analyzed under three different configurations of the supply chain systems, and the formulated problem has been solved by using an evolutional optimization method known as Differential Evolution (DE). The numerical results show that the coordinating policy with the incentive scheme outperforms the other policies and can improve the performance of the overall system as well as all members under the concept of supply chain management.

SWAB/NVALT (Dutch Working Party on Antibiotic Policy and Dutch Association of Chest Physicians) guidelines on the management of community-acquired pneumonia in adults.

PubMed

Wiersinga, W J; Bonten, M J; Boersma, W G; Jonkers, R E; Aleva, R M; Kullberg, B J; Schouten, J A; Degener, J E; Janknegt, R; Verheij, T J; Sachs, A P E; Prins, J M

2012-03-01

The Dutch Working Party on Antibiotic Policy (SWAB) and the Dutch Association of Chest Physicians (NVALT) convened a joint committee to develop evidence-based guidelines on the diagnosis and treatment of community acquired pneumonia (CAP). The guidelines are intended for adult patients with CAP who present at the hospital and are treated as outpatients as well as for hospitalised patients up to 72 hours after admission. Areas covered include current patterns of epidemiology and antibiotic resistance of causative agents of CAP in the Netherlands, the possibility to predict the causative agent of CAP on the basis of clinical data at first presentation, risk factors associated with specific pathogens, the importance of the severity of disease upon presentation for choice of initial treatment, the role of rapid diagnostic tests in treatment decisions, the optimal initial empiric treatment and treatment when a specific pathogen has been identified, the timeframe in which the first dose of antibiotics should be given, optimal duration of antibiotic treatment and antibiotic switch from the intravenous to the oral route. Additional recommendations are made on the role of radiological investigations in the diagnostic work-up of patients with a clinical suspicion of CAP, on the potential benefit of adjunctive immunotherapy, and on the policy for patients with parapneumonic effusions.

A dynamic parking charge optimal control model under perspective of commuters' evolutionary game behavior

NASA Astrophysics Data System (ADS)

Lin, XuXun; Yuan, PengCheng

2018-01-01

In this research we consider commuters' dynamic learning effect by modeling the trip mode choice behavior from a new perspective of dynamic evolutionary game theory. We explore the behavior pattern of different types of commuters and study the evolution path and equilibrium properties under different traffic conditions. We further establish a dynamic parking charge optimal control (referred to as DPCOC) model to alter commuters' trip mode choice while minimizing the total social cost. Numerical tests show. (1) Under fixed parking fee policy, the evolutionary results are completely decided by the travel time and the only method for public transit induction is to increase the parking charge price. (2) Compared with fixed parking fee policy, DPCOC policy proposed in this research has several advantages. Firstly, it can effectively turn the evolutionary path and evolutionary stable strategy to a better situation while minimizing the total social cost. Secondly, it can reduce the sensitivity of trip mode choice behavior to traffic congestion and improve the ability to resist interferences and emergencies. Thirdly, it is able to control the private car proportion to a stable state and make the trip behavior more predictable for the transportation management department. The research results can provide theoretical basis and decision-making references for commuters' mode choice prediction, dynamic setting of urban parking charge prices and public transit induction.

Active model-based balancing strategy for self-reconfigurable batteries

NASA Astrophysics Data System (ADS)

Bouchhima, Nejmeddine; Schnierle, Marc; Schulte, Sascha; Birke, Kai Peter

2016-08-01

This paper describes a novel balancing strategy for self-reconfigurable batteries where the discharge and charge rates of each cell can be controlled. While much effort has been focused on improving the hardware architecture of self-reconfigurable batteries, energy equalization algorithms have not been systematically optimized in terms of maximizing the efficiency of the balancing system. Our approach includes aspects of such optimization theory. We develop a balancing strategy for optimal control of the discharge rate of battery cells. We first formulate the cell balancing as a nonlinear optimal control problem, which is modeled afterward as a network program. Using dynamic programming techniques and MATLAB's vectorization feature, we solve the optimal control problem by generating the optimal battery operation policy for a given drive cycle. The simulation results show that the proposed strategy efficiently balances the cells over the life of the battery, an obvious advantage that is absent in the other conventional approaches. Our algorithm is shown to be robust when tested against different influencing parameters varying over wide spectrum on different drive cycles. Furthermore, due to the little computation time and the proved low sensitivity to the inaccurate power predictions, our strategy can be integrated in a real-time system.

A policy iteration approach to online optimal control of continuous-time constrained-input systems.

PubMed

Modares, Hamidreza; Naghibi Sistani, Mohammad-Bagher; Lewis, Frank L

2013-09-01

This paper is an effort towards developing an online learning algorithm to find the optimal control solution for continuous-time (CT) systems subject to input constraints. The proposed method is based on the policy iteration (PI) technique which has recently evolved as a major technique for solving optimal control problems. Although a number of online PI algorithms have been developed for CT systems, none of them take into account the input constraints caused by actuator saturation. In practice, however, ignoring these constraints leads to performance degradation or even system instability. In this paper, to deal with the input constraints, a suitable nonquadratic functional is employed to encode the constraints into the optimization formulation. Then, the proposed PI algorithm is implemented on an actor-critic structure to solve the Hamilton-Jacobi-Bellman (HJB) equation associated with this nonquadratic cost functional in an online fashion. That is, two coupled neural network (NN) approximators, namely an actor and a critic are tuned online and simultaneously for approximating the associated HJB solution and computing the optimal control policy. The critic is used to evaluate the cost associated with the current policy, while the actor is used to find an improved policy based on information provided by the critic. Convergence to a close approximation of the HJB solution as well as stability of the proposed feedback control law are shown. Simulation results of the proposed method on a nonlinear CT system illustrate the effectiveness of the proposed approach. Copyright © 2013 ISA. All rights reserved.

Economic analysis of secondary and enhanced oil recovery techniques in Wyoming

NASA Astrophysics Data System (ADS)

Kara, Erdal

This dissertation primarily aims to theoretically analyze a firm's optimization of enhanced oil recovery (EOR) and carbon dioxide sequestration under different social policies and empirically analyze the firm's optimization of enhanced oil recovery. The final part of the dissertation empirically analyzes how geological factors and water injection management influence oil recovery. The first chapter builds a theoretical model to analyze economic optimization of EOR and geological carbon sequestration under different social policies. Specifically, it analyzes how social policies on sequestration influence the extent of oil operations, optimal oil production and CO2 sequestration. The theoretical results show that the socially optimal policy is a subsidy on the net CO2 sequestration, assuming negative net emissions from EOR. Such a policy is expected to increase a firm's total carbon dioxide sequestration. The second chapter statistically estimates the theoretical oil production model and its different versions. Empirical results are not robust over different estimation techniques and not in line with the theoretical production model. The last part of the second chapter utilizes a simplified version of theoretical model and concludes that EOR via CO2 injection improves oil recovery. The final chapter analyzes how a contemporary oil recovery technology (water flooding of oil reservoirs) and various reservoir-specific geological factors influence oil recovery in Wyoming. The results show that there is a positive concave relationship between cumulative water injection and cumulative oil recovery and also show that certain geological factors affect the oil recovery. Moreover, the curvature of the concave functional relationship between cumulative water injection and oil recovery is reservoir-specific due to heterogeneities among different reservoirs.

'Are we there yet?' - operationalizing the concept of Integrated Public Health Policies.

PubMed

Hendriks, Anna-Marie; Habraken, Jolanda; Jansen, Maria W J; Gubbels, Jessica S; De Vries, Nanne K; van Oers, Hans; Michie, Susan; Atkins, L; Kremers, Stef P J

2014-02-01

Although 'integrated' public health policies are assumed to be the ideal way to optimize public health, it remains hard to determine how far removed we are from this ideal, since clear operational criteria and defining characteristics are lacking. A literature review identified gaps in previous operationalizations of integrated public health policies. We searched for an approach that could fill these gaps. We propose the following defining characteristics of an integrated policy: (1) the combination of policies includes an appropriate mix of interventions that optimizes the functioning of the behavioral system, thus ensuring that motivation, capability and opportunity interact in such a way that they promote the preferred (health-promoting) behavior of the target population, and (2) the policies are implemented by the relevant policy sectors from different policy domains. Our criteria should offer added value since they describe pathways in the process towards formulating integrated policy. The aim of introducing our operationalization is to assist policy makers and researchers in identifying truly integrated cases. The Behavior Change Wheel proved to be a useful framework to develop operational criteria to assess the current state of integrated public health policies in practice. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

The forest and agricultural sector optimization model (FASOM): model structure and policy applications.

Treesearch

Darius M. Adams; Ralph J. Alig; J.M. Callaway; Bruce A. McCarl; Steven M. Winnett

1996-01-01

The Forest and Agricultural Sector Optimization Model (FASOM) is a dynamic, nonlinear programming model of the forest and agricultural sectors in the United States. The FASOM model initially was developed to evaluate welfare and market impacts of alternative policies for sequestering carbon in trees but also has been applied to a wider range of forest and agricultural...

Relations between information, time, and value of water

NASA Astrophysics Data System (ADS)

Weijs, S. V.; Galindo, L. C.

2015-12-01

This research uses with stochastic dynamic programming (SDP) as a tool to reveal economic information about managed water resources. An application to the operation of an example hydropower reservoir is presented. SDP explicitly balances the marginal value of water for immediate use and its expected opportunity cost of not having more water available for future use. The result of an SDP analysis is a steady state policy, which gives the optimal decision as a function of the state. A commonly applied form gives the optimal release as a function of the month, current reservoir level and current inflow to the reservoir. The steady state policy can be complemented with a real-time management strategy, that can depend on more real-time information. An information-theoretical perspective is given on how this information influences the value of water, and how to deal with that influence in hydropower reservoir optimization. This results in some conjectures about how the information gain from real-time operation could affect the optimal long term policy. Another issue is the sharing of increased benefits that result from this information gain in a multi-objective setting. It is argued that this should be accounted for in negotiations about an operation policy.

Network meta-analysis of diagnostic test accuracy studies identifies and ranks the optimal diagnostic tests and thresholds for health care policy and decision-making.

PubMed

Owen, Rhiannon K; Cooper, Nicola J; Quinn, Terence J; Lees, Rosalind; Sutton, Alex J

2018-07-01

Network meta-analyses (NMA) have extensively been used to compare the effectiveness of multiple interventions for health care policy and decision-making. However, methods for evaluating the performance of multiple diagnostic tests are less established. In a decision-making context, we are often interested in comparing and ranking the performance of multiple diagnostic tests, at varying levels of test thresholds, in one simultaneous analysis. Motivated by an example of cognitive impairment diagnosis following stroke, we synthesized data from 13 studies assessing the efficiency of two diagnostic tests: Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), at two test thresholds: MMSE <25/30 and <27/30, and MoCA <22/30 and <26/30. Using Markov chain Monte Carlo (MCMC) methods, we fitted a bivariate network meta-analysis model incorporating constraints on increasing test threshold, and accounting for the correlations between multiple test accuracy measures from the same study. We developed and successfully fitted a model comparing multiple tests/threshold combinations while imposing threshold constraints. Using this model, we found that MoCA at threshold <26/30 appeared to have the best true positive rate, whereas MMSE at threshold <25/30 appeared to have the best true negative rate. The combined analysis of multiple tests at multiple thresholds allowed for more rigorous comparisons between competing diagnostics tests for decision making. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Valuing hydrological alteration in Multi-Objective reservoir management

NASA Astrophysics Data System (ADS)

Bizzi, S.; Pianosi, F.; Soncini-Sessa, R.

2012-04-01

Water management through dams and reservoirs is worldwide necessary to support key human-related activities ranging from hydropower production to water allocation for agricultural production, and flood risk mitigation. Advances in multi-objectives (MO) optimization techniques and ever growing computing power make it possible to design reservoir operating policies that represent Pareto-optimal tradeoffs between the multiple interests analysed. These progresses if on one hand are likely to enhance performances of commonly targeted objectives (such as hydropower production or water supply), on the other risk to strongly penalize all the interests not directly (i.e. mathematically) optimized within the MO algorithm. Alteration of hydrological regime, although is a well established cause of ecological degradation and its evaluation and rehabilitation are commonly required by recent legislation (as the Water Framework Directive in Europe), is rarely embedded as an objective in MO planning of optimal releases from reservoirs. Moreover, even when it is explicitly considered, the criteria adopted for its evaluation are doubted and not commonly trusted, undermining the possibility of real implementation of environmentally friendly policies. The main challenges in defining and assessing hydrological alterations are: how to define a reference state (referencing); how to define criteria upon which to build mathematical indicators of alteration (measuring); and finally how to aggregate the indicators in a single evaluation index that can be embedded in a MO optimization problem (valuing). This paper aims to address these issues by: i) discussing benefits and constrains of different approaches to referencing, measuring and valuing hydrological alteration; ii) testing two alternative indices of hydrological alteration in the context of MO problems, one based on the established framework of Indices of Hydrological Alteration (IHA, Richter et al., 1996), and a novel satisfying the mathematical properties required by widely used optimization methods based on dynamic programming; iii) discussing the ranking provided by the proposed indices for a case study in Italy where different operating policies were designed using a MO algorithm, taking into account hydropower production, irrigation supply and flood mitigation and imposing different type of minimum environmental flow; iv) providing a framework to effectively include hydrological alteration within MO problem of reservoir management. Richter, B.D., Baumgartner, J.V., Powell, J., Braun, D.P., 1996, A Method for Assessing Hydrologic Alteration within Ecosystems, Conservation Biology, 10(4), 1163-1174.

Contract portfolio optimization for a gasoline supply chain

NASA Astrophysics Data System (ADS)

Wang, Shanshan

Major oil companies sell gasoline through three channels of trade: branded (associated with long-term contracts), unbranded (associated with short-term contracts), and spot market. The branded channel provides them with a long-term secured and sustainable demand source, but requires an inflexible long-term commitment with demand and price risks. The unbranded channel provides a medium level of allocation flexibility. The spot market provides them with the greatest allocation flexibility to the changing market conditions, but the spot market's illiquidity mitigates this benefit. In order to sell the product in a profitable and sustainable way, they need an optimal contract portfolio. This dissertation addresses the contract portfolio optimization problem from different perspectives (retrospective view and forward-looking view) at different levels (strategic level, tactical level and operational level). The objective of the retrospective operational model is to develop a financial case to estimate the business value of having a dynamic optimization model and quantify the opportunity values missed in the past. This model proves the financial significance of the problem and provides top management valuable insights into the business. BP has applied the insights and principles gained from this work and implemented the model to the entire Midwest gasoline supply chain to retrospectively review optimization opportunities. The strategic model is the most parsimonious model that captures the essential economic tradeoffs among different contract types, to demonstrate the need for a contract portfolio and what drives the portfolio. We examine the properties of the optimal contract portfolio and provide a comparative statics analysis by changing the model parameters. As the strategic model encapsulates the business problem at the macroscopic level, the tactical model resolves lower level issues. It considers the time dynamics, the information flow and contracting flow. Using this model, we characterize a simple and easily implementable dynamic contract portfolio policy that would enable the company to dynamically rebalance its supply contract portfolio over time in anticipation of the future market conditions in each individual channel while satisfying the contractual obligations. The optimal policy is a state-dependent base-share contract portfolio policy characterized by a branded base-share level and an unbranded contract commitment combination, given as a function of the initial information state. Using real-world market data, we estimate the model parameters. We also apply an efficient modified policy iteration method to compute the optimal contract portfolio strategies and corresponding profit value. We present computational results in order to obtain insights into the structure of optimal policies, capture the value of the dynamic contract portfolio policy by comparing it with static policies, and illustrate the sensitivity of the optimal contract portfolio and corresponding profit value in terms of the different parameters. Considering the geographic dispersion of different market areas and the pipeline network together with the dynamic contract portfolio optimization problem, we formulate a forward-looking operational model, which could be used by gasoline suppliers for lower-level planning. Finally, we discuss the generalization of the framework to other problems and applications, as well as further research.

Complementing carbon prices with technology policies to keep climate targets within reach

NASA Astrophysics Data System (ADS)

Bertram, Christoph; Luderer, Gunnar; Pietzcker, Robert C.; Schmid, Eva; Kriegler, Elmar; Edenhofer, Ottmar

2015-03-01

Economic theory suggests that comprehensive carbon pricing is most efficient to reach ambitious climate targets, and previous studies indicated that the carbon price required for limiting global mean warming to 2 °C is between US$16 and US$73 per tonne of CO2 in 2015 (ref. ). Yet, a global implementation of such high carbon prices is unlikely to be politically feasible in the short term. Instead, most climate policies enacted so far are technology policies or fragmented and moderate carbon pricing schemes. This paper shows that ambitious climate targets can be kept within reach until 2030 despite a sub-optimal policy mix. With a state-of-the-art energy-economy model we quantify the interactions and unique effects of three major policy components: (1) a carbon price starting at US$7 per tonne of CO2 in 2015 to incentivize economy-wide mitigation, flanked by (2) support for low-carbon energy technologies to pave the way for future decarbonization, and (3) a moratorium on new coal-fired power plants to limit stranded assets. We find that such a mix limits the efficiency losses compared with the optimal policy, and at the same time lowers distributional impacts. Therefore, we argue that this instrument mix might be a politically more feasible alternative to the optimal policy based on a comprehensive carbon price alone.

[Cost-effectiveness of breast cancer screening policies in Mexico].

PubMed

Valencia-Mendoza, Atanacio; Sánchez-González, Gilberto; Bautista-Arredondo, Sergio; Torres-Mejía, Gabriela; Bertozzi, Stefano M

2009-01-01

Generate cost-effectiveness information to allow policy makers optimize breast cancer (BC) policy in Mexico. We constructed a Markov model that incorporates four interrelated processes of the disease: the natural history; detection using mammography; treatment; and other competing-causes mortality, according to which 13 different strategies were modeled. Strategies (starting age, % of coverage, frequency in years)= (48, 25, 2), (40, 50, 2) and (40, 50, 1) constituted the optimal method for expanding the BC program, yielding 75.3, 116.4 and 171.1 thousand pesos per life-year saved, respectively. The strategies included in the optimal method for expanding the program produce a cost per life-year saved of less than two times the GNP per capita and hence are cost-effective according to WHO Commission on Macroeconomics and Health criteria.

Off-Policy Actor-Critic Structure for Optimal Control of Unknown Systems With Disturbances.

PubMed

Song, Ruizhuo; Lewis, Frank L; Wei, Qinglai; Zhang, Huaguang

2016-05-01

An optimal control method is developed for unknown continuous-time systems with unknown disturbances in this paper. The integral reinforcement learning (IRL) algorithm is presented to obtain the iterative control. Off-policy learning is used to allow the dynamics to be completely unknown. Neural networks are used to construct critic and action networks. It is shown that if there are unknown disturbances, off-policy IRL may not converge or may be biased. For reducing the influence of unknown disturbances, a disturbances compensation controller is added. It is proven that the weight errors are uniformly ultimately bounded based on Lyapunov techniques. Convergence of the Hamiltonian function is also proven. The simulation study demonstrates the effectiveness of the proposed optimal control method for unknown systems with disturbances.

Efficiency, equity and feasibility of strategies to identify the poor: an application to premium exemptions under National Health Insurance in Ghana.

PubMed

Jehu-Appiah, Caroline; Aryeetey, Genevieve; Spaan, Ernst; Agyepong, Irene; Baltussen, Rob

2010-05-01

This paper outlines the potential strategies to identify the poor, and assesses their feasibility, efficiency and equity. Analyses are illustrated for the case of premium exemptions under National Health Insurance (NHI) in Ghana. A literature search in Medline search was performed to identify strategies to identify the poor. Models were developed including information on demography and poverty, and costs and errors of in- and exclusion of these strategies in two regions in Ghana. Proxy means testing (PMT), participatory welfare ranking (PWR), and geographic targeting (GT) are potentially useful strategies to identify the poor, and vary in terms of their efficiency, equity and feasibility. Costs to exempt one poor individual range between US$11.63 and US$66.67, and strategies may exclude up to 25% of the poor. Feasibility of strategies is dependent on their aptness in rural/urban settings, and administrative capacity to implement. A decision framework summarizes the above information to guide policy making. We recommend PMT as an optimal strategy in relative low poverty incidence urbanized settings, PWR as an optimal strategy in relative low poverty incidence rural settings, and GT as an optimal strategy in high incidence poverty settings. This paper holds important lessons not only for NHI in Ghana but also for other countries implementing exemption policies. Copyright (c) 2009 Elsevier Ireland Ltd. All rights reserved.

Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities

NASA Astrophysics Data System (ADS)

Sadeghi, Alireza; Sheikholeslami, Fatemeh; Giannakis, Georgios B.

2018-02-01

Small basestations (SBs) equipped with caching units have potential to handle the unprecedented demand growth in heterogeneous networks. Through low-rate, backhaul connections with the backbone, SBs can prefetch popular files during off-peak traffic hours, and service them to the edge at peak periods. To intelligently prefetch, each SB must learn what and when to cache, while taking into account SB memory limitations, the massive number of available contents, the unknown popularity profiles, as well as the space-time popularity dynamics of user file requests. In this work, local and global Markov processes model user requests, and a reinforcement learning (RL) framework is put forth for finding the optimal caching policy when the transition probabilities involved are unknown. Joint consideration of global and local popularity demands along with cache-refreshing costs allow for a simple, yet practical asynchronous caching approach. The novel RL-based caching relies on a Q-learning algorithm to implement the optimal policy in an online fashion, thus enabling the cache control unit at the SB to learn, track, and possibly adapt to the underlying dynamics. To endow the algorithm with scalability, a linear function approximation of the proposed Q-learning scheme is introduced, offering faster convergence as well as reduced complexity and memory requirements. Numerical tests corroborate the merits of the proposed approach in various realistic settings.

EGU2013 SM1.4/GI1.6 session: "Improving seismic networks performances: from site selection to data integration"

NASA Astrophysics Data System (ADS)

Pesaresi, D.; Busby, R.

2013-08-01

The number and quality of seismic stations and networks in Europe continually improves, nevertheless there is always scope to optimize their performance. In this session we welcomed contributions from all aspects of seismic network installation, operation and management. This includes site selection; equipment testing and installation; planning and implementing communication paths; policies for redundancy in data acquisition, processing and archiving; and integration of different datasets including GPS and OBS.

Optimal management strategies in variable environments: Stochastic optimal control methods

USGS Publications Warehouse

Williams, B.K.

1985-01-01

Dynamic optimization was used to investigate the optimal defoliation of salt desert shrubs in north-western Utah. Management was formulated in the context of optimal stochastic control theory, with objective functions composed of discounted or time-averaged biomass yields. Climatic variability and community patterns of salt desert shrublands make the application of stochastic optimal control both feasible and necessary. A primary production model was used to simulate shrub responses and harvest yields under a variety of climatic regimes and defoliation patterns. The simulation results then were used in an optimization model to determine optimal defoliation strategies. The latter model encodes an algorithm for finite state, finite action, infinite discrete time horizon Markov decision processes. Three questions were addressed: (i) What effect do changes in weather patterns have on optimal management strategies? (ii) What effect does the discounting of future returns have? (iii) How do the optimal strategies perform relative to certain fixed defoliation strategies? An analysis was performed for the three shrub species, winterfat (Ceratoides lanata), shadscale (Atriplex confertifolia) and big sagebrush (Artemisia tridentata). In general, the results indicate substantial differences among species in optimal control strategies, which are associated with differences in physiological and morphological characteristics. Optimal policies for big sagebrush varied less with variation in climate, reserve levels and discount rates than did either shadscale or winterfat. This was attributed primarily to the overwintering of photosynthetically active tissue and to metabolic activity early in the growing season. Optimal defoliation of shadscale and winterfat generally was more responsive to differences in plant vigor and climate, reflecting the sensitivity of these species to utilization and replenishment of carbohydrate reserves. Similarities could be seen in the influence of both the discount rate and the climatic patterns on optimal harvest strategics. In general, decreases in either the discount rate or in the frequency of favorable weather patterns lcd to a more conservative defoliation policy. This did not hold, however, for plants in states of low vigor. Optimal control for shadscale and winterfat tended to stabilize on a policy of heavy defoliation stress, followed by one or more seasons of rest. Big sagebrush required a policy of heavy summer defoliation when sufficient active shoot material is present at the beginning of the growing season. The comparison of fixed and optimal strategies indicated considerable improvement in defoliation yields when optimal strategies are followed. The superior performance was attributable to increased defoliation of plants in states of high vigor. Improvements were found for both discounted and undiscounted yields.

Coupled stochastic soil moisture simulation-optimization model of deficit irrigation

NASA Astrophysics Data System (ADS)

Alizadeh, Hosein; Mousavi, S. Jamshid

2013-07-01

This study presents an explicit stochastic optimization-simulation model of short-term deficit irrigation management for large-scale irrigation districts. The model which is a nonlinear nonconvex program with an economic objective function is built on an agrohydrological simulation component. The simulation component integrates (1) an explicit stochastic model of soil moisture dynamics of the crop-root zone considering interaction of stochastic rainfall and irrigation with shallow water table effects, (2) a conceptual root zone salt balance model, and 3) the FAO crop yield model. Particle Swarm Optimization algorithm, linked to the simulation component, solves the resulting nonconvex program with a significantly better computational performance compared to a Monte Carlo-based implicit stochastic optimization model. The model has been tested first by applying it in single-crop irrigation problems through which the effects of the severity of water deficit on the objective function (net benefit), root-zone water balance, and irrigation water needs have been assessed. Then, the model has been applied in Dasht-e-Abbas and Ein-khosh Fakkeh Irrigation Districts (DAID and EFID) of the Karkheh Basin in southwest of Iran. While the maximum net benefit has been obtained for a stress-avoidance (SA) irrigation policy, the highest water profitability has been resulted when only about 60% of the water used in the SA policy is applied. The DAID with respectively 33% of total cultivated area and 37% of total applied water has produced only 14% of the total net benefit due to low-valued crops and adverse soil and shallow water table conditions.

Optimal Navigation of Self-Propelled Colloids in Microstructured Mazes

NASA Astrophysics Data System (ADS)

Yang, Yuguang; Bevan, Michael

Controlling navigation of self-propelled microscopic `robots' subject to random Brownian motion in complex microstructured environments (e.g., porous media, tumor vasculature) is important to many emerging applications (e.g., enhanced oil recovery, drug delivery). In this work, we design an optimal feedback policy to navigate an active self-propelled colloidal rod in complex mazes with various obstacle types. Actuation of the rods is modelled based on a light-controlled osmotic flow mechanism, which produces different propulsion velocities along the rod's long axis. Actuator-parameterized Langevin equations, with soft rod-obstacle repulsive interactions, are developed to describe the system dynamics. A Markov decision process (MDP) framework is used for optimal policy calculations with design goals of colloidal rods reaching target end points in minimum time. Simulations show that optimal MDP-based policies are able to control rod trajectories to reach target regions order-of-magnitudes faster than uncontrolled rods, which diverges as maze complexity increases. An efficient multi-graph based implementation for MDP is also presented, which scales linearly with the maze dimension.

Universal approximators for multi-objective direct policy search in water reservoir management problems: a comparative analysis

NASA Astrophysics Data System (ADS)

Giuliani, Matteo; Mason, Emanuele; Castelletti, Andrea; Pianosi, Francesca

2014-05-01

The optimal operation of water resources systems is a wide and challenging problem due to non-linearities in the model and the objectives, high dimensional state-control space, and strong uncertainties in the hydroclimatic regimes. The application of classical optimization techniques (e.g., SDP, Q-learning, gradient descent-based algorithms) is strongly limited by the dimensionality of the system and by the presence of multiple, conflicting objectives. This study presents a novel approach which combines Direct Policy Search (DPS) and Multi-Objective Evolutionary Algorithms (MOEAs) to solve high-dimensional state and control space problems involving multiple objectives. DPS, also known as parameterization-simulation-optimization in the water resources literature, is a simulation-based approach where the reservoir operating policy is first parameterized within a given family of functions and, then, the parameters optimized with respect to the objectives of the management problem. The selection of a suitable class of functions to which the operating policy belong to is a key step, as it might restrict the search for the optimal policy to a subspace of the decision space that does not include the optimal solution. In the water reservoir literature, a number of classes have been proposed. However, many of these rules are based largely on empirical or experimental successes and they were designed mostly via simulation and for single-purpose reservoirs. In a multi-objective context similar rules can not easily inferred from the experience and the use of universal function approximators is generally preferred. In this work, we comparatively analyze two among the most common universal approximators: artificial neural networks (ANN) and radial basis functions (RBF) under different problem settings to estimate their scalability and flexibility in dealing with more and more complex problems. The multi-purpose HoaBinh water reservoir in Vietnam, accounting for hydropower production and flood control, is used as a case study. Preliminary results show that the RBF policy parametrization is more effective than the ANN one. In particular, the approximated Pareto front obtained with RBF control policies successfully explores the full tradeoff space between the two conflicting objectives, while most of the ANN solutions results to be Pareto-dominated by the RBF ones.

A Comparison Study of Stochastic- and Guaranteed- Service Approaches on Safety Stock Optimization for Multi Serial Systems

NASA Astrophysics Data System (ADS)

Li, Peng; Wu, Di

2018-01-01

Two competing approaches have been developed over the years for multi-echelon inventory system optimization, stochastic-service approach (SSA) and guaranteed-service approach (GSA). Although they solve the same inventory policy optimization problem in their core, they make different assumptions with regard to the role of safety stock. This paper provides a detailed comparison of the two approaches by considering operating flexibility costs in the optimization of (R, Q) policies for a continuous review serial inventory system. The results indicate the GSA model is more efficiency in solving the complicated inventory problem in terms of the computation time, and the cost difference of the two approaches is quite small.

Optimal tracking control for a class of nonlinear discrete-time systems with time delays based on heuristic dynamic programming.

PubMed

Zhang, Huaguang; Song, Ruizhuo; Wei, Qinglai; Zhang, Tieyan

2011-12-01

In this paper, a novel heuristic dynamic programming (HDP) iteration algorithm is proposed to solve the optimal tracking control problem for a class of nonlinear discrete-time systems with time delays. The novel algorithm contains state updating, control policy iteration, and performance index iteration. To get the optimal states, the states are also updated. Furthermore, the "backward iteration" is applied to state updating. Two neural networks are used to approximate the performance index function and compute the optimal control policy for facilitating the implementation of HDP iteration algorithm. At last, we present two examples to demonstrate the effectiveness of the proposed HDP iteration algorithm.

Optimal policies for simultaneous energy consumption and ancillary service provision for flexible loads under stochastic prices and no capacity reservation constraint

NASA Astrophysics Data System (ADS)

Kefayati, Mahdi; Baldick, Ross

2015-07-01

Flexible loads, i.e. the loads whose power trajectory is not bound to a specific one, constitute a sizable portion of current and future electric demand. This flexibility can be used to improve the performance of the grid, should the right incentives be in place. In this paper, we consider the optimal decision making problem faced by a flexible load, demanding a certain amount of energy over its availability period, subject to rate constraints. The load is also capable of providing ancillary services (AS) by decreasing or increasing its consumption in response to signals from the independent system operator (ISO). Under arbitrarily distributed and correlated Markovian energy and AS prices, we obtain the optimal policy for minimising expected total cost, which includes cost of energy and benefits from AS provision, assuming no capacity reservation requirement for AS provision. We also prove that the optimal policy has a multi-threshold form and can be computed, stored and operated efficiently. We further study the effectiveness of our proposed optimal policy and its impact on the grid. We show that, while optimal simultaneous consumption and AS provision under real-time stochastic prices are achievable with acceptable computational burden, the impact of adopting such real-time pricing schemes on the network might not be as good as suggested by the majority of the existing literature. In fact, we show that such price responsive loads are likely to induce peak-to-average ratios much more than what is observed in the current distribution networks and adversely affect the grid.

Dynamic mobility applications policy analysis : policy and institutional issues for freight advanced traveler information systems (FRATIS).

DOT National Transportation Integrated Search

2014-10-30

This report documents policy considerations for the Freight Advanced Traveler Information System, or FRATIS. FRATIS applications provide freight-specific route guidance and optimize drayage operations so that load movements are coordinated between fr...

An Extended EPQ-Based Problem with a Discontinuous Delivery Policy, Scrap Rate, and Random Breakdown

PubMed Central

Song, Ming-Syuan; Chen, Hsin-Mei; Chiu, Yuan-Shyi P.

2015-01-01

In real supply chain environments, the discontinuous multidelivery policy is often used when finished products need to be transported to retailers or customers outside the production units. To address this real-life production-shipment situation, this study extends recent work using an economic production quantity- (EPQ-) based inventory model with a continuous inventory issuing policy, defective items, and machine breakdown by incorporating a multiple delivery policy into the model to replace the continuous policy and investigates the effect on the optimal run time decision for this specific EPQ model. Next, we further expand the scope of the problem to combine the retailer's stock holding cost into our study. This enhanced EPQ-based model can be used to reflect the situation found in contemporary manufacturing firms in which finished products are delivered to the producer's own retail stores and stocked there for sale. A second model is developed and studied. With the help of mathematical modeling and optimization techniques, the optimal run times that minimize the expected total system costs comprising costs incurred in production units, transportation, and retail stores are derived, for both models. Numerical examples are provided to demonstrate the applicability of our research results. PMID:25821853

An extended EPQ-based problem with a discontinuous delivery policy, scrap rate, and random breakdown.

PubMed

Chiu, Singa Wang; Lin, Hong-Dar; Song, Ming-Syuan; Chen, Hsin-Mei; Chiu, Yuan-Shyi P

2015-01-01

In real supply chain environments, the discontinuous multidelivery policy is often used when finished products need to be transported to retailers or customers outside the production units. To address this real-life production-shipment situation, this study extends recent work using an economic production quantity- (EPQ-) based inventory model with a continuous inventory issuing policy, defective items, and machine breakdown by incorporating a multiple delivery policy into the model to replace the continuous policy and investigates the effect on the optimal run time decision for this specific EPQ model. Next, we further expand the scope of the problem to combine the retailer's stock holding cost into our study. This enhanced EPQ-based model can be used to reflect the situation found in contemporary manufacturing firms in which finished products are delivered to the producer's own retail stores and stocked there for sale. A second model is developed and studied. With the help of mathematical modeling and optimization techniques, the optimal run times that minimize the expected total system costs comprising costs incurred in production units, transportation, and retail stores are derived, for both models. Numerical examples are provided to demonstrate the applicability of our research results.

Endogenous fertility, Ricardian equivalence, and debt management policy.

PubMed

Lapan, H E; Enders, W

1990-03-01

This paper develops a model in which dynastic families optimally determine fertility. Government debt represents a tax on future generations and on childbearing; the Ricardian Equivalence Hypothesis does not hold. Debt is welfare reducing in that it distorts the fertility decision. An increase in government debt induces a decline in fertility and an increase in the steady state capital/labor ratio. If a government inherits an existing stock of debt, the 1st-best policy is to eliminate the debt immediately. In other situations the optimal debt management policy will not, in general, entail a total elimination of the debt.

Postaudit of optimal conjunctive use policies

USGS Publications Warehouse

Nishikawa, Tracy; Martin, Peter; ,

1998-01-01

A simulation-optimization model was developed for the optimal management of the city of Santa Barbara's water resources during a drought; however, this model addressed only groundwater flow and not the advective-dispersive, density-dependent transport of seawater. Zero-m freshwater head constraints at the coastal boundary were used as surrogates for the control of seawater intrusion. In this study, the strategies derived from the simulation-optimization model using two surface water supply scenarios are evaluated using a two-dimensional, density-dependent groundwater flow and transport model. Comparisons of simulated chloride mass fractions are made between maintaining the actual pumping policies of the 1987-91 drought and implementing the optimal pumping strategies for each scenario. The results indicate that using 0-m freshwater head constraints allowed no more seawater intrusion than under actual 1987-91 drought conditions and that the simulation-optimization model yields least-cost strategies that deliver more water than under actual drought conditions while controlling seawater intrusion.

Management of a stage-structured insect pest: an application of approximate optimization.

PubMed

Hackett, Sean C; Bonsall, Michael B

2018-06-01

Ecological decision problems frequently require the optimization of a sequence of actions over time where actions may have both immediate and downstream effects. Dynamic programming can solve such problems only if the dimensionality is sufficiently low. Approximate dynamic programming (ADP) provides a suite of methods applicable to problems of arbitrary complexity at the expense of guaranteed optimality. The most easily generalized method is the look-ahead policy: a brute-force algorithm that identifies reasonable actions by constructing and solving a series of temporally truncated approximations of the full problem over a defined planning horizon. We develop and apply this approach to a pest management problem inspired by the Mediterranean fruit fly, Ceratitis capitata. The model aims to minimize the cumulative costs of management actions and medfly-induced losses over a single 16-week season. The medfly population is stage-structured and grows continuously while management decisions are made at discrete, weekly intervals. For each week, the model chooses between inaction, insecticide application, or one of six sterile insect release ratios. Look-ahead policy performance is evaluated over a range of planning horizons, two levels of crop susceptibility to medfly and three levels of pesticide persistence. In all cases, the actions proposed by the look-ahead policy are contrasted to those of a myopic policy that minimizes costs over only the current week. We find that look-ahead policies always out-performed a myopic policy and decision quality is sensitive to the temporal distribution of costs relative to the planning horizon: it is beneficial to extend the planning horizon when it excludes pertinent costs. However, longer planning horizons may reduce decision quality when major costs are resolved imminently. ADP methods such as the look-ahead-policy-based approach developed here render questions intractable to dynamic programming amenable to inference but should be applied carefully as their flexibility comes at the expense of guaranteed optimality. However, given the complexity of many ecological management problems, the capacity to propose a strategy that is "good enough" using a more representative problem formulation may be preferable to an optimal strategy derived from a simplified model. © 2018 by the Ecological Society of America.

A Low Power IoT Sensor Node Architecture for Waste Management Within Smart Cities Context.

PubMed

Cerchecci, Matteo; Luti, Francesco; Mecocci, Alessandro; Parrino, Stefano; Peruzzi, Giacomo; Pozzebon, Alessandro

2018-04-21

This paper focuses on the realization of an Internet of Things (IoT) architecture to optimize waste management in the context of Smart Cities. In particular, a novel typology of sensor node based on the use of low cost and low power components is described. This node is provided with a single-chip microcontroller, a sensor able to measure the filling level of trash bins using ultrasounds and a data transmission module based on the LoRa LPWAN (Low Power Wide Area Network) technology. Together with the node, a minimal network architecture was designed, based on a LoRa gateway, with the purpose of testing the IoT node performances. Especially, the paper analyzes in detail the node architecture, focusing on the energy saving technologies and policies, with the purpose of extending the batteries lifetime by reducing power consumption, through hardware and software optimization. Tests on sensor and radio module effectiveness are also presented.

A Low Power IoT Sensor Node Architecture for Waste Management Within Smart Cities Context

PubMed Central

Cerchecci, Matteo; Luti, Francesco; Mecocci, Alessandro; Parrino, Stefano; Peruzzi, Giacomo

2018-01-01

This paper focuses on the realization of an Internet of Things (IoT) architecture to optimize waste management in the context of Smart Cities. In particular, a novel typology of sensor node based on the use of low cost and low power components is described. This node is provided with a single-chip microcontroller, a sensor able to measure the filling level of trash bins using ultrasounds and a data transmission module based on the LoRa LPWAN (Low Power Wide Area Network) technology. Together with the node, a minimal network architecture was designed, based on a LoRa gateway, with the purpose of testing the IoT node performances. Especially, the paper analyzes in detail the node architecture, focusing on the energy saving technologies and policies, with the purpose of extending the batteries lifetime by reducing power consumption, through hardware and software optimization. Tests on sensor and radio module effectiveness are also presented. PMID:29690552

Deadlock-free genetic scheduling algorithm for automated manufacturing systems based on deadlock control policy.

PubMed

Xing, KeYi; Han, LiBin; Zhou, MengChu; Wang, Feng

2012-06-01

Deadlock-free control and scheduling are vital for optimizing the performance of automated manufacturing systems (AMSs) with shared resources and route flexibility. Based on the Petri net models of AMSs, this paper embeds the optimal deadlock avoidance policy into the genetic algorithm and develops a novel deadlock-free genetic scheduling algorithm for AMSs. A possible solution of the scheduling problem is coded as a chromosome representation that is a permutation with repetition of parts. By using the one-step look-ahead method in the optimal deadlock control policy, the feasibility of a chromosome is checked, and infeasible chromosomes are amended into feasible ones, which can be easily decoded into a feasible deadlock-free schedule. The chromosome representation and polynomial complexity of checking and amending procedures together support the cooperative aspect of genetic search for scheduling problems strongly.

Consumers' views of direct-to-consumer genetic information.

PubMed

McBride, Colleen M; Wade, Christopher H; Kaphingst, Kimberly A

2010-01-01

In this report, we describe the evolution and types of genetic information provided directly to consumers, discuss potential advantages and disadvantages of these products, and review research evaluating consumer responses to direct-to-consumer (DTC) genetic testing. The available evidence to date has focused on predictive tests and does not suggest that individuals, health care providers, or health care systems have been harmed by a DTC provision of genetic information. An understanding of consumer responses to susceptibility tests has lagged behind. The Multiplex Initiative is presented as a case study of research to understand consumers' responses to DTC susceptibility tests. Three priority areas are recommended for accelerated research activities to inform public policy regarding DTC genetic information: (a) exploring consumer's long-term responses to DTC genetic testing on a comprehensive set of outcomes, (b) evaluating optimal services to support decision making about genetic testing, and (c) evaluating best practices in promoting genetic competencies among health providers.

Essays on Causal Inference for Public Policy

ERIC Educational Resources Information Center

Zajonc, Tristan

2012-01-01

Effective policymaking requires understanding the causal effects of competing proposals. Relevant causal quantities include proposals' expected effect on different groups of recipients, the impact of policies over time, the potential trade-offs between competing objectives, and, ultimately, the optimal policy. This dissertation studies causal…

Dave Bielen | NREL

Science.gov Websites

Dave Bielen Photo of Dave Bielen Dave Bielen Energy and Environmental Policy Analyst David.Bielen Energy Analysis Center. Areas of Expertise Environmental policy design Dynamic programming Time series energy policy GHG emissions mitigation in the electricity and transportation sectors Optimal control of

Environmental tipping points significantly affect the cost-benefit assessment of climate policies.

PubMed

Cai, Yongyang; Judd, Kenneth L; Lenton, Timothy M; Lontzek, Thomas S; Narita, Daiju

2015-04-14

Most current cost-benefit analyses of climate change policies suggest an optimal global climate policy that is significantly less stringent than the level required to meet the internationally agreed 2 °C target. This is partly because the sum of estimated economic damage of climate change across various sectors, such as energy use and changes in agricultural production, results in only a small economic loss or even a small economic gain in the gross world product under predicted levels of climate change. However, those cost-benefit analyses rarely take account of environmental tipping points leading to abrupt and irreversible impacts on market and nonmarket goods and services, including those provided by the climate and by ecosystems. Here we show that including environmental tipping point impacts in a stochastic dynamic integrated assessment model profoundly alters cost-benefit assessment of global climate policy. The risk of a tipping point, even if it only has nonmarket impacts, could substantially increase the present optimal carbon tax. For example, a risk of only 5% loss in nonmarket goods that occurs with a 5% annual probability at 4 °C increase of the global surface temperature causes an immediate two-thirds increase in optimal carbon tax. If the tipping point also has a 5% impact on market goods, the optimal carbon tax increases by more than a factor of 3. Hence existing cost-benefit assessments of global climate policy may be significantly underestimating the needs for controlling climate change.

Environmental tipping points significantly affect the cost−benefit assessment of climate policies

PubMed Central

Cai, Yongyang; Judd, Kenneth L.; Lenton, Timothy M.; Lontzek, Thomas S.; Narita, Daiju

2015-01-01

Most current cost−benefit analyses of climate change policies suggest an optimal global climate policy that is significantly less stringent than the level required to meet the internationally agreed 2 °C target. This is partly because the sum of estimated economic damage of climate change across various sectors, such as energy use and changes in agricultural production, results in only a small economic loss or even a small economic gain in the gross world product under predicted levels of climate change. However, those cost−benefit analyses rarely take account of environmental tipping points leading to abrupt and irreversible impacts on market and nonmarket goods and services, including those provided by the climate and by ecosystems. Here we show that including environmental tipping point impacts in a stochastic dynamic integrated assessment model profoundly alters cost−benefit assessment of global climate policy. The risk of a tipping point, even if it only has nonmarket impacts, could substantially increase the present optimal carbon tax. For example, a risk of only 5% loss in nonmarket goods that occurs with a 5% annual probability at 4 °C increase of the global surface temperature causes an immediate two-thirds increase in optimal carbon tax. If the tipping point also has a 5% impact on market goods, the optimal carbon tax increases by more than a factor of 3. Hence existing cost−benefit assessments of global climate policy may be significantly underestimating the needs for controlling climate change. PMID:25825719

Kernel-based least squares policy iteration for reinforcement learning.

PubMed

Xu, Xin; Hu, Dewen; Lu, Xicheng

2007-07-01

In this paper, we present a kernel-based least squares policy iteration (KLSPI) algorithm for reinforcement learning (RL) in large or continuous state spaces, which can be used to realize adaptive feedback control of uncertain dynamic systems. By using KLSPI, near-optimal control policies can be obtained without much a priori knowledge on dynamic models of control plants. In KLSPI, Mercer kernels are used in the policy evaluation of a policy iteration process, where a new kernel-based least squares temporal-difference algorithm called KLSTD-Q is proposed for efficient policy evaluation. To keep the sparsity and improve the generalization ability of KLSTD-Q solutions, a kernel sparsification procedure based on approximate linear dependency (ALD) is performed. Compared to the previous works on approximate RL methods, KLSPI makes two progresses to eliminate the main difficulties of existing results. One is the better convergence and (near) optimality guarantee by using the KLSTD-Q algorithm for policy evaluation with high precision. The other is the automatic feature selection using the ALD-based kernel sparsification. Therefore, the KLSPI algorithm provides a general RL method with generalization performance and convergence guarantee for large-scale Markov decision problems (MDPs). Experimental results on a typical RL task for a stochastic chain problem demonstrate that KLSPI can consistently achieve better learning efficiency and policy quality than the previous least squares policy iteration (LSPI) algorithm. Furthermore, the KLSPI method was also evaluated on two nonlinear feedback control problems, including a ship heading control problem and the swing up control of a double-link underactuated pendulum called acrobot. Simulation results illustrate that the proposed method can optimize controller performance using little a priori information of uncertain dynamic systems. It is also demonstrated that KLSPI can be applied to online learning control by incorporating an initial controller to ensure online performance.

A Sarsa(λ)-based control model for real-time traffic light coordination.

PubMed

Zhou, Xiaoke; Zhu, Fei; Liu, Quan; Fu, Yuchen; Huang, Wei

2014-01-01

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control.

Optimal Policy of Cross-Layer Design for Channel Access and Transmission Rate Adaptation in Cognitive Radio Networks

NASA Astrophysics Data System (ADS)

He, Hao; Wang, Jun; Zhu, Jiang; Li, Shaoqian

2010-12-01

In this paper, we investigate the cross-layer design of joint channel access and transmission rate adaptation in CR networks with multiple channels for both centralized and decentralized cases. Our target is to maximize the throughput of CR network under transmission power constraint by taking spectrum sensing errors into account. In centralized case, this problem is formulated as a special constrained Markov decision process (CMDP), which can be solved by standard linear programming (LP) method. As the complexity of finding the optimal policy by LP increases exponentially with the size of action space and state space, we further apply action set reduction and state aggregation to reduce the complexity without loss of optimality. Meanwhile, for the convenience of implementation, we also consider the pure policy design and analyze the corresponding characteristics. In decentralized case, where only local information is available and there is no coordination among the CR users, we prove the existence of the constrained Nash equilibrium and obtain the optimal decentralized policy. Finally, in the case that the traffic load parameters of the licensed users are unknown for the CR users, we propose two methods to estimate the parameters for two different cases. Numerical results validate the theoretic analysis.

Finding optimal vaccination strategies under parameter uncertainty using stochastic programming.

PubMed

Tanner, Matthew W; Sattenspiel, Lisa; Ntaimo, Lewis

2008-10-01

We present a stochastic programming framework for finding the optimal vaccination policy for controlling infectious disease epidemics under parameter uncertainty. Stochastic programming is a popular framework for including the effects of parameter uncertainty in a mathematical optimization model. The problem is initially formulated to find the minimum cost vaccination policy under a chance-constraint. The chance-constraint requires that the probability that R(*)

Assessment of regional management strategies for controlling seawater intrusion

USGS Publications Warehouse

Reichard, E.G.; Johnson, T.A.

2005-01-01

Simulation-optimization methods, applied with adequate sensitivity tests, can provide useful quantitative guidance for controlling seawater intrusion. This is demonstrated in an application to the West Coast Basin of coastal Los Angeles that considers two management options for improving hydraulic control of seawater intrusion: increased injection into barrier wells and in lieu delivery of surface water to replace current pumpage. For the base-case optimization analysis, assuming constant groundwater demand, in lieu delivery was determined to be most cost effective. Reduced-cost information from the optimization provided guidance for prioritizing locations for in lieu delivery. Model sensitivity to a suite of hydrologic, economic, and policy factors was tested. Raising the imposed average water-level constraint at the hydraulic-control locations resulted in nonlinear increases in cost. Systematic varying of the relative costs of injection and in lieu water yielded a trade-off curve between relative costs and injection/in lieu amounts. Changing the assumed future scenario to one of increasing pumpage in the adjacent Central Basin caused a small increase in the computed costs of seawater intrusion control. Changing the assumed boundary condition representing interaction with an adjacent basin did not affect the optimization results. Reducing the assumed hydraulic conductivity of the main productive aquifer resulted in a large increase in the model-computed cost. Journal of Water Resources Planning and Management ?? ASCE.

Dopaminergic Balance between Reward Maximization and Policy Complexity

PubMed Central

Parush, Naama; Tishby, Naftali; Bergman, Hagai

2011-01-01

Previous reinforcement-learning models of the basal ganglia network have highlighted the role of dopamine in encoding the mismatch between prediction and reality. Far less attention has been paid to the computational goals and algorithms of the main-axis (actor). Here, we construct a top-down model of the basal ganglia with emphasis on the role of dopamine as both a reinforcement learning signal and as a pseudo-temperature signal controlling the general level of basal ganglia excitability and motor vigilance of the acting agent. We argue that the basal ganglia endow the thalamic-cortical networks with the optimal dynamic tradeoff between two constraints: minimizing the policy complexity (cost) and maximizing the expected future reward (gain). We show that this multi-dimensional optimization processes results in an experience-modulated version of the softmax behavioral policy. Thus, as in classical softmax behavioral policies, probability of actions are selected according to their estimated values and the pseudo-temperature, but in addition also vary according to the frequency of previous choices of these actions. We conclude that the computational goal of the basal ganglia is not to maximize cumulative (positive and negative) reward. Rather, the basal ganglia aim at optimization of independent gain and cost functions. Unlike previously suggested single-variable maximization processes, this multi-dimensional optimization process leads naturally to a softmax-like behavioral policy. We suggest that beyond its role in the modulation of the efficacy of the cortico-striatal synapses, dopamine directly affects striatal excitability and thus provides a pseudo-temperature signal that modulates the tradeoff between gain and cost. The resulting experience and dopamine modulated softmax policy can then serve as a theoretical framework to account for the broad range of behaviors and clinical states governed by the basal ganglia and dopamine systems. PMID:21603228

Optimal policy for profit maximising in an EOQ model under non-linear holding cost and stock-dependent demand rate

NASA Astrophysics Data System (ADS)

Pando, V.; García-Laguna, J.; San-José, L. A.

2012-11-01

In this article, we integrate a non-linear holding cost with a stock-dependent demand rate in a maximising profit per unit time model, extending several inventory models studied by other authors. After giving the mathematical formulation of the inventory system, we prove the existence and uniqueness of the optimal policy. Relying on this result, we can obtain the optimal solution using different numerical algorithms. Moreover, we provide a necessary and sufficient condition to determine whether a system is profitable, and we establish a rule to check when a given order quantity is the optimal lot size of the inventory model. The results are illustrated through numerical examples and the sensitivity of the optimal solution with respect to changes in some values of the parameters is assessed.

(n, N) type maintenance policy for multi-component systems with failure interactions

NASA Astrophysics Data System (ADS)

Zhang, Zhuoqi; Wu, Su; Li, Binfeng; Lee, Seungchul

2015-04-01

This paper studies maintenance policies for multi-component systems in which failure interactions and opportunistic maintenance (OM) involve. This maintenance problem can be formulated as a Markov decision process (MDP). However, since an action set and state space in MDP exponentially expand as the number of components increase, traditional approaches are computationally intractable. To deal with curse of dimensionality, we decompose such a multi-component system into mutually influential single-component systems. Each single-component system is formulated as an MDP with the objective of minimising its long-run average maintenance cost. Under some reasonable assumptions, we prove the existence of the optimal (n, N) type policy for a single-component system. An algorithm to obtain the optimal (n, N) type policy is also proposed. Based on the proposed algorithm, we develop an iterative approximation algorithm to obtain an acceptable maintenance policy for a multi-component system. Numerical examples find that failure interactions and OM pose significant effects on a maintenance policy.

The ethical implications and religious significance of organ transplantation payment systems.

PubMed

Smith, Hunter Jackson

2016-03-01

One of the more polarizing policies proposed to alleviate the organ shortage is financial payment of donors in return for organs. A priori and empirical investigation concludes that such systems are ethically inadequate. A new methodological approach towards policy formation and implementation is proposed which places ethical concerns at its core. From a hypothetical secular origin, the optimal ethical policy structure concerning organ donation is derived. However, when applied universally, it does not yield ideal results for every culture and society due to region-specific variation. Since religion holds significant influence in the organ donation debate, three religions-Catholicism, Islam, and Shinto-were examined in order to illustrate this variation. Although secular ethical concerns should rest at the core of policy construction, certain region-specific contexts require cultural and religious competence and necessitate the adjustment of the optimal template policy accordingly to yield the best moral and practical results.

Assessing groundwater policy with coupled economic-groundwater hydrologic modeling

NASA Astrophysics Data System (ADS)

Mulligan, Kevin B.; Brown, Casey; Yang, Yi-Chen E.; Ahlfeld, David P.

2014-03-01

This study explores groundwater management policies and the effect of modeling assumptions on the projected performance of those policies. The study compares an optimal economic allocation for groundwater use subject to streamflow constraints, achieved by a central planner with perfect foresight, with a uniform tax on groundwater use and a uniform quota on groundwater use. The policies are compared with two modeling approaches, the Optimal Control Model (OCM) and the Multi-Agent System Simulation (MASS). The economic decision models are coupled with a physically based representation of the aquifer using a calibrated MODFLOW groundwater model. The results indicate that uniformly applied policies perform poorly when simulated with more realistic, heterogeneous, myopic, and self-interested agents. In particular, the effects of the physical heterogeneity of the basin and the agents undercut the perceived benefits of policy instruments assessed with simple, single-cell groundwater modeling. This study demonstrates the results of coupling realistic hydrogeology and human behavior models to assess groundwater management policies. The Republican River Basin, which overlies a portion of the Ogallala aquifer in the High Plains of the United States, is used as a case study for this analysis.

Online Solution of Two-Player Zero-Sum Games for Continuous-Time Nonlinear Systems With Completely Unknown Dynamics.

PubMed

Fu, Yue; Chai, Tianyou

2016-12-01

Regarding two-player zero-sum games of continuous-time nonlinear systems with completely unknown dynamics, this paper presents an online adaptive algorithm for learning the Nash equilibrium solution, i.e., the optimal policy pair. First, for known systems, the simultaneous policy updating algorithm (SPUA) is reviewed. A new analytical method to prove the convergence is presented. Then, based on the SPUA, without using a priori knowledge of any system dynamics, an online algorithm is proposed to simultaneously learn in real time either the minimal nonnegative solution of the Hamilton-Jacobi-Isaacs (HJI) equation or the generalized algebraic Riccati equation for linear systems as a special case, along with the optimal policy pair. The approximate solution to the HJI equation and the admissible policy pair is reexpressed by the approximation theorem. The unknown constants or weights of each are identified simultaneously by resorting to the recursive least square method. The convergence of the online algorithm to the optimal solutions is provided. A practical online algorithm is also developed. Simulation results illustrate the effectiveness of the proposed method.

Optimal birth control of age-dependent competitive species III. Overtaking problem

NASA Astrophysics Data System (ADS)

He, Ze-Rong; Cheng, Ji-Shu; Zhang, Chun-Guo

2008-01-01

A study is made of an overtaking optimal problem for a population system consisting of two competing species, which is controlled by fertilities. The existence of optimal policy is proved and a maximum principle is carefully derived under less restrictive conditions. Weak and strong turnpike properties of optimal trajectories are established.

Optimal ordering and production policy for a recoverable item inventory system with learning effect

NASA Astrophysics Data System (ADS)

Tsai, Deng-Maw

2012-02-01

This article presents two models for determining an optimal integrated economic order quantity and economic production quantity policy in a recoverable manufacturing environment. The models assume that the unit production time of the recovery process decreases with the increase in total units produced as a result of learning. A fixed proportion of used products are collected from customers and then recovered for reuse. The recovered products are assumed to be in good condition and acceptable to customers. Constant demand can be satisfied by utilising both newly purchased products and recovered products. The aim of this article is to show how to minimise total inventory-related cost. The total cost functions of the two models are derived and two simple search procedures are proposed to determine optimal policy parameters. Numerical examples are provided to illustrate the proposed models. In addition, sensitivity analyses have also been performed and are discussed.

Urban water infrastructure optimization to reduce environmental impacts and costs.

PubMed

Lim, Seong-Rin; Suh, Sangwon; Kim, Jung-Hoon; Park, Hung Suck

2010-01-01

Urban water planning and policy have been focusing on environmentally benign and economically viable water management. The objective of this study is to develop a mathematical model to integrate and optimize urban water infrastructures for supply-side planning and policy: freshwater resources and treated wastewater are allocated to various water demand categories in order to reduce contaminants in the influents supplied for drinking water, and to reduce consumption of the water resources imported from the regions beyond a city boundary. A case study is performed to validate the proposed model. An optimal urban water system of a metropolitan city is calculated on the basis of the model and compared to the existing water system. The integration and optimization decrease (i) average concentrations of the influents supplied for drinking water, which can improve human health and hygiene; (ii) total consumption of water resources, as well as electricity, reducing overall environmental impacts; (iii) life cycle cost; and (iv) water resource dependency on other regions, improving regional water security. This model contributes to sustainable urban water planning and policy. 2009 Elsevier Ltd. All rights reserved.

Short-term Operation of Multi-purpose Reservoir using Model Predictive Control

NASA Astrophysics Data System (ADS)

Uysal, Gokcen; Schwanenberg, Dirk; Alvarado Montero, Rodolfo; Sensoy, Aynur; Arda Sorman, Ali

2017-04-01

Operation of water structures especially with conflicting water supply and flood mitigation objectives is under more stress attributed to growing water demand and changing hydro-climatic conditions. Model Predictive Control (MPC) based optimal control solutions has been successfully applied to different water resources applications. In this study, Feedback Control (FBC) and MPC get combined and an improved joint optimization-simulation operating scheme is proposed. Water supply and flood control objectives are fulfilled by incorporating the long term water supply objectives into a time-dependent variable guide curve policy whereas the extreme floods are attenuated by means of short-term optimization based on MPC. A final experiment is carried out to assess the lead time performance and reliability of forecasts in a hindcasting experiment with imperfect, perturbed forecasts. The framework is tested in Yuvacık Dam reservoir where the main water supply reservoir of Kocaeli City in the northwestern part of Turkey (the Marmara region) and it requires a challenging gate operation due to restricted downstream flow conditions.

Policy Iteration for $H_\\infty $ Optimal Control of Polynomial Nonlinear Systems via Sum of Squares Programming.

PubMed

Zhu, Yuanheng; Zhao, Dongbin; Yang, Xiong; Zhang, Qichao

2018-02-01

Sum of squares (SOS) polynomials have provided a computationally tractable way to deal with inequality constraints appearing in many control problems. It can also act as an approximator in the framework of adaptive dynamic programming. In this paper, an approximate solution to the optimal control of polynomial nonlinear systems is proposed. Under a given attenuation coefficient, the Hamilton-Jacobi-Isaacs equation is relaxed to an optimization problem with a set of inequalities. After applying the policy iteration technique and constraining inequalities to SOS, the optimization problem is divided into a sequence of feasible semidefinite programming problems. With the converged solution, the attenuation coefficient is further minimized to a lower value. After iterations, approximate solutions to the smallest -gain and the associated optimal controller are obtained. Four examples are employed to verify the effectiveness of the proposed algorithm.

Adaptive dynamic programming for finite-horizon optimal control of discrete-time nonlinear systems with ε-error bound.

PubMed

Wang, Fei-Yue; Jin, Ning; Liu, Derong; Wei, Qinglai

2011-01-01

In this paper, we study the finite-horizon optimal control problem for discrete-time nonlinear systems using the adaptive dynamic programming (ADP) approach. The idea is to use an iterative ADP algorithm to obtain the optimal control law which makes the performance index function close to the greatest lower bound of all performance indices within an ε-error bound. The optimal number of control steps can also be obtained by the proposed ADP algorithms. A convergence analysis of the proposed ADP algorithms in terms of performance index function and control policy is made. In order to facilitate the implementation of the iterative ADP algorithms, neural networks are used for approximating the performance index function, computing the optimal control policy, and modeling the nonlinear system. Finally, two simulation examples are employed to illustrate the applicability of the proposed method.

Multiobjective optimization for Groundwater Nitrate Pollution Control. Application to El Salobral-Los Llanos aquifer (Spain).

NASA Astrophysics Data System (ADS)

Llopis-Albert, C.; Peña-Haro, S.; Pulido-Velazquez, M.; Molina, J.

2012-04-01

Water quality management is complex due to the inter-relations between socio-political, environmental and economic constraints and objectives. In order to choose an appropriate policy to reduce nitrate pollution in groundwater it is necessary to consider different objectives, often in conflict. In this paper, a hydro-economic modeling framework, based on a non-linear optimization(CONOPT) technique, which embeds simulation of groundwater mass transport through concentration response matrices, is used to study optimal policies for groundwater nitrate pollution control under different objectives and constraints. Three objectives were considered: recovery time (for meeting the environmental standards, as required by the EU Water Framework Directive and Groundwater Directive), maximum nitrate concentration in groundwater, and net benefits in agriculture. Another criterion was added: the reliability of meeting the nitrate concentration standards. The approach allows deriving the trade-offs between the reliability of meeting the standard, the net benefits from agricultural production and the recovery time. Two different policies were considered: spatially distributed fertilizer standards or quotas (obtained through multi-objective optimization) and fertilizer prices. The multi-objective analysis allows to compare the achievement of the different policies, Pareto fronts (or efficiency frontiers) and tradeoffs for the set of mutually conflicting objectives. The constraint method is applied to generate the set of non-dominated solutions. The multi-objective framework can be used to design groundwater management policies taking into consideration different stakeholders' interests (e.g., policy makers, agricultures or environmental groups). The methodology was applied to the El Salobral-Los Llanos aquifer in Spain. Over the past 30 years the area has undertaken a significant socioeconomic development, mainly due to the intensive groundwater use for irrigated crops, which has provoked a steady decline of groundwater levels as well as high nitrate concentrations at certain locations (above 50 mg/l.). The results showed the usefulness of this multi-objective hydro-economic approach for designing sustainable nitrate pollution control policies (as fertilizer quotas or efficient fertilizer pricing policies) with insight into the economic cost of satisfying the environmental constraints and the tradeoffs with different time horizons.

Randomized shortest-path problems: two related models.

PubMed

Saerens, Marco; Achbany, Youssef; Fouss, François; Yen, Luh

2009-08-01

This letter addresses the problem of designing the transition probabilities of a finite Markov chain (the policy) in order to minimize the expected cost for reaching a destination node from a source node while maintaining a fixed level of entropy spread throughout the network (the exploration). It is motivated by the following scenario. Suppose you have to route agents through a network in some optimal way, for instance, by minimizing the total travel cost-nothing particular up to now-you could use a standard shortest-path algorithm. Suppose, however, that you want to avoid pure deterministic routing policies in order, for instance, to allow some continual exploration of the network, avoid congestion, or avoid complete predictability of your routing strategy. In other words, you want to introduce some randomness or unpredictability in the routing policy (i.e., the routing policy is randomized). This problem, which will be called the randomized shortest-path problem (RSP), is investigated in this work. The global level of randomness of the routing policy is quantified by the expected Shannon entropy spread throughout the network and is provided a priori by the designer. Then, necessary conditions to compute the optimal randomized policy-minimizing the expected routing cost-are derived. Iterating these necessary conditions, reminiscent of Bellman's value iteration equations, allows computing an optimal policy, that is, a set of transition probabilities in each node. Interestingly and surprisingly enough, this first model, while formulated in a totally different framework, is equivalent to Akamatsu's model ( 1996 ), appearing in transportation science, for a special choice of the entropy constraint. We therefore revisit Akamatsu's model by recasting it into a sum-over-paths statistical physics formalism allowing easy derivation of all the quantities of interest in an elegant, unified way. For instance, it is shown that the unique optimal policy can be obtained by solving a simple linear system of equations. This second model is therefore more convincing because of its computational efficiency and soundness. Finally, simulation results obtained on simple, illustrative examples show that the models behave as expected.

Control and design of multiple unmanned air vehicles for persistent surveillance

NASA Astrophysics Data System (ADS)

Nigam, Nikhil

Control of multiple autonomous aircraft for search and exploration, is a topic of current research interest for applications such as weather monitoring, geographical surveys, search and rescue, tactical reconnaissance, and extra-terrestrial exploration, and the need to distribute sensing is driven by considerations of efficiency, reliability, cost and scalability. Hence, this problem has been extensively studied in the fields of controls and artificial intelligence. The task of persistent surveillance is different from a coverage/exploration problem, in that all areas need to be continuously searched, minimizing the time between visitations to each region in the target space. This distinction does not allow a straightforward application of most exploration techniques to the problem, although ideas from these methods can still be used. The use of aerial vehicles is motivated by their ability to cover larger spaces and their relative insensitivity to terrain. However, the dynamics of Unmanned Air Vehicles (UAVs) adds complexity to the control problem. Most of the work in the literature decouples the vehicle dynamics and control policies, but their interaction is particularly interesting for a surveillance mission. Stochastic environments and UAV failures further enrich the problem by requiring the control policies to be robust, and this aspect is particularly important for hardware implementations. For a persistent mission, it becomes imperative to consider the range/endurance constraints of the vehicles. The coupling of the control policy with the endurance constraints of the vehicles is an aspect that has not been sufficiently explored. Design of UAVs for desirable mission performance is also an issue of considerable significance. The use of a single monolithic optimization for such a problem has practical limitations, and decomposition-based design is a potential alternative. In this research high-level control policies are devised, that are scalable, reliable, efficient, and robust to changes in the environment. Most of the existing techniques that carry performance guarantees are not scalable or robust to changes. The scalable techniques are often heuristic in nature, resulting in lack of reliability and performance. Our policies are tested in a multi-UAV simulation environment developed for this problem, and shown to be near-optimal in spite of being completely reactive in nature. We explicitly account for the coupling between aircraft dynamics and control policies as well, and suggest modifications to improve performance under dynamic constraints. A smart refueling policy is also developed to account for limited endurance, and large performance benefits are observed. The method is based on the solution of a linear program that can be efficiently solved online in a distributed setting, unlike previous work. The Vehicle Swarm Technology Laboratory (VSTL), a hardware testbed developed at Boeing Research and Technology for evaluating swarm of UAVs, is described next and used to test the control strategy in a real-world scenario. The simplicity and robustness of the strategy allows easy implementation and near replication of the performance observed in simulation. Finally, an architecture for system-of-systems design based on Collaborative Optimization (CO) is presented. Earlier work coupling operations and design has used frameworks that make certain assumptions not valid for this problem. The efficacy of our approach is illustrated through preliminary design results, and extension to more realistic settings is also demonstrated.

Open space preservation, property value, and optimal spatial configuration

Treesearch

Yong Jiang; Stephen K. Swallow

2007-01-01

The public has increasingly demonstrated a strong support for open space preservation. How to finance the socially efficient level of open space with the optimal spatial structure is of high policy relevance to local governments. In this study, we developed a spatially explicit open space model to help identify the socially optimal amount and optimal spatial...

Optimal Pricing and Advertising Policies for New Product Oligopoly Models. Revision.

DTIC Science & Technology

1981-08-01

The problem of characterizing an optimal pricing and advertising policy over time is an important question in the field of marketing as well as in the...the effects of the learning curve phenomenon and market saturation are most pronounced. We isider first the monopoly case with linear advertising cost...Another sur- prising result i that, after the market is at least half saturated, a pulse of advertising must be preceded by a significant drop in

A Sarsa(λ)-Based Control Model for Real-Time Traffic Light Coordination

PubMed Central

Zhu, Fei; Liu, Quan; Fu, Yuchen; Huang, Wei

2014-01-01

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control. PMID:24592183

Analysis of Artificial Neural Network Backpropagation Using Conjugate Gradient Fletcher Reeves In The Predicting Process

NASA Astrophysics Data System (ADS)

Wanto, Anjar; Zarlis, Muhammad; Sawaluddin; Hartama, Dedy

2017-12-01

Backpropagation is a good artificial neural network algorithm used to predict, one of which is to predict the rate of Consumer Price Index (CPI) based on the foodstuff sector. While conjugate gradient fletcher reeves is a suitable optimization method when juxtaposed with backpropagation method, because this method can shorten iteration without reducing the quality of training and testing result. Consumer Price Index (CPI) data that will be predicted to come from the Central Statistics Agency (BPS) Pematangsiantar. The results of this study will be expected to contribute to the government in making policies to improve economic growth. In this study, the data obtained will be processed by conducting training and testing with artificial neural network backpropagation by using parameter learning rate 0,01 and target error minimum that is 0.001-0,09. The training network is built with binary and bipolar sigmoid activation functions. After the results with backpropagation are obtained, it will then be optimized using the conjugate gradient fletcher reeves method by conducting the same training and testing based on 5 predefined network architectures. The result, the method used can increase the speed and accuracy result.

A review of distributed parameter groundwater management modeling methods

USGS Publications Warehouse

Gorelick, Steven M.

1983-01-01

Models which solve the governing groundwater flow or solute transport equations in conjunction with optimization techniques, such as linear and quadratic programing, are powerful aquifer management tools. Groundwater management models fall in two general categories: hydraulics or policy evaluation and water allocation. Groundwater hydraulic management models enable the determination of optimal locations and pumping rates of numerous wells under a variety of restrictions placed upon local drawdown, hydraulic gradients, and water production targets. Groundwater policy evaluation and allocation models can be used to study the influence upon regional groundwater use of institutional policies such as taxes and quotas. Furthermore, fairly complex groundwater-surface water allocation problems can be handled using system decomposition and multilevel optimization. Experience from the few real world applications of groundwater optimization-management techniques is summarized. Classified separately are methods for groundwater quality management aimed at optimal waste disposal in the subsurface. This classification is composed of steady state and transient management models that determine disposal patterns in such a way that water quality is protected at supply locations. Classes of research missing from the literature are groundwater quality management models involving nonlinear constraints, models which join groundwater hydraulic and quality simulations with political-economic management considerations, and management models that include parameter uncertainty.

Optimizing the response to surveillance alerts in automated surveillance systems.

PubMed

Izadi, Masoumeh; Buckeridge, David L

2011-02-28

Although much research effort has been directed toward refining algorithms for disease outbreak alerting, considerably less attention has been given to the response to alerts generated from statistical detection algorithms. Given the inherent inaccuracy in alerting, it is imperative to develop methods that help public health personnel identify optimal policies in response to alerts. This study evaluates the application of dynamic decision making models to the problem of responding to outbreak detection methods, using anthrax surveillance as an example. Adaptive optimization through approximate dynamic programming is used to generate a policy for decision making following outbreak detection. We investigate the degree to which the model can tolerate noise theoretically, in order to keep near optimal behavior. We also evaluate the policy from our model empirically and compare it with current approaches in routine public health practice for investigating alerts. Timeliness of outbreak confirmation and total costs associated with the decisions made are used as performance measures. Using our approach, on average, 80 per cent of outbreaks were confirmed prior to the fifth day of post-attack with considerably less cost compared to response strategies currently in use. Experimental results are also provided to illustrate the robustness of the adaptive optimization approach and to show the realization of the derived error bounds in practice. Copyright © 2011 John Wiley & Sons, Ltd.

A Review of Distributed Parameter Groundwater Management Modeling Methods

NASA Astrophysics Data System (ADS)

Gorelick, Steven M.

1983-04-01

Models which solve the governing groundwater flow or solute transport equations in conjunction with optimization techniques, such as linear and quadratic programing, are powerful aquifer management tools. Groundwater management models fall in two general categories: hydraulics or policy evaluation and water allocation. Groundwater hydraulic management models enable the determination of optimal locations and pumping rates of numerous wells under a variety of restrictions placed upon local drawdown, hydraulic gradients, and water production targets. Groundwater policy evaluation and allocation models can be used to study the influence upon regional groundwater use of institutional policies such as taxes and quotas. Furthermore, fairly complex groundwater-surface water allocation problems can be handled using system decomposition and multilevel optimization. Experience from the few real world applications of groundwater optimization-management techniques is summarized. Classified separately are methods for groundwater quality management aimed at optimal waste disposal in the subsurface. This classification is composed of steady state and transient management models that determine disposal patterns in such a way that water quality is protected at supply locations. Classes of research missing from the literature are groundwater quality management models involving nonlinear constraints, models which join groundwater hydraulic and quality simulations with political-economic management considerations, and management models that include parameter uncertainty.

Integrated dynamic policy management methodology and system for strategic environmental assessment of golf course installation policy in Taiwan

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Ching-Ho, E-mail: chchen@tea.ntue.edu.t; Liu, Wei-Lin, E-mail: wlliu@nanya.edu.t; Graduate Institute of Environmental Engineering, National Central University, Jungli, Taoyuan 320, Taiwan

Strategic environmental assessment (SEA) focuses primarily on assessing how policies, plans, and programs (PPPs) influence the sustainability of the involved regions. However, the processes of assessing policies and developing management strategies for pollution load and resource use are usually separate in the current SEA system. This study developed a policy management methodology to overcome the defects generated during the above processes. This work first devised a dynamic management framework using the methods of systems thinking, system dynamics, and Managing for Results (MFRs). Furthermore, a driving force-pressure-state-impact-response (DPSIR) indicator system was developed. The golf course installation policy was applied as amore » case study. Taiwan, counties of Taiwan, and the golf courses within those individual counties were identified as a system, subsystems, and objects, respectively. This study identified an object-linked double-layer framework with multi-stage-option to simultaneously to quantify golf courses in each subsystem and determine ratios of abatement and allocation for pollution load and resource use of each golf course. The DPSIR indicator values for each item of each golf course in each subsystem are calculated based on the options taken in the two decision layers. The summation of indicator values for all items of all golf courses in all subsystems according to various options is defined as the sustainability value of the policy. An optimization model and a system (IDPMS) were developed to obtain the greatest sustainability value of the policy, while golf course quantity, human activity intensity, total quantities of pollution load and resource use are simultaneously obtained. The solution method based on enumeration of multiple bounds for objectives and constraints (EMBOC) was developed for the problem with 1.95 x 10{sup 128} combinations of possible options to solve the optimal solution in ten minutes using a personal computer with 3.0 GHz CPU. This study obtain the policy with the optimal environmental sustainability value in Taiwan is 102 golf courses. Human activity intensity and total quantities of pollution load and resource use which are concurrently obtained are less than those of the existing policy and the existing quantities in 2006. The optimal solution remains unchanged under most sensitivity analysis conditions, unless the weights and constraints are extremely changed. The analytical results indicate that the proposed methodology can be used to assist the authorities for simultaneously generating and assessing the policy during the SEA process.« less

Optimal harvesting of a stochastic delay tri-trophic food-chain model with Lévy jumps

NASA Astrophysics Data System (ADS)

Qiu, Hong; Deng, Wenmin

2018-02-01

In this paper, the optimal harvesting of a stochastic delay tri-trophic food-chain model with Lévy jumps is considered. We introduce two kinds of environmental perturbations in this model. One is called white noise which is continuous and is described by a stochastic integral with respect to the standard Brownian motion. And the other one is jumping noise which is modeled by a Lévy process. Under some mild assumptions, the critical values between extinction and persistent in the mean of each species are established. The sufficient and necessary criteria for the existence of optimal harvesting policy are established and the optimal harvesting effort and the maximum of sustainable yield are also obtained. We utilize the ergodic method to discuss the optimal harvesting problem. The results show that white noises and Lévy noises significantly affect the optimal harvesting policy while time delays is harmless for the optimal harvesting strategy in some cases. At last, some numerical examples are introduced to show the validity of our results.

Optimization of Medication Use at Accountable Care Organizations.

PubMed

Wilks, Chrisanne; Krisle, Erik; Westrich, Kimberly; Lunner, Kristina; Muhlestein, David; Dubois, Robert

2017-10-01

Optimized medication use involves the effective use of medications for better outcomes, improved patient experience, and lower costs. Few studies systematically gather data on the actions accountable care organizations (ACOs) have taken to optimize medication use. To (a) assess how ACOs optimize medication use; (b) establish an association between efforts to optimize medication use and achievement on financial and quality metrics; (c) identify organizational factors that correlate with optimized medication use; and (d) identify barriers to optimized medication use. This cross-sectional study consisted of a survey and interviews that gathered information on the perceptions of ACO leadership. The survey contained a medication practices inventory (MPI) composed of 38 capabilities across 6 functional domains related to optimizing medication use. ACOs completed self-assessments that included rating each component of the MPI on a scale of 1 to 10. Fisher's exact tests, 2-proportions tests, t-tests, and logistic regression were used to test for associations between ACO scores on the MPI and performance on financial and quality metrics, and on ACO descriptive characteristics. Of the 847 ACOs that were contacted, 49 provided usable survey data. These ACOs rated their own system's ability to manage the quality and costs of optimizing medication use, providing a 64% and 31% affirmative response, respectively. Three ACOs achieved an overall MPI score of 8 or higher, 45 scored between 4 and 7.9, and 1 scored between 0 and 3.9. Using the 3 score groups, the study did not identify a relationship between MPI scores and achievement on financial or quality benchmarks, ACO provider type, member volume, date of ACO creation, or the presence of a pharmacist in a leadership position. Barriers to optimizing medication use relate to reimbursement for pharmacist integration, lack of health information technology interoperability, lack of data, feasibility issues, and physician buy-in. Compared with 2012 data, data on ACOs that participated in this study show that they continue to build effective strategies to optimize medication use. These ACOs struggle with both notification related to prescription use and measurement of the influence optimized medication use has on costs and quality outcomes. Compared with the earlier study, these data find that more ACOs are involving pharmacists directly in care, expanding the use of generics, electronically transmitting prescriptions, identifying gaps in care and potential adverse events, and educating patients on therapeutic alternatives. ACO-level policies that facilitate practices to optimize medication use are needed. Integrating pharmacists into care, giving both pharmacists and physicians access to clinical data, obtaining physician buy-in, and measuring the impact of practices to optimize medication use may improve these practices. This research was sponsored and funded by the National Pharmaceutical Council (NPC), an industry funded health policy research group that is not involved in lobbying or advocacy. Employees of the sponsor contributed to the research questions, determination of the relevance of the research questions, and the research design. Specifically, there was involvement in the survey and interview instruments. They also contributed to some data interpretation and revision of the manuscript. Leavitt Partners was hired by NPC to conduct research for this study and also serves a number of health care clients, including life sciences companies, provider organizations, accountable care organizations, and payers. Westrich and Dubois are employed by the NPC. Wilks, Krisle, Lunner, and Muhlestein are employed by Leavitt Partners and did not receive separate compensation. Study concept and design were contributed by Krisle, Dubois, and Muhlestein, along with Lunner and Westrich. Krisle and Muhlestein collected the data, and data interpretation was performed by Wilks, Krisle, and Muhlestein, along with Dubois and Westrich. The manuscript was written primarily by Wilks, along with Krisle and Muhlestein, and revised by Wilks, Westrich, Lunner, and Krisle. Preliminary versions of this work were presented at the following: National Council for Prescription Drug Programs Educational Summit, November 1, 2016; Academy Health 2016 Annual Research Meeting, June 27, 2016; Accountable Care Learning Collaborative Webinar, June 16, 2016; the 21st Annual PBMI Drug Benefit Conference, February 29, 2016; National Value-Based Payment and Pay for Performance Summit, February 17, 2016; National Accountable Care Congress, November 17, 2015; and American Journal of Managed Care's ACO Emerging Healthcare Delivery Coalition, Fall 2015 Live Meeting, October 15, 2015.

Effectiveness of breast cancer screening policies in countries with medium-low incidence rates.

PubMed

Kong, Qingxia; Mondschein, Susana; Pereira, Ana

2018-02-05

Chile has lower breast cancer incidence rates compared to those in developed countries. Our public health system aims to perform 10 biennial screening mammograms in the age group of 50 to 69 years by 2020. Using a dynamic programming model, we have found the optimal ages to perform 10 screening mammograms that lead to the lowest lifetime death rate and we have evaluated a set of fixed inter-screening interval policies. The optimal ages for the 10 mammograms are 43, 47, 51, 54, 57, 61, 65, 68, 72, and 76 years, and the most effective fixed inter-screening is every four years after the 40 years. Both policies respectively reduce lifetime death rate in 6.4% and 5.7% and the cost of saving one life in 17% and 9.3% compared to the 2020 Chilean policy. Our findings show that two-year inter-screening interval policies are less effective in countries with lower breast cancer incidence; thus we recommend screening policies with a wider age range and larger inter-screening intervals for Chile.

Effectiveness of breast cancer screening policies in countries with medium-low incidence rates

PubMed Central

Kong, Qingxia; Mondschein, Susana; Pereira, Ana

2018-01-01

ABSTRACT Chile has lower breast cancer incidence rates compared to those in developed countries. Our public health system aims to perform 10 biennial screening mammograms in the age group of 50 to 69 years by 2020. Using a dynamic programming model, we have found the optimal ages to perform 10 screening mammograms that lead to the lowest lifetime death rate and we have evaluated a set of fixed inter-screening interval policies. The optimal ages for the 10 mammograms are 43, 47, 51, 54, 57, 61, 65, 68, 72, and 76 years, and the most effective fixed inter-screening is every four years after the 40 years. Both policies respectively reduce lifetime death rate in 6.4% and 5.7% and the cost of saving one life in 17% and 9.3% compared to the 2020 Chilean policy. Our findings show that two-year inter-screening interval policies are less effective in countries with lower breast cancer incidence; thus we recommend screening policies with a wider age range and larger inter-screening intervals for Chile. PMID:29412375

Ontario's daily physical activity policy for elementary schools: is everything in place for success?

PubMed

Robertson-Wilson, Jennifer E; Lévesque, Lucie

2009-01-01

The development, implementation, and evaluation of policies may play an important role in promoting health behaviours such as physical activity. The Ontario Ministry of Education (OME) recently mandated Memorandum No. 138 requiring daily physical activity (DPA) for Ontario elementary students in grades one through eight. The purpose of this paper is to examine implementation strategies. Hogwood and Gunn's 10 preconditions for "perfect implementation" are used to examine publicly available Ministry DPA policy documents to assess whether these implementation strategies have been considered in the policy documents. Several preconditions (e.g., allocation of resources, task specification) appear to have been considered, however a number of preconditions (e.g., the sustainability of resources, extent to which the policy is valued, and evaluation plans) thought to be important require additional attention to ensure optimal DPA implementation. Additional reflection upon Hogwood and Gunn's implementation preconditions would, in our opinion, assist in facilitating optimal DPA implementation as per Memorandum No. 138.

Optimal policies of non-cross-resistant chemotherapy on Goldie and Coldman's cancer model.

PubMed

Chen, Jeng-Huei; Kuo, Ya-Hui; Luh, Hsing Paul

2013-10-01

Mathematical models can be used to study the chemotherapy on tumor cells. Especially, in 1979, Goldie and Coldman proposed the first mathematical model to relate the drug sensitivity of tumors to their mutation rates. Many scientists have since referred to this pioneering work because of its simplicity and elegance. Its original idea has also been extended and further investigated in massive follow-up studies of cancer modeling and optimal treatment. Goldie and Coldman, together with Guaduskas, later used their model to explain why an alternating non-cross-resistant chemotherapy is optimal with a simulation approach. Subsequently in 1983, Goldie and Coldman proposed an extended stochastic based model and provided a rigorous mathematical proof to their earlier simulation work when the extended model is approximated by its quasi-approximation. However, Goldie and Coldman's analytic study of optimal treatments majorly focused on a process with symmetrical parameter settings, and presented few theoretical results for asymmetrical settings. In this paper, we recast and restate Goldie, Coldman, and Guaduskas' model as a multi-stage optimization problem. Under an asymmetrical assumption, the conditions under which a treatment policy can be optimal are derived. The proposed framework enables us to consider some optimal policies on the model analytically. In addition, Goldie, Coldman and Guaduskas' work with symmetrical settings can be treated as a special case of our framework. Based on the derived conditions, this study provides an alternative proof to Goldie and Coldman's work. In addition to the theoretical derivation, numerical results are included to justify the correctness of our work. Copyright © 2013 Elsevier Inc. All rights reserved.

Single Mothers and Their Infants: Factors Associated with Optimal Parenting.

ERIC Educational Resources Information Center

Barratt, Marguerite Stevenson; And Others

1991-01-01

Examined factors that might influence optimal early parenting by Caucasian single mothers (n=53). Results indicated optimal parenting was linked with older maternal age, fewer maternal psychological symptoms, and less difficult infant temperament. Recommends particular needs of single mother should be considered when formulating public policy.…

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

DTIC Science & Technology

2016-09-09

prenticeship Scheduling (COVAS), which performs ma- chine learning using human expert demonstration, in conjunction with optimization, to automatically and ef...ficiently produce optimal solutions to challenging real- world scheduling problems. COVAS first learns a policy from human scheduling demonstration via...apprentice- ship learning , then uses this initial solution to provide a tight bound on the value of the optimal solution, thereby substantially

Biocapacity optimization in regional planning

PubMed Central

Guo, Jianjun; Yue, Dongxia; Li, Kai; Hui, Cang

2017-01-01

Ecological overshoot has been accelerating across the globe. Optimizing biocapacity has become a key to resolve the overshoot of ecological demand in regional sustainable development. However, most literature has focused on reducing ecological footprint but ignores the potential of spatial optimization of biocapacity through regional planning of land use. Here we develop a spatial probability model and present four scenarios for optimizing biocapacity of a river basin in Northwest China. The potential of enhanced biocapacity and its effects on ecological overshoot and water consumption in the region were explored. Two scenarios with no restrictions on croplands and water use reduced the overshoot by 29 to 53%, and another two scenarios which do not allow croplands and water use to increase worsened the overshoot by 11 to 15%. More spatially flexible transition rules of land use led to higher magnitude of change after optimization. However, biocapacity optimization required a large amount of additional water resources, casting considerable pressure on the already water-scarce socio-ecological system. Our results highlight the potential for policy makers to manage/optimize regional land use which addresses ecological overshoot. Investigation on the feasibility of such spatial optimization complies with the forward-looking policies for sustainable development and deserves further attention. PMID:28112224

Optimal Scheduling and Fair Service Policy for STDMA in Underwater Networks with Acoustic Communications

PubMed Central

2018-01-01

In this work, a multi-hop string network with a single sink node is analyzed. A periodic optimal scheduling for TDMA operation that considers the characteristic long propagation delay of the underwater acoustic channel is presented. This planning of transmissions is obtained with the help of a new geometrical method based on a 2D lattice in the space-time domain. In order to evaluate the performance of this optimal scheduling, two service policies have been compared: FIFO and Round-Robin. Simulation results, including achievable throughput, packet delay, and queue length, are shown. The network fairness has also been quantified with the Gini index. PMID:29462966

Finite horizon optimum control with and without a scrap value

NASA Astrophysics Data System (ADS)

Neck, R.; Blueschke-Nikolaeva, V.; Blueschke, D.

2017-06-01

In this paper, we study the effects of scrap values on the solutions of optimal control problems with finite time horizon. We show how to include a scrap value, either for the state variables or for the state and the control variables, in the OPTCON2 algorithm for the optimal control of dynamic economic systems. We ask whether the introduction of a scrap value can serve as a substitute for an infinite horizon in economic policy optimization problems where the latter option is not available. Using a simple numerical macroeconomic model, we demonstrate that the introduction of a scrap value cannot induce control policies which can be expected for problems with an infinite time horizon.

A modelling tool for policy analysis to support the design of efficient and effective policy responses for complex public health problems.

PubMed

Atkinson, Jo-An; Page, Andrew; Wells, Robert; Milat, Andrew; Wilson, Andrew

2015-03-03

In the design of public health policy, a broader understanding of risk factors for disease across the life course, and an increasing awareness of the social determinants of health, has led to the development of more comprehensive, cross-sectoral strategies to tackle complex problems. However, comprehensive strategies may not represent the most efficient or effective approach to reducing disease burden at the population level. Rather, they may act to spread finite resources less intensively over a greater number of programs and initiatives, diluting the potential impact of the investment. While analytic tools are available that use research evidence to help identify and prioritise disease risk factors for public health action, they are inadequate to support more targeted and effective policy responses for complex public health problems. This paper discusses the limitations of analytic tools that are commonly used to support evidence-informed policy decisions for complex problems. It proposes an alternative policy analysis tool which can integrate diverse evidence sources and provide a platform for virtual testing of policy alternatives in order to design solutions that are efficient, effective, and equitable. The case of suicide prevention in Australia is presented to demonstrate the limitations of current tools to adequately inform prevention policy and discusses the utility of the new policy analysis tool. In contrast to popular belief, a systems approach takes a step beyond comprehensive thinking and seeks to identify where best to target public health action and resources for optimal impact. It is concerned primarily with what can be reasonably left out of strategies for prevention and can be used to explore where disinvestment may occur without adversely affecting population health (or equity). Simulation modelling used for policy analysis offers promise in being able to better operationalise research evidence to support decision making for complex problems, improve targeting of public health policy, and offers a foundation for strengthening relationships between policy makers, stakeholders, and researchers.

Preliminary Work for Examining the Scalability of Reinforcement Learning

NASA Technical Reports Server (NTRS)

Clouse, Jeff

1998-01-01

Researchers began studying automated agents that learn to perform multiple-step tasks early in the history of artificial intelligence (Samuel, 1963; Samuel, 1967; Waterman, 1970; Fikes, Hart & Nilsonn, 1972). Multiple-step tasks are tasks that can only be solved via a sequence of decisions, such as control problems, robotics problems, classic problem-solving, and game-playing. The objective of agents attempting to learn such tasks is to use the resources they have available in order to become more proficient at the tasks. In particular, each agent attempts to develop a good policy, a mapping from states to actions, that allows it to select actions that optimize a measure of its performance on the task; for example, reducing the number of steps necessary to complete the task successfully. Our study focuses on reinforcement learning, a set of learning techniques where the learner performs trial-and-error experiments in the task and adapts its policy based on the outcome of those experiments. Much of the work in reinforcement learning has focused on a particular, simple representation, where every problem state is represented explicitly in a table, and associated with each state are the actions that can be chosen in that state. A major advantage of this table lookup representation is that one can prove that certain reinforcement learning techniques will develop an optimal policy for the current task. The drawback is that the representation limits the application of reinforcement learning to multiple-step tasks with relatively small state-spaces. There has been a little theoretical work that proves that convergence to optimal solutions can be obtained when using generalization structures, but the structures are quite simple. The theory says little about complex structures, such as multi-layer, feedforward artificial neural networks (Rumelhart & McClelland, 1986), but empirical results indicate that the use of reinforcement learning with such structures is promising. These empirical results make no theoretical claims, nor compare the policies produced to optimal policies. A goal of our work is to be able to make the comparison between an optimal policy and one stored in an artificial neural network. A difficulty of performing such a study is finding a multiple-step task that is small enough that one can find an optimal policy using table lookup, yet large enough that, for practical purposes, an artificial neural network is really required. We have identified a limited form of the game OTHELLO as satisfying these requirements. The work we report here is in the very preliminary stages of research, but this paper provides background for the problem being studied and a description of our initial approach to examining the problem. In the remainder of this paper, we first describe reinforcement learning in more detail. Next, we present the game OTHELLO. Finally we argue that a restricted form of the game meets the requirements of our study, and describe our preliminary approach to finding an optimal solution to the problem.

Optimizing Chemical Reactions with Deep Reinforcement Learning.

PubMed

Zhou, Zhenpeng; Li, Xiaocheng; Zare, Richard N

2017-12-27

Deep reinforcement learning was employed to optimize chemical reactions. Our model iteratively records the results of a chemical reaction and chooses new experimental conditions to improve the reaction outcome. This model outperformed a state-of-the-art blackbox optimization algorithm by using 71% fewer steps on both simulations and real reactions. Furthermore, we introduced an efficient exploration strategy by drawing the reaction conditions from certain probability distributions, which resulted in an improvement on regret from 0.062 to 0.039 compared with a deterministic policy. Combining the efficient exploration policy with accelerated microdroplet reactions, optimal reaction conditions were determined in 30 min for the four reactions considered, and a better understanding of the factors that control microdroplet reactions was reached. Moreover, our model showed a better performance after training on reactions with similar or even dissimilar underlying mechanisms, which demonstrates its learning ability.

Online stochastic optimization of radiotherapy patient scheduling.

PubMed

Legrain, Antoine; Fortin, Marie-Andrée; Lahrichi, Nadia; Rousseau, Louis-Martin

2015-06-01

The effective management of a cancer treatment facility for radiation therapy depends mainly on optimizing the use of the linear accelerators. In this project, we schedule patients on these machines taking into account their priority for treatment, the maximum waiting time before the first treatment, and the treatment duration. We collaborate with the Centre Intégré de Cancérologie de Laval to determine the best scheduling policy. Furthermore, we integrate the uncertainty related to the arrival of patients at the center. We develop a hybrid method combining stochastic optimization and online optimization to better meet the needs of central planning. We use information on the future arrivals of patients to provide an accurate picture of the expected utilization of resources. Results based on real data show that our method outperforms the policies typically used in treatment centers.

Balancing Flood Risk and Water Supply in California: Policy Search Combining Short-Term Forecast Ensembles and Groundwater Recharge

NASA Astrophysics Data System (ADS)

Herman, J. D.; Steinschneider, S.; Nayak, M. A.

2017-12-01

Short-term weather forecasts are not codified into the operating policies of federal, multi-purpose reservoirs, despite their potential to improve service provision. This is particularly true for facilities that provide flood protection and water supply, since the potential flood damages are often too severe to accept the risk of inaccurate forecasts. Instead, operators must maintain empty storage capacity to mitigate flood risk, even if the system is currently in drought, as occurred in California from 2012-2016. This study investigates the potential for forecast-informed operating rules to improve water supply efficiency while maintaining flood protection, combining state-of-the-art weather hindcasts with a novel tree-based policy optimization framework. We hypothesize that forecasts need only accurately predict the occurrence of a storm, rather than its intensity, to be effective in regions like California where wintertime, synoptic-scale storms dominate the flood regime. We also investigate the potential for downstream groundwater injection to improve the utility of forecasts. These hypotheses are tested in a case study of Folsom Reservoir on the American River. Because available weather hindcasts are relatively short (10-20 years), we propose a new statistical framework to develop synthetic forecasts to assess the risk associated with inaccurate forecasts. The efficiency of operating policies is tested across a range of scenarios that include varying forecast skill and additional groundwater pumping capacity. Results suggest that the combined use of groundwater storage and short-term weather forecasts can substantially improve the tradeoff between water supply and flood control objectives in large, multi-purpose reservoirs in California.

Adaptive optimal control of unknown constrained-input systems using policy iteration and neural networks.

PubMed

Modares, Hamidreza; Lewis, Frank L; Naghibi-Sistani, Mohammad-Bagher

2013-10-01

This paper presents an online policy iteration (PI) algorithm to learn the continuous-time optimal control solution for unknown constrained-input systems. The proposed PI algorithm is implemented on an actor-critic structure where two neural networks (NNs) are tuned online and simultaneously to generate the optimal bounded control policy. The requirement of complete knowledge of the system dynamics is obviated by employing a novel NN identifier in conjunction with the actor and critic NNs. It is shown how the identifier weights estimation error affects the convergence of the critic NN. A novel learning rule is developed to guarantee that the identifier weights converge to small neighborhoods of their ideal values exponentially fast. To provide an easy-to-check persistence of excitation condition, the experience replay technique is used. That is, recorded past experiences are used simultaneously with current data for the adaptation of the identifier weights. Stability of the whole system consisting of the actor, critic, system state, and system identifier is guaranteed while all three networks undergo adaptation. Convergence to a near-optimal control law is also shown. The effectiveness of the proposed method is illustrated with a simulation example.

Optimal order policy in response to announced price increase for deteriorating items with limited special order quantity

NASA Astrophysics Data System (ADS)

Ouyang, Liang-Yuh; Wu, Kun-Shan; Yang, Chih-Te; Yen, Hsiu-Feng

2016-02-01

When a supplier announces an impending price increase due to take effect at a certain time in the future, it is important for each retailer to decide whether to purchase additional stock to take advantage of the present lower price. This study explores the possible effects of price increases on a retailer's replenishment policy when the special order quantity is limited and the rate of deterioration of the goods is assumed to be constant. The two situations discussed in this study are as follows: (1) when the special order time coincides with the retailer's replenishment time and (2) when the special order time occurs during the retailer's sales period. By analysing the total cost savings between special and regular orders during the depletion time of the special order quantity, the optimal order policy for each situation can be determined. We provide several numerical examples to illustrate the theories in practice. Additionally, we conduct a sensitivity analysis on the optimal solution with respect to the main parameters.

A comparison of alternative medicare reimbursement policies under optimal hospital pricing.

PubMed Central

Dittman, D A; Morey, R C

1983-01-01

This paper applies and extends the use of a nonlinear hospital pricing model, recently posited in the literature by Dittman and Morey [1]. That model applied a hospital profit-maximizing behavior and studied the effects of optimal pricing of hospital ancillary services on the incidence of payment by private insurance companies and the Medicare trust fund. Here, we examine variations of the above model where both hospital profit-maximizing and profit-satisficing postures are of interest. We apply the model to three types of Medicare reimbursement policies currently in use or under legislative mandate to implement. The policies differ according to hospital size and whether cross-subsidies are allowed. We are interested in determining the effects of profit-maximizing and -satisficing behaviors of these three reimbursement policies on the levels of profits received, and on the respective implications for private payors and the Medicare trust fund. PMID:6347973

Optimal advanced credit releases in ecosystem service markets.

PubMed

BenDor, Todd K; Guo, Tianshu; Yates, Andrew J

2014-03-01

Ecosystem service markets are popular policy tools for ecosystem protection. Advanced credit releases are an important factor affecting the supply side of ecosystem markets. Under an advanced credit release policy, regulators give ecosystem suppliers a fraction of the total ecosystem credits generated by a restoration project before it is verified that the project actually achieves the required ecological thresholds. In spite of their prominent role in ecosystem markets, there is virtually no regulatory or research literature on the proper design of advanced credit release policies. Using U.S. aquatic ecosystem markets as an example, we develop a principal-agent model of the behavior of regulators and wetland/stream mitigation bankers to determine and explore the optimal degree of advance credit release. The model highlights the tension between regulators' desire to induce market participation, while at the same time ensuring that bankers successfully complete ecological restoration. Our findings suggest several simple guidelines for strengthening advanced credit release policy.

Optimal Advanced Credit Releases in Ecosystem Service Markets

NASA Astrophysics Data System (ADS)

BenDor, Todd K.; Guo, Tianshu; Yates, Andrew J.

2014-03-01

Ecosystem service markets are popular policy tools for ecosystem protection. Advanced credit releases are an important factor affecting the supply side of ecosystem markets. Under an advanced credit release policy, regulators give ecosystem suppliers a fraction of the total ecosystem credits generated by a restoration project before it is verified that the project actually achieves the required ecological thresholds. In spite of their prominent role in ecosystem markets, there is virtually no regulatory or research literature on the proper design of advanced credit release policies. Using U.S. aquatic ecosystem markets as an example, we develop a principal-agent model of the behavior of regulators and wetland/stream mitigation bankers to determine and explore the optimal degree of advance credit release. The model highlights the tension between regulators' desire to induce market participation, while at the same time ensuring that bankers successfully complete ecological restoration. Our findings suggest several simple guidelines for strengthening advanced credit release policy.

Policy Implications Analysis: A Methodological Advancement for Policy Research and Evaluation.

ERIC Educational Resources Information Center

Madey, Doren L.; Stenner, A. Jackson

Policy Implications Analysis (PIA) is a tool designed to maximize the likelihood that an evaluation report will have an impact on decision-making. PIA was designed to help people planning and conducting evaluations tailor their information so that it has optimal potential for being used and acted upon. This paper describes the development and…

Using Contribution Analysis to Evaluate the Impacts of Research on Policy: Getting to "Good Enough"

ERIC Educational Resources Information Center

Riley, Barbara L.; Kernoghan, Alison; Stockton, Lisa; Montague, Steve; Yessis, Jennifer; Willis, Cameron D.

2018-01-01

Assessing societal impacts of research is more difficult than assessing advances in knowledge. Methods to evaluate research impact on policy processes and outcomes are especially underdeveloped, and are needed to optimize the influence of research on policy for addressing complex issues such as chronic diseases. Contribution analysis (CA), a…

Alcohol policy reform in Australia: what can we learn from the evidence?

PubMed

Doran, Christopher M; Hall, Wayne D; Shakeshaft, Anthony P; Vos, Theo; Cobiac, Linda J

2010-04-19

Alcohol consumption is a major risk factor contributing to the burden of disease in Australia. The National Preventative Health Taskforce recommends the long-term goal of reshaping Australia's drinking culture to produce healthier and safer outcomes. A study of the cost-effectiveness of interventions to reduce alcohol-related harm in Australia suggests that policymakers could achieve over 10 times the health gain if they reallocated the current level of investment. The optimal package of interventions identified in the study comprises, in order of cost-effectiveness, volumetric taxation, advertising bans, an increase in the minimum legal drinking age to 21 years, brief intervention by primary care practitioners, licensing controls, a drink-driving mass media campaign, and random breath testing. Australia has a window of opportunity to significantly expand activities to reduce alcohol-related harm. It is important that federal and state governments take this opportunity to reform alcohol policy in Australia.

Optimal maintenance of a multi-unit system under dependencies

NASA Astrophysics Data System (ADS)

Sung, Ho-Joon

The availability, or reliability, of an engineering component greatly influences the operational cost and safety characteristics of a modern system over its life-cycle. Until recently, the reliance on past empirical data has been the industry-standard practice to develop maintenance policies that provide the minimum level of system reliability. Because such empirically-derived policies are vulnerable to unforeseen or fast-changing external factors, recent advancements in the study of topic on maintenance, which is known as optimal maintenance problem, has gained considerable interest as a legitimate area of research. An extensive body of applicable work is available, ranging from those concerned with identifying maintenance policies aimed at providing required system availability at minimum possible cost, to topics on imperfect maintenance of multi-unit system under dependencies. Nonetheless, these existing mathematical approaches to solve for optimal maintenance policies must be treated with caution when considered for broader applications, as they are accompanied by specialized treatments to ease the mathematical derivation of unknown functions in both objective function and constraint for a given optimal maintenance problem. These unknown functions are defined as reliability measures in this thesis, and theses measures (e.g., expected number of failures, system renewal cycle, expected system up time, etc.) do not often lend themselves to possess closed-form formulas. It is thus quite common to impose simplifying assumptions on input probability distributions of components' lifetime or repair policies. Simplifying the complex structure of a multi-unit system to a k-out-of-n system by neglecting any sources of dependencies is another commonly practiced technique intended to increase the mathematical tractability of a particular model. This dissertation presents a proposal for an alternative methodology to solve optimal maintenance problems by aiming to achieve the same end-goals as Reliability Centered Maintenance (RCM). RCM was first introduced to the aircraft industry in an attempt to bridge the gap between the empirically-driven and theory-driven approaches to establishing optimal maintenance policies. Under RCM, qualitative processes that enable the prioritizing of functions based on the criticality and influence would be combined with mathematical modeling to obtain the optimal maintenance policies. Where this thesis work deviates from RCM is its proposal to directly apply quantitative processes to model the reliability measures in optimal maintenance problem. First, Monte Carlo (MC) simulation, in conjunction with a pre-determined Design of Experiments (DOE) table, can be used as a numerical means of obtaining the corresponding discrete simulated outcomes of the reliability measures based on the combination of decision variables (e.g., periodic preventive maintenance interval, trigger age for opportunistic maintenance, etc.). These discrete simulation results can then be regressed as Response Surface Equations (RSEs) with respect to the decision variables. Such an approach to represent the reliability measures with continuous surrogate functions (i.e., the RSEs) not only enables the application of the numerical optimization technique to solve for optimal maintenance policies, but also obviates the need to make mathematical assumptions or impose over-simplifications on the structure of a multi-unit system for the sake of mathematical tractability. The applicability of the proposed methodology to a real-world optimal maintenance problem is showcased through its application to a Time Limited Dispatch (TLD) of Full Authority Digital Engine Control (FADEC) system. In broader terms, this proof-of-concept exercise can be described as a constrained optimization problem, whose objective is to identify the optimal system inspection interval that guarantees a certain level of availability for a multi-unit system. A variety of reputable numerical techniques were used to model the problem as accurately as possible, including algorithms for the MC simulation, imperfect maintenance model from quasi renewal processes, repair time simulation, and state transition rules. Variance Reduction Techniques (VRTs) were also used in an effort to enhance MC simulation efficiency. After accurate MC simulation results are obtained, the RSEs are generated based on the goodness-of-fit measure to yield as parsimonious model as possible to construct the optimization problem. Under the assumption of constant failure rate for lifetime distributions, the inspection interval from the proposed methodology was found to be consistent with the one from the common approach used in industry that leverages Continuous Time Markov Chain (CTMC). While the latter does not consider maintenance cost settings, the proposed methodology enables an operator to consider different types of maintenance cost settings, e.g., inspection cost, system corrective maintenance cost, etc., to result in more flexible maintenance policies. When the proposed methodology was applied to the same TLD of FADEC example, but under the more generalized assumption of strictly Increasing Failure Rate (IFR) for lifetime distribution, it was shown to successfully capture component wear-out, as well as the economic dependencies among the system components.

A bottom-up approach to identifying the maximum operational adaptive capacity of water resource systems to a changing climate

NASA Astrophysics Data System (ADS)

Culley, S.; Noble, S.; Yates, A.; Timbs, M.; Westra, S.; Maier, H. R.; Giuliani, M.; Castelletti, A.

2016-09-01

Many water resource systems have been designed assuming that the statistical characteristics of future inflows are similar to those of the historical record. This assumption is no longer valid due to large-scale changes in the global climate, potentially causing declines in water resource system performance, or even complete system failure. Upgrading system infrastructure to cope with climate change can require substantial financial outlay, so it might be preferable to optimize existing system performance when possible. This paper builds on decision scaling theory by proposing a bottom-up approach to designing optimal feedback control policies for a water system exposed to a changing climate. This approach not only describes optimal operational policies for a range of potential climatic changes but also enables an assessment of a system's upper limit of its operational adaptive capacity, beyond which upgrades to infrastructure become unavoidable. The approach is illustrated using the Lake Como system in Northern Italy—a regulated system with a complex relationship between climate and system performance. By optimizing system operation under different hydrometeorological states, it is shown that the system can continue to meet its minimum performance requirements for more than three times as many states as it can under current operations. Importantly, a single management policy, no matter how robust, cannot fully utilize existing infrastructure as effectively as an ensemble of flexible management policies that are updated as the climate changes.

Optimal policy for mitigating emissions in the European transport sector

NASA Astrophysics Data System (ADS)

Leduc, Sylvain; Piera, Patrizio; Sennai, Mesfun; Igor, Staritsky; Berien, Elbersen; Tijs, Lammens; Florian, Kraxner

2017-04-01

A geographic explicit techno-economic model, BeWhere (www.iiasa.ac.at/bewhere), has been developed at the European scale (Europe 28, the Balkans countries, Turkey, Moldavia and Ukraine) at a 40km grid size, to assess the potential of bioenergy from non-food feedstock. Based on the minimization of the supply chain from feedstock collection to the final energy product distribution, the model identifies the optimal bioenergy production plants in terms of spatial location, technology and capacity. The feedstock of interests are woody biomass (divided into eight types from conifers and non-conifers) and five different crop residuals. For each type of feedstock, one or multiple technologies can be applied for either heat, electricity or biofuel production. The model is run for different policy tools such as carbon cost, biofuel support, or subsidies, and the optimal mix of technologies and biomass needed is optimized to reach a production cost competitive against the actual reference system which is fossil fuel based. From this approach, the optimal mix of policy tools that can be applied country wide in Europe will be identified. The preliminary results show that high carbon tax and biofuel support contribute to the development of large scale biofuel production based on woody biomass plants mainly located in the northern part of Europe. Finally the highest emission reduction is reached with low biofuel support and high carbon tax evenly distributed in Europe.

New bounding and decomposition approaches for MILP investment problems: Multi-area transmission and generation planning under policy constraints

DOE PAGES

Munoz, F. D.; Hobbs, B. F.; Watson, J. -P.

2016-02-01

A novel two-phase bounding and decomposition approach to compute optimal and near-optimal solutions to large-scale mixed-integer investment planning problems is proposed and it considers a large number of operating subproblems, each of which is a convex optimization. Our motivating application is the planning of power transmission and generation in which policy constraints are designed to incentivize high amounts of intermittent generation in electric power systems. The bounding phase exploits Jensen’s inequality to define a lower bound, which we extend to stochastic programs that use expected-value constraints to enforce policy objectives. The decomposition phase, in which the bounds are tightened, improvesmore » upon the standard Benders’ algorithm by accelerating the convergence of the bounds. The lower bound is tightened by using a Jensen’s inequality-based approach to introduce an auxiliary lower bound into the Benders master problem. Upper bounds for both phases are computed using a sub-sampling approach executed on a parallel computer system. Numerical results show that only the bounding phase is necessary if loose optimality gaps are acceptable. But, the decomposition phase is required to attain optimality gaps. Moreover, use of both phases performs better, in terms of convergence speed, than attempting to solve the problem using just the bounding phase or regular Benders decomposition separately.« less

New bounding and decomposition approaches for MILP investment problems: Multi-area transmission and generation planning under policy constraints

DOE Office of Scientific and Technical Information (OSTI.GOV)

Munoz, F. D.; Hobbs, B. F.; Watson, J. -P.

A novel two-phase bounding and decomposition approach to compute optimal and near-optimal solutions to large-scale mixed-integer investment planning problems is proposed and it considers a large number of operating subproblems, each of which is a convex optimization. Our motivating application is the planning of power transmission and generation in which policy constraints are designed to incentivize high amounts of intermittent generation in electric power systems. The bounding phase exploits Jensen’s inequality to define a lower bound, which we extend to stochastic programs that use expected-value constraints to enforce policy objectives. The decomposition phase, in which the bounds are tightened, improvesmore » upon the standard Benders’ algorithm by accelerating the convergence of the bounds. The lower bound is tightened by using a Jensen’s inequality-based approach to introduce an auxiliary lower bound into the Benders master problem. Upper bounds for both phases are computed using a sub-sampling approach executed on a parallel computer system. Numerical results show that only the bounding phase is necessary if loose optimality gaps are acceptable. But, the decomposition phase is required to attain optimality gaps. Moreover, use of both phases performs better, in terms of convergence speed, than attempting to solve the problem using just the bounding phase or regular Benders decomposition separately.« less

Lot sizing and unequal-sized shipment policy for an integrated production-inventory system

NASA Astrophysics Data System (ADS)

Giri, B. C.; Sharma, S.

2014-05-01

This article develops a single-manufacturer single-retailer production-inventory model in which the manufacturer delivers the retailer's ordered quantity in unequal shipments. The manufacturer's production process is imperfect and it may produce some defective items during a production run. The retailer performs a screening process immediately after receiving the order from the manufacturer. The expected average total cost of the integrated production-inventory system is derived using renewal theory and a solution procedure is suggested to determine the optimal production and shipment policy. An extensive numerical study based on different sets of parameter values is conducted and the optimal results so obtained are analysed to examine the relative performance of the models under equal and unequal shipment policies.

Many-objective reservoir policy identification and refinement to reduce policy inertia and myopia in water management

NASA Astrophysics Data System (ADS)

Giuliani, M.; Herman, J. D.; Castelletti, A.; Reed, P.

2014-04-01

This study contributes a decision analytic framework to overcome policy inertia and myopia in complex river basin management contexts. The framework combines reservoir policy identification, many-objective optimization under uncertainty, and visual analytics to characterize current operations and discover key trade-offs between alternative policies for balancing competing demands and system uncertainties. The approach is demonstrated on the Conowingo Dam, located within the Lower Susquehanna River, USA. The Lower Susquehanna River is an interstate water body that has been subject to intensive water management efforts due to competing demands from urban water supply, atomic power plant cooling, hydropower production, and federally regulated environmental flows. We have identified a baseline operating policy for the Conowingo Dam that closely reproduces the dynamics of current releases and flows for the Lower Susquehanna and thus can be used to represent the preferences structure guiding current operations. Starting from this baseline policy, our proposed decision analytic framework then combines evolutionary many-objective optimization with visual analytics to discover new operating policies that better balance the trade-offs within the Lower Susquehanna. Our results confirm that the baseline operating policy, which only considers deterministic historical inflows, significantly overestimates the system's reliability in meeting the reservoir's competing demands. Our proposed framework removes this bias by successfully identifying alternative reservoir policies that are more robust to hydroclimatic uncertainties while also better addressing the trade-offs across the Conowingo Dam's multisector services.

Evaluating forest management policies by parametric linear programing

Treesearch

Daniel I. Navon; Richard J. McConnen

1967-01-01

An analytical and simulation technique, parametric linear programing explores alternative conditions and devises an optimal management plan for each condition. Its application in solving policy-decision problems in the management of forest lands is illustrated in an example.

Efficient air pollution regulation of coal-fired power in China

NASA Astrophysics Data System (ADS)

Feng, Therese

This dissertation evaluates monetary external costs of electricity generation in the People's Republic of China and implications for efficient pollution control policy. It presents an integrated assessment of environmental damages of air emissions of a representative new coal-fired plant in urban areas of north and south China. The simulation evaluates the nature and magnitude of damages in China, transboundary effects in Japan and Korea, and global greenhouse gas warming impacts. The valuation is used to identify efficient abatement policy for Chinese plants over time; evaluate benefits of differentiated policies; and consider the importance of dynamic policy. Potential annual damages of operating a 600-MW power plant without controls in China today would be 43-45 million (U.S. 1995). Annual local damages of 37-40 million far exceed transboundary or greenhouse gas damages (1.4 million and $4.6 million respectively). The largest component of damages is the risk of human mortality and chronic morbidity from long-term exposure to fine particles. Efficient pollution control minimizes the sum of abatement costs and residual unabated damages. Because monetary damages reflect sufferers' willingness to pay to avoid environmental risks, the choice of efficient controls is fundamentally tied to societal values and preferences. The optimal path for Chinese abatement moves from modest dispersion measures at present to combined dispersion and emission controls approaching those of current-day United States, by 2050. The inclusion of transboundary and greenhouse damages does not substantively alter local policies. Welfare benefits are gained by differentiating abatement policy by pollutant, meteorological parameters, and by population density. An analysis of optimal one-time investment in abatement for a plant in a growing economy suggests that some investment is optimal at all incomes but no single level of abatement is suitable for all economies. Forward-looking policy anticipates higher future values for environmental services and provides distinct welfare advantages over time compared to myopic or static policies-such as the imposition of developed country standards-especially if aggregate capacity growth is considered.

Throughput-optimal scheduling for broadcast channels

NASA Astrophysics Data System (ADS)

Eryilmaz, Atilla; Srikant, Rayadurgam; Perkins, James R.

2001-07-01

In this paper, we consider a degraded Gaussian broadcast channel, where the transmitter maintains separate queues for each receiver. We present throughput optimal policies that stabilize the queues without knowing the statistics of the arrival processes to these queues.

Optimizing Chemical Reactions with Deep Reinforcement Learning

PubMed Central

2017-01-01

Deep reinforcement learning was employed to optimize chemical reactions. Our model iteratively records the results of a chemical reaction and chooses new experimental conditions to improve the reaction outcome. This model outperformed a state-of-the-art blackbox optimization algorithm by using 71% fewer steps on both simulations and real reactions. Furthermore, we introduced an efficient exploration strategy by drawing the reaction conditions from certain probability distributions, which resulted in an improvement on regret from 0.062 to 0.039 compared with a deterministic policy. Combining the efficient exploration policy with accelerated microdroplet reactions, optimal reaction conditions were determined in 30 min for the four reactions considered, and a better understanding of the factors that control microdroplet reactions was reached. Moreover, our model showed a better performance after training on reactions with similar or even dissimilar underlying mechanisms, which demonstrates its learning ability. PMID:29296675

Maintaining environmental quality while expanding biomass production: Sub-regional U.S. policy simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Egbendewe-Mondzozo, Aklesso; Swinton, S.; Izaurralde, Roberto C.

2013-03-01

This paper evaluates environmental policy effects on ligno-cellulosic biomass production and environ- mental outcomes using an integrated bioeconomic optimization model. The environmental policy integrated climate (EPIC) model is used to simulate crop yields and environmental indicators in current and future potential bioenergy cropping systems based on weather, topographic and soil data. The crop yield and environmental outcome parameters from EPIC are combined with biomass transport costs and economic parameters in a representative farmer profit-maximizing mathematical optimization model. The model is used to predict the impact of alternative policies on biomass production and environmental outcomes. We find that without environmental policy,more » rising biomass prices initially trigger production of annual crop residues, resulting in increased greenhouse gas emissions, soil erosion, and nutrient losses to surface and ground water. At higher biomass prices, perennial bioenergy crops replace annual crop residues as biomass sources, resulting in lower environmental impacts. Simulations of three environmental policies namely a carbon price, a no-till area subsidy, and a fertilizer tax reveal that only the carbon price policy systematically mitigates environmental impacts. The fertilizer tax is ineffectual and too costly to farmers. The no-till subsidy is effective only at low biomass prices and is too costly to government.« less

Effect of conserving habitat for biodiversity on optimal management of non-industrial private forests in Florida

Treesearch

Jagannadha R. Matta; Janaki R. R. Alavalapati; George A. Stainback

2008-01-01

Healthy forests and enhanced habitat for wildlife is a growing concern among public and policy makers. These concerns have led to substantial interest in promoting various regulatory and voluntary compliance policies to further biodiversity on private forests. These policies, however, might result in additional cost to forestland owners. In this paper, we estimate the...

Local Bifurcations and Optimal Theory in a Delayed Predator-Prey Model with Threshold Prey Harvesting

NASA Astrophysics Data System (ADS)

Tankam, Israel; Tchinda Mouofo, Plaire; Mendy, Abdoulaye; Lam, Mountaga; Tewa, Jean Jules; Bowong, Samuel

2015-06-01

We investigate the effects of time delay and piecewise-linear threshold policy harvesting for a delayed predator-prey model. It is the first time that Holling response function of type III and the present threshold policy harvesting are associated with time delay. The trajectories of our delayed system are bounded; the stability of each equilibrium is analyzed with and without delay; there are local bifurcations as saddle-node bifurcation and Hopf bifurcation; optimal harvesting is also investigated. Numerical simulations are provided in order to illustrate each result.

PATENTS AND RESEARCH INVESTMENTS: ASSESSING THE EMPIRICAL EVIDENCE.

PubMed

Budish, Eric; Roin, Benjamin N; Williams, Heidi L

2016-05-01

A well-developed theoretical literature - dating back at least to Nordhaus (1969) - has analyzed optimal patent policy design. We re-present the core trade-off of the Nordhaus model and highlight an empirical question which emerges from the Nordhaus framework as a key input into optimal patent policy design: namely, what is the elasticity of R&D investment with respect to the patent term? We then review the - surprisingly small - body of empirical evidence that has been developed on this question over the nearly half century since the publication of Nordhaus's book.

Exploration and Exploitation During Sequential Search

PubMed Central

Dam, Gregory; Körding, Konrad

2012-01-01

When we learn how to throw darts we adjust how we throw based on where the darts stick. Much of skill learning is computationally similar in that we learn using feedback obtained after the completion of individual actions. We can formalize such tasks as a search problem; among the set of all possible actions, find the action that leads to the highest reward. In such cases our actions have two objectives: we want to best utilize what we already know (exploitation), but we also want to learn to be more successful in the future (exploration). Here we tested how participants learn movement trajectories where feedback is provided as a monetary reward that depends on the chosen trajectory. We mathematically derived the optimal search policy for our experiment using decision theory. The search behavior of participants is well predicted by an ideal searcher model that optimally combines exploration and exploitation. PMID:21585479

Impacts of subsidy policies on vaccination decisions in contact networks

NASA Astrophysics Data System (ADS)

Zhang, Hai-Feng; Wu, Zhi-Xi; Xu, Xiao-Ke; Small, Michael; Wang, Lin; Wang, Bing-Hong

2013-07-01

To motivate more people to participate in vaccination campaigns, various subsidy policies are often supplied by government and the health sectors. However, these external incentives may also alter the vaccination decisions of the broader public, and hence the choice of incentive needs to be carefully considered. Since human behavior and the networking-constrained interactions among individuals significantly impact the evolution of an epidemic, here we consider the voluntary vaccination on human contact networks. To this end, two categories of typical subsidy policies are considered: (1) under the free subsidy policy, the total amount of subsidy is distributed to a certain fraction of individual and who are vaccinated without personal cost, and (2) under the partial-offset subsidy policy, each vaccinated person is offset by a certain amount of subsidy. A vaccination decision model based on evolutionary game theory is established to study the effects of these different subsidy policies on disease control. Simulations suggest that, because the partial-offset subsidy policy encourages more people to take vaccination, its performance is significantly better than that of the free subsidy policy. However, an interesting phenomenon emerges in the partial-offset scenario: with limited amount of total subsidy, a moderate subsidy rate for each vaccinated individual can guarantee the group-optimal vaccination, leading to the maximal social benefits, while such an optimal phenomenon is not evident for the free subsidy scenario.

Optimization based on benefit of regional energy suppliers of distributed generation in active distribution network

NASA Astrophysics Data System (ADS)

Huo, Xianxu; Li, Guodong; Jiang, Ling; Wang, Xudong

2017-08-01

With the development of electricity market, distributed generation (DG) technology and related policies, regional energy suppliers are encouraged to build DG. Under this background, the concept of active distribution network (ADN) is put forward. In this paper, a bi-level model of intermittent DG considering benefit of regional energy suppliers is proposed. The objective of the upper level is the maximization of benefit of regional energy suppliers. On this basis, the lower level is optimized for each scene. The uncertainties of DG output and load of users, as well as four active management measures, which include demand-side management, curtailing the output power of DG, regulating reactive power compensation capacity and regulating the on-load tap changer, are considered. Harmony search algorithm and particle swarm optimization are combined as a hybrid strategy to solve the model. This model and strategy are tested with IEEE-33 node system, and results of case study indicate that the model and strategy successfully increase the capacity of DG and benefit of regional energy suppliers.

Population-wide folic acid fortification and preterm birth: testing the folate depletion hypothesis.

PubMed

Naimi, Ashley I; Auger, Nathalie

2015-04-01

We assess whether population-wide folic acid fortification policies were followed by a reduction of preterm and early-term birth rates in Québec among women with short and optimal interpregnancy intervals. We extracted birth certificate data for 1.3 million births between 1981 and 2010 to compute age-adjusted preterm and early-term birth rates stratified by short and optimal interpregnancy intervals. We used Joinpoint regression to detect changes in the preterm and early term birth rates and assess whether these changes coincide with the implementation of population-wide folic acid fortification. A change in the preterm birth rate occurred in 2000 among women with short (95% confidence interval [CI] = 1994, 2005) and optimal (95% CI = 1995, 2008) interpregnancy intervals. Changes in early term birth rates did not coincide with the implementation of folic acid fortification. Our results do not indicate a link between folic acid fortification and early term birth but suggest an improvement in preterm birth rates after implementation of a nationwide folic acid fortification program.

Statistical-QoS Guaranteed Energy Efficiency Optimization for Energy Harvesting Wireless Sensor Networks

PubMed Central

Cheng, Wenchi; Zhang, Hailin

2017-01-01

Energy harvesting, which offers a never-ending energy supply, has emerged as a prominent technology to prolong the lifetime and reduce costs for the battery-powered wireless sensor networks. However, how to improve the energy efficiency while guaranteeing the quality of service (QoS) for energy harvesting based wireless sensor networks is still an open problem. In this paper, we develop statistical delay-bounded QoS-driven power control policies to maximize the effective energy efficiency (EEE), which is defined as the spectrum efficiency under given specified QoS constraints per unit harvested energy, for energy harvesting based wireless sensor networks. For the battery-infinite wireless sensor networks, our developed QoS-driven power control policy converges to the Energy harvesting Water Filling (E-WF) scheme and the Energy harvesting Channel Inversion (E-CI) scheme under the very loose and stringent QoS constraints, respectively. For the battery-finite wireless sensor networks, our developed QoS-driven power control policy becomes the Truncated energy harvesting Water Filling (T-WF) scheme and the Truncated energy harvesting Channel Inversion (T-CI) scheme under the very loose and stringent QoS constraints, respectively. Furthermore, we evaluate the outage probabilities to theoretically analyze the performance of our developed QoS-driven power control policies. The obtained numerical results validate our analysis and show that our developed optimal power control policies can optimize the EEE over energy harvesting based wireless sensor networks. PMID:28832509

Statistical-QoS Guaranteed Energy Efficiency Optimization for Energy Harvesting Wireless Sensor Networks.

PubMed

Gao, Ya; Cheng, Wenchi; Zhang, Hailin

2017-08-23

Energy harvesting, which offers a never-ending energy supply, has emerged as a prominent technology to prolong the lifetime and reduce costs for the battery-powered wireless sensor networks. However, how to improve the energy efficiency while guaranteeing the quality of service (QoS) for energy harvesting based wireless sensor networks is still an open problem. In this paper, we develop statistical delay-bounded QoS-driven power control policies to maximize the effective energy efficiency (EEE), which is defined as the spectrum efficiency under given specified QoS constraints per unit harvested energy, for energy harvesting based wireless sensor networks. For the battery-infinite wireless sensor networks, our developed QoS-driven power control policy converges to the Energy harvesting Water Filling (E-WF) scheme and the Energy harvesting Channel Inversion (E-CI) scheme under the very loose and stringent QoS constraints, respectively. For the battery-finite wireless sensor networks, our developed QoS-driven power control policy becomes the Truncated energy harvesting Water Filling (T-WF) scheme and the Truncated energy harvesting Channel Inversion (T-CI) scheme under the very loose and stringent QoS constraints, respectively. Furthermore, we evaluate the outage probabilities to theoretically analyze the performance of our developed QoS-driven power control policies. The obtained numerical results validate our analysis and show that our developed optimal power control policies can optimize the EEE over energy harvesting based wireless sensor networks.

A survey of georgia adult protective service staff: implications for older adult injury prevention and policy.

PubMed

Strasser, Sheryl M; Kerr, Judith; King, Patricia S; Payne, Brian; Beddington, Sarah; Pendrick, Danielle; Leyda, Elizabeth; McCarty, Frances

2011-07-01

The aging population is a rapidly growing demographic. Isolation and limited autonomy render many of the elderly vulnerable to abuse, neglect and exploitation. As the population grows, so does the need for Adult Protective Services (APS). This study was conducted to examine current knowledge of older adult protection laws in Georgia among APS staff and to identify training opportunities to better prepare the APS workforce in case detection and intervention. The Georgia State University Institute of Public Health faculty developed a primary survey in partnership with the Georgia Division of Aging Services' leadership to identify key training priority issues for APS caseworkers and investigators. A 47-item electronic questionnaire was delivered to all APS employees via work-issued email accounts. We conducted descriptive analyses, t-tests and chi-square analyses to determine APS employees' baseline knowledge of Georgia's elder abuse policies, laws and practices, as well as examine associations of age, ethnicity, and educational attainment with knowledge. We used a p-value of 0.05 and 95% confidence intervals to determine statistical significance of analyses performed. Ninety-two out of 175 APS staff responded to the survey (53% response rate). The majority of respondents were Caucasian (56%) women (92%). For over half the survey items, paired sample t-tests revealed significant differences between what APS staff reported as known and what APS staff members indicated they needed to know more about in terms of elder abuse and current policies. Chi-square tests revealed that non-Caucasians significantly preferred video conferencing as a training format (44% compared to 18%), [χ(2)(1) = 7.102, p < .008], whereas Caucasians preferred asynchronous online learning formats (55% compared to 28%) [χ(2)(1) =5.951, p < .015]. Results from this study provide the Georgia Division of Aging with insight into specific policy areas that are not well understood by APS staff. Soliciting input from intended trainees allows public health educators to tailor and improve training sessions. Trainee input may result in optimization of policy implementation, which may result in greater injury prevention and protection of older adults vulnerable to abuse, neglect and exploitation.

Optimizing Water Use and Hydropower Production in Operational Reservoir System Scheduling with RiverWare

NASA Astrophysics Data System (ADS)

Magee, T. M.; Zagona, E. A.

2017-12-01

Practical operational optimization of multipurpose reservoir systems is challenging for several reasons. Each purpose has its own constraints which may conflict with those of other purposes. While hydropower generation typically provides the bulk of the revenue, it is also among the lowest priority purposes. Each river system has important details that are specific to the location such as hydrology, reservoir storage capacity, physical limitations, bottlenecks, and the continuing evolution of operational policy. In addition, reservoir operations models include discrete, nonlinear, and nonconvex physical processes and if-then operating policies. Typically, the forecast horizon for scheduling needs to be extended far into the future to avoid near term (e.g., a few hours or a day) scheduling decisions that result in undesirable future states; this makes the computational effort much larger than may be expected. Put together, these challenges lead to large and customized mathematical optimization problems which must be solved efficiently to be of practical use. In addition, the solution process must be robust in an operational setting. We discuss a unique modeling approach in RiverWare that meets these challenges in an operational setting. The approach combines a Preemptive Linear Goal Programming optimization model to handle prioritized policies complimented by preprocessing and postprocessing with Rulebased Simulation to improve the solution with regard to nonlinearities, discrete issues, and if-then logic. An interactive policy language with a graphical user interface allows modelers to customize both the optimization and simulation based on the unique aspects of the policy for their system while the routine physical aspect of operations are modeled automatically. The modeler is aided by a set of compiled predefined functions and functions shared by other modelers. We illustrate the success of the approach with examples from daily use at the Tennessee Valley Authority, the Bonneville Power Administration, and public utility districts on the Mid-Columbia River. We discuss recent innovations to improve solution quality, robustness, and performance for these systems. We conclude with new modeling challenges to extend the modeling approach to other uses.

Climate change mitigation: comparative assessment of Malaysian and ASEAN scenarios.

PubMed

Rasiah, Rajah; Ahmed, Adeel; Al-Amin, Abul Quasem; Chenayah, Santha

2017-01-01

This paper analyses empirically the optimal climate change mitigation policy of Malaysia with the business as usual scenario of ASEAN to compare their environmental and economic consequences over the period 2010-2110. A downscaling empirical dynamic model is constructed using a dual multidisciplinary framework combining economic, earth science, and ecological variables to analyse the long-run consequences. The model takes account of climatic variables, including carbon cycle, carbon emission, climatic damage, carbon control, carbon concentration, and temperature. The results indicate that without optimal climate policy and action, the cumulative cost of climate damage for Malaysia and ASEAN as a whole over the period 2010-2110 would be MYR40.1 trillion and MYR151.0 trillion, respectively. Under the optimal policy, the cumulative cost of climatic damage for Malaysia would fall to MYR5.3 trillion over the 100 years. Also, the additional economic output of Malaysia will rise from MYR2.1 billion in 2010 to MYR3.6 billion in 2050 and MYR5.5 billion in 2110 under the optimal climate change mitigation scenario. The additional economic output for ASEAN would fall from MYR8.1 billion in 2010 to MYR3.2 billion in 2050 before rising again slightly to MYR4.7 billion in 2110 in the business as usual ASEAN scenario.

Methane mitigation timelines to inform energy technology evaluation

NASA Astrophysics Data System (ADS)

Roy, Mandira; Edwards, Morgan R.; Trancik, Jessika E.

2015-11-01

Energy technologies emitting differing proportions of methane (CH4) and carbon dioxide (CO2) vary significantly in their relative climate impacts over time, due to the distinct atmospheric lifetimes and radiative efficiencies of the two gases. Standard technology comparisons using the global warming potential (GWP) with a fixed time horizon do not account for the timing of emissions in relation to climate policy goals. Here we develop a portfolio optimization model that incorporates changes in technology impacts based on the temporal proximity of emissions to a radiative forcing (RF) stabilization target. An optimal portfolio, maximizing allowed energy consumption while meeting the RF target, is obtained by year-wise minimization of the marginal RF impact in an intended stabilization year. The optimal portfolio calls for using certain higher-CH4-emitting technologies prior to an optimal switching year, followed by CH4-light technologies as the stabilization year approaches. We apply the model to evaluate transportation technology pairs and find that accounting for dynamic emissions impacts, in place of using the static GWP, can result in CH4 mitigation timelines and technology transitions that allow for significantly greater energy consumption while meeting a climate policy target. The results can inform the forward-looking evaluation of energy technologies by engineers, private investors, and policy makers.

77 FR 38751 - Codification of Animal Testing Policy

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-29

... Animal Testing Policy AGENCY: Consumer Product Safety Commission. ACTION: Proposed Statement of Policy on Animal Testing SUMMARY: The Consumer Product Safety Commission (CPSC or Commission) proposes to codify its statement of policy on animal testing, as amended, which was previously published in the Federal...

HIV testing for pregnant women: a rights-based analysis of national policies.

PubMed

King, Elizabeth J; Maman, Suzanne; Wyckoff, Sarah C; Pierce, Matthew W; Groves, Allison K

2013-01-01

Ethical and human rights concerns have been expressed regarding the global shift in policies on HIV testing of pregnant women. The main purpose of this research was to conduct a policy analysis using a human rights-based approach of national policies for HIV testing of pregnant women. We collected HIV testing policies from 19 countries including: Cambodia, China, Guyana, Haiti, India, Jamaica, Kenya, Moldova, Papua New Guinea, Russian Federation, South Africa, Sudan, Swaziland, Tanzania, Ukraine, United States, Uzbekistan, Zambia and Zimbabwe. We analysed the HIV testing policies using a standardised framework that focused on government obligations to respect, protect and fulfil. Our results highlight the need for more attention to issues of pregnant women's autonomy in consenting to HIV testing, confidentiality in antenatal care settings and provision of counselling and care services. We conclude with a discussion about potential implications of the current testing policies and provide recommendations for ways that HIV testing policies can more effectively uphold the human rights of pregnant women.

Hierarchical prisoner’s dilemma in hierarchical game for resource competition

NASA Astrophysics Data System (ADS)

Fujimoto, Yuma; Sagawa, Takahiro; Kaneko, Kunihiko

2017-07-01

Dilemmas in cooperation are one of the major concerns in game theory. In a public goods game, each individual cooperates by paying a cost or defecting without paying it, and receives a reward from the group out of the collected cost. Thus, defecting is beneficial for each individual, while cooperation is beneficial for the group. Now, groups (say, countries) consisting of individuals also play games. To study such a multi-level game, we introduce a hierarchical game in which multiple groups compete for limited resources by utilizing the collected cost in each group, where the power to appropriate resources increases with the population of the group. Analyzing this hierarchical game, we found a hierarchical prisoner’s dilemma, in which groups choose the defecting policy (say, armament) as a Nash strategy to optimize each group’s benefit, while cooperation optimizes the total benefit. On the other hand, for each individual, refusing to pay the cost (say, tax) is a Nash strategy, which turns out to be a cooperation policy for the group, thus leading to a hierarchical dilemma. Here the group reward increases with the group size. However, we find that there exists an optimal group size that maximizes the individual payoff. Furthermore, when the population asymmetry between two groups is large, the smaller group will choose a cooperation policy (say, disarmament) to avoid excessive response from the larger group, and the prisoner’s dilemma between the groups is resolved. Accordingly, the relevance of this hierarchical game on policy selection in society and the optimal size of human or animal groups are discussed.

An Approximate Dynamic Programming Mode for Optimal MEDEVAC Dispatching

DTIC Science & Technology

2015-03-26

over the myopic policy. This indicates the ADP policy is efficiently managing resources by 28 not immediately sending the nearest available MEDEVAC...DISPATCHING THESIS Presented to the Faculty Department of Operational Sciences Graduate School of Engineering and Management Air Force Institute of Technology...medical evacuation (MEDEVAC) dispatch policies. To solve the MDP, we apply an ap- proximate dynamic programming (ADP) technique. The problem of deciding

Optimal birth control of age-dependent competitive species

NASA Astrophysics Data System (ADS)

He, Ze-Rong

2005-05-01

We study optimal birth policies for two age-dependent populations in a competing system, which is controlled by fertilities. New results on problems with free final time and integral phase constraints are presented, and the approximate controllability of system is discussed.

The financial consequences of lost demand and reducing boarding in hospital emergency departments.

PubMed

Pines, Jesse M; Batt, Robert J; Hilton, Joshua A; Terwiesch, Christian

2011-10-01

Some have suggested that emergency department (ED) boarding is prevalent because it maximizes revenue as hospitals prioritize non-ED admissions, which reimburse higher than ED admissions. We explore the revenue implications to the overall hospital of reducing boarding in the ED. We quantified the revenue effect of reducing boarding-the balance of higher ED demand and the reduction of non-ED admissions-using financial modeling informed by regression analysis and discrete-event simulation with data from 1 inner-city teaching hospital during 2 years (118,000 ED visits, 22% ED admission rate, 7% left without being seen rate, 36,000 non-ED admissions). Various inpatient bed management policies for reducing non-ED admissions were tested. Non-ED admissions generated more revenue than ED admissions ($4,118 versus $2,268 per inpatient day). A 1-hour reduction in ED boarding time would result in $9,693 to $13,298 of additional daily revenue from capturing left without being seen and diverted ambulance patients. To accommodate this demand, we found that simulated management policies in which non-ED admissions are reduced without consideration to hospital capacity (ie, static policies) mostly did not result in higher revenue. Many dynamic policies requiring cancellation of various proportions of non-ED admissions when the hospital reaches specific trigger points increased revenue. The optimal strategies tested resulted in an estimated $2.7 million and $3.6 in net revenue per year, depending on whether left without being seen patients were assumed to be outpatients or mirrored ambulatory admission rates, respectively. Dynamic inpatient bed management in inner-city teaching hospitals in which non-ED admissions are occasionally reduced to ensure that EDs have reduced boarding times is a financially attractive strategy. Copyright © 2010 American College of Emergency Physicians. Published by Mosby, Inc. All rights reserved.

One size fits all? An assessment tool for solid waste management at local and national levels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Broitman, Dani, E-mail: danib@techunix.technion.ac.il; Ayalon, Ofira; Kan, Iddo

2012-10-15

Highlights: Black-Right-Pointing-Pointer Waste management schemes are generally implemented at national or regional level. Black-Right-Pointing-Pointer Local conditions characteristics and constraints are often neglected. Black-Right-Pointing-Pointer We developed an economic model able to compare multi-level waste management options. Black-Right-Pointing-Pointer A detailed test case with real economic data and a best-fit scenario is described. Black-Right-Pointing-Pointer Most efficient schemes combine clear National directives with local level flexibility. - Abstract: As environmental awareness rises, integrated solid waste management (WM) schemes are increasingly being implemented all over the world. The different WM schemes usually address issues such as landfilling restrictions (mainly due to methane emissions and competingmore » land use), packaging directives and compulsory recycling goals. These schemes are, in general, designed at a national or regional level, whereas local conditions and constraints are sometimes neglected. When national WM top-down policies, in addition to setting goals, also dictate the methods by which they are to be achieved, local authorities lose their freedom to optimize their operational WM schemes according to their specific characteristics. There are a myriad of implementation options at the local level, and by carrying out a bottom-up approach the overall national WM system will be optimal on economic and environmental scales. This paper presents a model for optimizing waste strategies at a local level and evaluates this effect at a national level. This is achieved by using a waste assessment model which enables us to compare both the economic viability of several WM options at the local (single municipal authority) level, and aggregated results for regional or national levels. A test case based on various WM approaches in Israel (several implementations of mixed and separated waste) shows that local characteristics significantly influence WM costs, and therefore the optimal scheme is one under which each local authority is able to implement its best-fitting mechanism, given that national guidelines are kept. The main result is that strict national/regional WM policies may be less efficient, unless some type of local flexibility is implemented. Our model is designed both for top-down and bottom-up assessment, and can be easily adapted for a wide range of WM option comparisons at different levels.« less

Derivation of optimal joint operating rules for multi-purpose multi-reservoir water-supply system

NASA Astrophysics Data System (ADS)

Tan, Qiao-feng; Wang, Xu; Wang, Hao; Wang, Chao; Lei, Xiao-hui; Xiong, Yi-song; Zhang, Wei

2017-08-01

The derivation of joint operating policy is a challenging task for a multi-purpose multi-reservoir system. This study proposed an aggregation-decomposition model to guide the joint operation of multi-purpose multi-reservoir system, including: (1) an aggregated model based on the improved hedging rule to ensure the long-term water-supply operating benefit; (2) a decomposed model to allocate the limited release to individual reservoirs for the purpose of maximizing the total profit of the facing period; and (3) a double-layer simulation-based optimization model to obtain the optimal time-varying hedging rules using the non-dominated sorting genetic algorithm II, whose objectives were to minimize maximum water deficit and maximize water supply reliability. The water-supply system of Li River in Guangxi Province, China, was selected for the case study. The results show that the operating policy proposed in this study is better than conventional operating rules and aggregated standard operating policy for both water supply and hydropower generation due to the use of hedging mechanism and effective coordination among multiple objectives.

Suboptimal and optimal order policies for fixed and varying replenishment interval with declining market

NASA Astrophysics Data System (ADS)

Yu, Jonas C. P.; Wee, H. M.; Yang, P. C.; Wu, Simon

2016-06-01

One of the supply chain risks for hi-tech products is the result of rapid technological innovation; it results in a significant decline in the selling price and demand after the initial launch period. Hi-tech products include computers and communication consumer's products. From a practical standpoint, a more realistic replenishment policy is needed to consider the impact of risks; especially when some portions of shortages are lost. In this paper, suboptimal and optimal order policies with partial backordering are developed for a buyer when the component cost, the selling price, and the demand rate decline at a continuous rate. Two mathematical models are derived and discussed: one model has the suboptimal solution with the fixed replenishment interval and a simpler computational process; the other one has the optimal solution with the varying replenishment interval and a more complicated computational process. The second model results in more profit. Numerical examples are provided to illustrate the two replenishment models. Sensitivity analysis is carried out to investigate the relationship between the parameters and the net profit.

Advances in liver transplantation allocation systems.

PubMed

Schilsky, Michael L; Moini, Maryam

2016-03-14

With the growing number of patients in need of liver transplantation, there is a need for adopting new and modifying existing allocation policies that prioritize patients for liver transplantation. Policy should ensure fair allocation that is reproducible and strongly predictive of best pre and post transplant outcomes while taking into account the natural history of the potential recipients liver disease and its complications. There is wide acceptance for allocation policies based on urgency in which the sickest patients on the waiting list with the highest risk of mortality receive priority. Model for end-stage liver disease and Child-Turcotte-Pugh scoring system, the two most universally applicable systems are used in urgency-based prioritization. However, other factors must be considered to achieve optimal allocation. Factors affecting pre-transplant patient survival and the quality of the donor organ also affect outcome. The optimal system should have allocation prioritization that accounts for both urgency and transplant outcome. We reviewed past and current liver allocation systems with the aim of generating further discussion about improvement of current policies.

The application of defaults to optimize parents' health-based choices for children.

PubMed

Loeb, Katharine L; Radnitz, Cynthia; Keller, Kathleen; Schwartz, Marlene B; Marcus, Sue; Pierson, Richard N; Shannon, Michael; DeLaurentis, Danielle

2017-06-01

Optimal defaults is a compelling model from behavioral economics and the psychology of human decision-making, designed to shape or "nudge" choices in a positive direction without fundamentally restricting options. The current study aimed to test the effectiveness of optimal (less obesogenic) defaults and parent empowerment priming on health-based decisions with parent-child (ages 3-8) dyads in a community-based setting. Two proof-of-concept experiments (one on breakfast food selections and one on activity choice) were conducted comparing the main and interactive effects of optimal versus suboptimal defaults, and parent empowerment priming versus neutral priming, on parents' health-related choices for their children. We hypothesized that in each experiment, making the default option more optimal will lead to more frequent health-oriented choices, and that priming parents to be the ultimate decision-makers on behalf of their child's health will potentiate this effect. Results show that in both studies, default condition, but not priming condition or the interaction between default and priming, significantly predicted choice (healthier vs. less healthy option). There was also a significant main effect for default condition (and no effect for priming condition or the interaction term) on the quantity of healthier food children consumed in the breakfast experiment. These pilot studies demonstrate that optimal defaults can be practicably implemented to improve parents' food and activity choices for young children. Results can inform policies and practices pertaining to obesogenic environmental factors in school, restaurant, and home environments. Copyright © 2017 Elsevier Ltd. All rights reserved.

Towards a hierarchical optimization modeling framework for the evaluation and construction of spatially targeted incentive policies to promote green infrastructure (GI) amidst budgetary, compliance and GI-effectiveness uncertainties

EPA Science Inventory

Background:Bilevel optimization has been recognized as a 2-player Stackelberg game where players are represented as leaders and followers and each pursue their own set of objectives. Hierarchical optimization problems, which are a generalization of bilevel, are especially difficu...

An interprovincial cooperative game model for air pollution control in China.

PubMed

Xue, Jian; Zhao, Laijun; Fan, Longzhen; Qian, Ying

2015-07-01

The noncooperative air pollution reduction model (NCRM) that is currently adopted in China to manage air pollution reduction of each individual province has inherent drawbacks. In this paper, we propose a cooperative air pollution reduction game model (CRM) that consists of two parts: (1) an optimization model that calculates the optimal pollution reduction quantity for each participating province to meet the joint pollution reduction goal; and (2) a model that distribute the economic benefit of the cooperation (i.e., pollution reduction cost saving) among the provinces in the cooperation based on the Shapley value method. We applied the CRM to the case of SO2 reduction in the Beijing-Tianjin-Hebei region in China. The results, based on the data from 2003-2009, show that cooperation helps lower the overall SO2 pollution reduction cost from 4.58% to 11.29%. Distributed across the participating provinces, such a cost saving from interprovincial cooperation brings significant benefits to each local government and stimulates them for further cooperation in pollution reduction. Finally, sensitivity analysis is performed using the year 2009 data to test the parameters' effects on the pollution reduction cost savings. China is increasingly facing unprecedented pressure for immediate air pollution control. The current air pollution reduction policy does not allow cooperation and is less efficient. In this paper we developed a cooperative air pollution reduction game model that consists of two parts: (1) an optimization model that calculates the optimal pollution reduction quantity for each participating province to meet the joint pollution reduction goal; and (2) a model that distributes the cooperation gains (i.e., cost reduction) among the provinces in the cooperation based on the Shapley value method. The empirical case shows that such a model can help improve efficiency in air pollution reduction. The result of the model can serve as a reference for Chinese government pollution reduction policy design.

Optimization of Multiple Related Negotiation through Multi-Negotiation Network

NASA Astrophysics Data System (ADS)

Ren, Fenghui; Zhang, Minjie; Miao, Chunyan; Shen, Zhiqi

In this paper, a Multi-Negotiation Network (MNN) and a Multi- Negotiation Influence Diagram (MNID) are proposed to optimally handle Multiple Related Negotiations (MRN) in a multi-agent system. Most popular, state-of-the-art approaches perform MRN sequentially. However, a sequential procedure may not optimally execute MRN in terms of maximizing the global outcome, and may even lead to unnecessary losses in some situations. The motivation of this research is to use a MNN to handle MRN concurrently so as to maximize the expected utility of MRN. Firstly, both the joint success rate and the joint utility by considering all related negotiations are dynamically calculated based on a MNN. Secondly, by employing a MNID, an agent's possible decision on each related negotiation is reflected by the value of expected utility. Lastly, through comparing expected utilities between all possible policies to conduct MRN, an optimal policy is generated to optimize the global outcome of MRN. The experimental results indicate that the proposed approach can improve the global outcome of MRN in a successful end scenario, and avoid unnecessary losses in an unsuccessful end scenario.

Optimizing Input/Output Using Adaptive File System Policies

NASA Technical Reports Server (NTRS)

Madhyastha, Tara M.; Elford, Christopher L.; Reed, Daniel A.

1996-01-01

Parallel input/output characterization studies and experiments with flexible resource management algorithms indicate that adaptivity is crucial to file system performance. In this paper we propose an automatic technique for selecting and refining file system policies based on application access patterns and execution environment. An automatic classification framework allows the file system to select appropriate caching and pre-fetching policies, while performance sensors provide feedback used to tune policy parameters for specific system environments. To illustrate the potential performance improvements possible using adaptive file system policies, we present results from experiments involving classification-based and performance-based steering.

On optimal control problem for conservation law modelling one class of highly re-entrant production systems

NASA Astrophysics Data System (ADS)

D'Apice, Ciro; Kogut, Peter I.

2017-07-01

We discuss the optimal control problem stated as the minimization in the L2-sense of the mismatch between the actual out-flux and a demand forecast for a hyperbolic conservation law that models a highly re-entrant production system. The output of the factory is described as a function of the work in progress and the position of the so-called push-pull point (PPP) where we separate the beginning of the factory employing a push policy from the end of the factory, which uses a pull policy.

Awareness and perceived fairness of option B+ in Malawi: a population-level perspective

PubMed Central

Yeatman, Sara; Trinitapoli, Jenny

2017-01-01

Abstract Introduction: Policies for rationing antiretroviral therapy (ART) have been subject to on-going ethical debates. Introduced in Malawi in 2011, Option B+ prioritized HIV-positive pregnant women for lifelong ART regardless of the underlying state of their immune system, shifting the logic of allocation away from medical eligibility. Despite the rapid expansion of this policy, we know little about how it has been understood and interpreted by the people it affects. Methods: We assessed awareness and perceived fairness of the prioritization system for ART among a population-based sample of young women (n = 1440) and their partners (n = 574) in southern Malawi. We use a card-sort technique to elicit understandings of who gets ART under Option B+ and who should be prioritized, and we compare perceptions to actual ART policy using sequence analysis and optimal matching. We then use ordered logistic regression to identify the factors associated with policy awareness. Results: In 2015, only 30.7% of women and 21.1% of male partners understood how ART was being distributed. There was widespread confusion around whether otherwise healthy HIV-positive pregnant women could access ART under Option B + . Nonetheless, more young adults thought that the fairest policy should prioritize such women than believed the actual policy did. Women who were older, more educated or had recently engaged with the health system through antenatal care or ART had more accurate understandings of Option B + . Among men, policy awareness was lower, and was patterned only by education. Conclusions: Although most respondents were unaware that Option B+ afforded ART access to healthy-pregnant women, Malawians support the prioritization of pregnant women. Countries adopting Option B+ or other new ART policies such as universal test-and-treat should communicate the policies and their rationales to the public – such transparency would be more consistent with a fair and ethical process and could additionally serve to clarify confusion and enhance retention.​​ PMID:28362070

Awareness and perceived fairness of Option B+ in Malawi: A population-level perspective

PubMed

Yeatman, Sara; Trinitapoli, Jenny

2017-03-08

Policies for rationing antiretroviral therapy (ART) have been subject to on-going ethical debates. Introduced in Malawi in 2011, Option B+ prioritized HIV-positive pregnant women for lifelong ART regardless of the underlying state of their immune system, shifting the logic of allocation away from medical eligibility. Despite the rapid expansion of this policy, we know little about how it has been understood and interpreted by the people it affects. We assessed awareness and perceived fairness of the prioritization system for ART among a population-based sample of young women (n = 1440) and their partners (n = 574) in southern Malawi. We use a card-sort technique to elicit understandings of who gets ART under Option B+ and who should be prioritized, and we compare perceptions to actual ART policy using sequence analysis and optimal matching. We then use ordered logistic regression to identify the factors associated with policy awareness. In 2015, only 30.7% of women and 21.1% of male partners understood how ART was being distributed. There was widespread confusion around whether otherwise healthy HIV-positive pregnant women could access ART under Option B + . Nonetheless, more young adults thought that the fairest policy should prioritize such women than believed the actual policy did. Women who were older, more educated or had recently engaged with the health system through antenatal care or ART had more accurate understandings of Option B + . Among men, policy awareness was lower, and was patterned only by education. Although most respondents were unaware that Option B+ afforded ART access to healthy-pregnant women, Malawians support the prioritization of pregnant women. Countries adopting Option B+ or other new ART policies such as universal test-and-treat should communicate the policies and their rationales to the public - such transparency would be more consistent with a fair and ethical process and could additionally serve to clarify confusion and enhance retention.​​.

Infection control in cystic fibrosis: barriers to implementation and ideas for improvement.

PubMed

Saiman, Lisa; Garber, Elizabeth

2009-11-01

This review will focus on recent research documenting baseline adherence to infection control recommendations and barriers to their implementation as experienced by multidisciplinary cystic fibrosis (CF) care providers. In addition, controversies regarding optimal infection control will be discussed. Finally, suggestions to improve infection control in CF will be proposed. Compliance with recent guidelines was assessed for clinical microbiology laboratories and for infection control policies at CF care centers in the United States. Unlike earlier reports, the vast majority of laboratories used selective media for Burkholderia cepacia complex and identified all species of nonlactose fermenting Gram-negative bacilli. Fewer used selective media for Staphylococcus aureus or used agar-based susceptibility testing assays for Pseudomonas aeruginosa. Only 103 (65%) of 158 CF care centers provided written infection control policies for review and these were more likely to address inpatient than outpatient settings. Surveys of healthcare professionals showed that access to a copy of the CF infection control guidelines reduced barriers to adherence to selected infection control practices. These data suggest that access to national infection control guidelines and written local policies are critically important to improving infection control for CF.

Outage Probability Minimization for Energy Harvesting Cognitive Radio Sensor Networks

PubMed Central

Zhang, Fan; Jing, Tao; Huo, Yan; Jiang, Kaiwei

2017-01-01

The incorporation of cognitive radio (CR) capability in wireless sensor networks yields a promising network paradigm known as CR sensor networks (CRSNs), which is able to provide spectrum efficient data communication. However, due to the high energy consumption results from spectrum sensing, as well as subsequent data transmission, the energy supply for the conventional sensor nodes powered by batteries is regarded as a severe bottleneck for sustainable operation. The energy harvesting technique, which gathers energy from the ambient environment, is regarded as a promising solution to perpetually power-up energy-limited devices with a continual source of energy. Therefore, applying the energy harvesting (EH) technique in CRSNs is able to facilitate the self-sustainability of the energy-limited sensors. The primary concern of this study is to design sensing-transmission policies to minimize the long-term outage probability of EH-powered CR sensor nodes. We formulate this problem as an infinite-horizon discounted Markov decision process and propose an ϵ-optimal sensing-transmission (ST) policy through using the value iteration algorithm. ϵ is the error bound between the ST policy and the optimal policy, which can be pre-defined according to the actual need. Moreover, for a special case that the signal-to-noise (SNR) power ratio is sufficiently high, we present an efficient transmission (ET) policy and prove that the ET policy achieves the same performance with the ST policy. Finally, extensive simulations are conducted to evaluate the performance of the proposed policies and the impaction of various network parameters. PMID:28125023

Outage Probability Minimization for Energy Harvesting Cognitive Radio Sensor Networks.

PubMed

Zhang, Fan; Jing, Tao; Huo, Yan; Jiang, Kaiwei

2017-01-24

The incorporation of cognitive radio (CR) capability in wireless sensor networks yields a promising network paradigm known as CR sensor networks (CRSNs), which is able to provide spectrum efficient data communication. However, due to the high energy consumption results from spectrum sensing, as well as subsequent data transmission, the energy supply for the conventional sensor nodes powered by batteries is regarded as a severe bottleneck for sustainable operation. The energy harvesting technique, which gathers energy from the ambient environment, is regarded as a promising solution to perpetually power-up energy-limited devices with a continual source of energy. Therefore, applying the energy harvesting (EH) technique in CRSNs is able to facilitate the self-sustainability of the energy-limited sensors. The primary concern of this study is to design sensing-transmission policies to minimize the long-term outage probability of EH-powered CR sensor nodes. We formulate this problem as an infinite-horizon discounted Markov decision process and propose an ϵ -optimal sensing-transmission (ST) policy through using the value iteration algorithm. ϵ is the error bound between the ST policy and the optimal policy, which can be pre-defined according to the actual need. Moreover, for a special case that the signal-to-noise (SNR) power ratio is sufficiently high, we present an efficient transmission (ET) policy and prove that the ET policy achieves the same performance with the ST policy. Finally, extensive simulations are conducted to evaluate the performance of the proposed policies and the impaction of various network parameters.

Mitigation and adaptation within a climate change policy portfolio: A research program

EPA Science Inventory

It is now recognized that optimal global climate policy is a portfolio of the two key responses for reducing the risks of climate change: mitigation and adaptation. Significant differences between the two responses have inhibited understanding of how to appropriately view these...

Regional allocation of biomass to U.S. energy demands under a portfolio of policy scenarios.

PubMed

Mullins, Kimberley A; Venkatesh, Aranya; Nagengast, Amy L; Kocoloski, Matt

2014-01-01

The potential for widespread use of domestically available energy resources, in conjunction with climate change concerns, suggest that biomass may be an essential component of U.S. energy systems in the near future. Cellulosic biomass in particular is anticipated to be used in increasing quantities because of policy efforts, such as federal renewable fuel standards and state renewable portfolio standards. Unfortunately, these independently designed biomass policies do not account for the fact that cellulosic biomass can equally be used for different, competing energy demands. An integrated assessment of multiple feedstocks, energy demands, and system costs is critical for making optimal decisions about a unified biomass energy strategy. This study develops a spatially explicit, best-use framework to optimally allocate cellulosic biomass feedstocks to energy demands in transportation, electricity, and residential heating sectors, while minimizing total system costs and tracking greenhouse gas emissions. Comparing biomass usage across three climate policy scenarios suggests that biomass used for space heating is a low cost emissions reduction option, while biomass for liquid fuel or for electricity becomes attractive only as emissions reduction targets or carbon prices increase. Regardless of the policy approach, study results make a strong case for national and regional coordination in policy design and compliance pathways.

Model-based reinforcement learning with dimension reduction.

PubMed

Tangkaratt, Voot; Morimoto, Jun; Sugiyama, Masashi

2016-12-01

The goal of reinforcement learning is to learn an optimal policy which controls an agent to acquire the maximum cumulative reward. The model-based reinforcement learning approach learns a transition model of the environment from data, and then derives the optimal policy using the transition model. However, learning an accurate transition model in high-dimensional environments requires a large amount of data which is difficult to obtain. To overcome this difficulty, in this paper, we propose to combine model-based reinforcement learning with the recently developed least-squares conditional entropy (LSCE) method, which simultaneously performs transition model estimation and dimension reduction. We also further extend the proposed method to imitation learning scenarios. The experimental results show that policy search combined with LSCE performs well for high-dimensional control tasks including real humanoid robot control. Copyright © 2016 Elsevier Ltd. All rights reserved.

The optimal age of measles immunisation in low-income countries: a secondary analysis of the assumptions underlying the current policy

PubMed Central

Martins, Cesário L; Garly, May-Lill; Rodrigues, Amabelia; Benn, Christine S; Whittle, Hilton

2012-01-01

Objective The current policy of measles vaccination at 9 months of age was decided in the mid-1970s. The policy was not tested for impact on child survival but was based on studies of seroconversion after measles vaccination at different ages. The authors examined the empirical evidence for the six underlying assumptions. Design Secondary analysis. Data sources and methods These assumptions have not been research issues. Hence, the authors examined case reports to assess the empirical evidence for the original assumptions. The authors used existing reviews, and in December 2011, the authors made a PubMed search for relevant papers. The title and abstract of papers in English, French, Portuguese, Spanish, German and Scandinavian languages were assessed to ascertain whether the paper was potentially relevant. Based on cumulative measles incidence figures, the authors calculated how many measles cases had been prevented assuming everybody was vaccinated at a specific age, how many ‘vaccine failures’ would occur after the age of vaccination and how many cases would occur before the specific age of vaccination. In the combined analyses of several studies, the authors used the Mantel–Haenszel weighted RR stratifying for study or age groups to estimate common trends. Setting and participants African community studies of measles infection. Primary and secondary outcomes Consistency between assumptions and empirical evidence and the predicted effect on mortality. Results In retrospect, the major assumptions were based on false premises. First, in the single study examining this point, seronegative vaccinated children had considerable protection against measles infection. Second, in 18 community studies, vaccinated measles cases (‘vaccine failures’) had threefold lower case death than unvaccinated cases. Third, in 24 community studies, infants had twofold higher case death than older measles cases. Fourth, the only study examining the assumption that ‘vaccine failures’ lead to lack of confidence found the opposite because vaccinated children had milder measles infection. Fifth, a one-dose policy was recommended. However, the two randomised trials of early two-dose measles vaccination compared with one-dose vaccination found significantly reduced mortality until 3 years of age. Thus, current evidence suggests that the optimal age for a single dose of measles vaccine should have been 6 or 7 months resulting in fewer severe unvaccinated cases among infants but more mild ‘vaccine failures’ among older children. Furthermore, the two-dose trials indicate that measles vaccine reduces mortality from other causes than measles infection. Conclusions Many lives may have been lost by not determining the optimal age of measles vaccination. Since seroconversion continues to be the basis for policy, the current recommendation is to increase the age of measles vaccination to 12 months in countries with limited measles transmission. This policy may lead to an increase in child mortality. PMID:22815465

ESD: Power, Politics, and Policy: "Tragic Optimism" from Latin America

ERIC Educational Resources Information Center

González-Gaudiano, Edgar J.

2016-01-01

In light of the challenging developmental issues confronting the countries of Latin America, this response article analyzes the power and resistance of education for sustainable development from both theoretical and policy perspectives. Of particular concern are the neo-productivist strategies driving the latest stage of capitalist development.…

Using Enrollment Demand Models in Institutional Pricing Decisions.

ERIC Educational Resources Information Center

Weiler, William C.

1984-01-01

Issues in the application of enrollment demand analysis to institutions' pricing policy are discussed, including price change impact on enrollment, the role of enrollment demand models on long-range financial and personnel planning, use of tuition and financial aid policy in optimizing policymakers' enrollment objectives, and the redistribution…

Public-Private Manpower Policies.

ERIC Educational Resources Information Center

Weber, Arnold R., Ed.; And Others

This book deals with the question of identifying the optimal mix between public and private programs and provides some guides concerning the appropriate role of government in the manpower area. There is a need for public manpower programs which give some long range direction to manpower policies and which give continuing emphasis to the…

Student Incentives and Preferential Treatment in College Admissions

ERIC Educational Resources Information Center

Pastine, Ivan; Pastine, Tuvana

2012-01-01

We consider a framework in which the optimal admissions policy of a purely academic-quality oriented college implements preferential treatment in favor of the student from the deprived socioeconomic background which maximizes the competition between candidates. We find that the exact form of the preferential treatment admissions policy matters for…

Perspectives of North American Postsecondary Students with Learning Disabilities: A Scoping Review

ERIC Educational Resources Information Center

Lightfoot, Amy; Janemi, Roya; Rudman, Debbie Laliberte

2018-01-01

Despite the existence of policies aimed at ensuring equitable opportunities for individuals with disabilities, at the postsecondary level, students with learning disabilities and/or attention deficit/hyperactivity disorder have lower enrollment and completion rates than those without disabilities. To optimize policies and practices to support…

Development and Application of a Stakeholder Assisted Dynamic Model to Facilitate Socio Hydrological Groundwater Management on Watershed Scale

NASA Astrophysics Data System (ADS)

Baig, A. I.; Adamowski, J. F.; Malard, J. J.; Peng, G.

2017-12-01

Groundwater resource, especially in canal downstream areas are under direct threat due to over extraction by farming community. The resource is easily exploitable and no regulatory policies are enforced effectively in the region. Therefore, there is an urgent need to manage the resource judiciously through policy implementation and stakeholder engagement. In developing countries such as Pakistan, effective management solutions need consideration of some addition factors such as small land holdings, the poor economic status of farmers, and limited modeling and mathematical skills. This presentation will discuss development and application of a comprehensive but simple stakeholder assisted dynamic model to address such challenges. Two major components of the dynamic model were: (i) a system dynamics model that describes socio-economic factors such as market values; and ii) a physically based model that simulates the salt balance in the root zone with conjunctive use of canal and tube well irrigation water. Stakeholder proposed policy scenarios such as canal lining, government-sponsored tubewell installation schemes were tested and optimized through economic and environmental tradeoff criteria. After 20 years of simulation, government subsidies on tubewells appear as a short term policy that resulted 37% increase in water availability with 12% increase in farmer income. However, it showed detrimental effects on groundwater sustainability in long terms, with 10% drop in groundwater levels.

Vaccination and treatment as control interventions in an infectious disease model with their cost optimization

NASA Astrophysics Data System (ADS)

Kumar, Anuj; Srivastava, Prashant K.

2017-03-01

In this work, an optimal control problem with vaccination and treatment as control policies is proposed and analysed for an SVIR model. We choose vaccination and treatment as control policies because both these interventions have their own practical advantage and ease in implementation. Also, they are widely applied to control or curtail a disease. The corresponding total cost incurred is considered as weighted combination of costs because of opportunity loss due to infected individuals and costs incurred in providing vaccination and treatment. The existence of optimal control paths for the problem is established and guaranteed. Further, these optimal paths are obtained analytically using Pontryagin's Maximum Principle. We analyse our results numerically to compare three important strategies of proposed controls, viz.: vaccination only; with both treatment and vaccination; and treatment only. We note that first strategy (vaccination only) is less effective as well as expensive. Though, for a highly effective vaccine, vaccination alone may also work well in comparison with treatment only strategy. Among all the strategies, we observe that implementation of both treatment and vaccination is most effective and less expensive. Moreover, in this case the infective population is found to be relatively very low. Thus, we conclude that the comprehensive effect of vaccination and treatment not only minimizes cost burden due to opportunity loss and applied control policies but also keeps a tab on infective population.

Assessment of Trading Partners for China's Rare Earth Exports Using a Decision Analytic Approach

PubMed Central

He, Chunyan; Lei, Yalin; Ge, Jianping

2014-01-01

Chinese rare earth export policies currently result in accelerating its depletion. Thus adopting an optimal export trade selection strategy is crucial to determining and ultimately identifying the ideal trading partners. This paper introduces a multi-attribute decision-making methodology which is then used to select the optimal trading partner. In the method, an evaluation criteria system is established to assess the seven top trading partners based on three dimensions: political relationships, economic benefits and industrial security. Specifically, a simple additive weighing model derived from an additive utility function is utilized to calculate, rank and select alternatives. Results show that Japan would be the optimal trading partner for Chinese rare earths. The criteria evaluation method of trading partners for China's rare earth exports provides the Chinese government with a tool to enhance rare earth industrial policies. PMID:25051534

Determination of optimal environmental policy for reclamation of land unearthed in lignite mines - Strategy and tactics

NASA Astrophysics Data System (ADS)

Batzias, Dimitris F.; Pollalis, Yannis A.

2012-12-01

In this paper, optimal environmental policy for reclamation of land unearthed in lignite mines is defined as a strategic target. The tactics concerning the achievement of this target, includes estimation of optimal time lag between each lignite site (which is a segment of the whole lignite field) complete exploitation and its reclamation. Subsidizing of reclamation has been determined as a function of this time lag and relevant implementation is presented for parameter values valid for the Greek economy. We proved that the methodology we have developed gives reasonable quantitative results within the norms imposed by legislation. Moreover, the interconnection between strategy and tactics becomes evident, since the former causes the latter by deduction and the latter revises the former by induction in the time course of land reclamation.

Assessment of trading partners for China's rare earth exports using a decision analytic approach.

PubMed

He, Chunyan; Lei, Yalin; Ge, Jianping

2014-01-01

Chinese rare earth export policies currently result in accelerating its depletion. Thus adopting an optimal export trade selection strategy is crucial to determining and ultimately identifying the ideal trading partners. This paper introduces a multi-attribute decision-making methodology which is then used to select the optimal trading partner. In the method, an evaluation criteria system is established to assess the seven top trading partners based on three dimensions: political relationships, economic benefits and industrial security. Specifically, a simple additive weighing model derived from an additive utility function is utilized to calculate, rank and select alternatives. Results show that Japan would be the optimal trading partner for Chinese rare earths. The criteria evaluation method of trading partners for China's rare earth exports provides the Chinese government with a tool to enhance rare earth industrial policies.

Evaluation of the Navys Sea/Shore Flow Policy

DTIC Science & Technology

2016-06-01

CNA developed an independent Discrete -Event Simulation model to evaluate and assess the effect of alternative sea/shore flow policies. In this study...remains, even if the system is optimized. In building a Discrete -Event Simulation model, we discovered key factors that should be included in the... Discrete -Event Simulation model to evaluate the impact of sea/shore flow policy (the DES-SSF model) and compared the results with the SSFM for one

Geothermal power, policy, and design: Using levelized cost of energy and sensitivity analysis to target improved policy incentives for the U.S. geothermal market

NASA Astrophysics Data System (ADS)

Richard, Christopher L.

At the core of the geothermal industry is a need to identify how policy incentives can better be applied for optimal return. Literature from Bloomquist (1999), Doris et al. (2009), and McIlveen (2011) suggest that a more tailored approach to crafting geothermal policy is warranted. In this research the guiding theory is based on those suggestions and is structured to represent a policy analysis approach using analytical methods. The methods being used are focus on qualitative and quantitative results. To address the qualitative sections of this research an extensive review of contemporary literature is used to identify the frequency of use for specific barriers, and is followed upon with an industry survey to determine existing gaps. As a result there is support for certain barriers and justification for expanding those barriers found within the literature. This method of inquiry is an initial point for structuring modeling tools to further quantify the research results as part of the theoretical framework. Analytical modeling utilizes the levelized cost of energy as a foundation for comparative assessment of policy incentives. Model parameters use assumptions to draw conclusions from literature and survey results to reflect unique attributes held by geothermal power technologies. Further testing by policy option provides an opportunity to assess the sensitivity of each variable with respect to applied policy. Master limited partnerships, feed in tariffs, RD&D, and categorical exclusions all result as viable options for mitigating specific barriers associated to developing geothermal power. The results show reductions of levelized cost based upon the model's exclusive parameters. These results are also compared to contemporary policy options highlighting the need for tailored policy, as discussed by Bloomquist (1999), Doris et al. (2009), and McIlveen (2011). It is the intent of this research to provide the reader with a descriptive understanding of the role of geothermal power in the United States, and to recognize that not all policy or energy technology is created equal. Further study options are provide to expand the scope and granularity of this research design to better support a growing market.

Development of a flow-through system for the fish embryo toxicity test (FET) with the zebrafish (Danio rerio).

PubMed

Lammer, E; Kamp, H G; Hisgen, V; Koch, M; Reinhard, D; Salinas, E R; Wendler, K; Zok, S; Braunbeck, Th

2009-10-01

The acute fish test is still a mandatory component in chemical hazard and risk assessment. However, one of the objectives of the new European chemicals policy (REACH - Registration, Evaluation, Authorization and Restriction of Chemicals) is to promote non-animal testing. For whole effluent testing in Germany, the fish embryo toxicity test (FET) with the zebrafish (Danio rerio) has been an accepted and mandatory replacement of the fish test since January 2005. For chemical testing, however, further optimization of the FET is required to improve the correlation between the acute fish test and the alternative FET. Since adsorption of the test chemical to surfaces may reduce available exposure concentrations, a flow-through system for the FET using modified commercially available polystyrene 24-well microtiter plates was developed, thus combining the advantages of the standard FET with those of continuous delivery of test substances. The advantages of the design presented include: small test footprint, availability of adequate volumes of test solution for subsequent chemical analysis, and sufficient flow to compensate for effects of non-specific adsorption within 24h. The flow-through test system can also be utilized to conduct longer-term embryo larval fish tests, thus offering the possibility for teratogenicity testing.

National policy on testing for HIV in South Africa: an urgent need.

PubMed

Oosthuizen, H

2001-01-01

No National Policy exists in South Africa for the testing of persons who may be infected by HIV/AIDS. A Draft National Policy on Testing for HIV was published in the Government Gazette by the Minister of Health for commentary in December 1999. The policy provides for the following: circumstances under which HIV testing may be conducted; informed consent, pre-test and post-test counselling; and the interpretation of the policy. This policy will apply to persons who are able to give consent as well as to those who are legally entitled to give proxy consent to HIV testing. Testing with informed consent means that the individual has been made aware of, and understands, the implications of the test. "The vision which fueled our struggle for freedom; the deployment of energies and resources; the unity and commitment to common goals--all these are needed if we are to bring AIDS under control. Future generations will judge us on the adequacy of our response." President Nelson Mandela. DAVOS, 1996.

32 CFR 634.35 - Chemical testing policies and procedures.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 32 National Defense 4 2010-07-01 2010-07-01 true Chemical testing policies and procedures. 634.35... Chemical testing policies and procedures. (a) Validity of chemical testing. Results of chemical testing are... instruction manual. (iv) Perform preventive maintenance as required by the instruction manual. (c) Chemical...

32 CFR 634.35 - Chemical testing policies and procedures.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 32 National Defense 4 2011-07-01 2011-07-01 false Chemical testing policies and procedures. 634.35... Chemical testing policies and procedures. (a) Validity of chemical testing. Results of chemical testing are... instruction manual. (iv) Perform preventive maintenance as required by the instruction manual. (c) Chemical...

32 CFR 634.35 - Chemical testing policies and procedures.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 32 National Defense 4 2012-07-01 2011-07-01 true Chemical testing policies and procedures. 634.35... Chemical testing policies and procedures. (a) Validity of chemical testing. Results of chemical testing are... instruction manual. (iv) Perform preventive maintenance as required by the instruction manual. (c) Chemical...

32 CFR 634.35 - Chemical testing policies and procedures.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 32 National Defense 4 2014-07-01 2013-07-01 true Chemical testing policies and procedures. 634.35... Chemical testing policies and procedures. (a) Validity of chemical testing. Results of chemical testing are... instruction manual. (iv) Perform preventive maintenance as required by the instruction manual. (c) Chemical...

32 CFR 634.35 - Chemical testing policies and procedures.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 32 National Defense 4 2013-07-01 2013-07-01 false Chemical testing policies and procedures. 634.35... Chemical testing policies and procedures. (a) Validity of chemical testing. Results of chemical testing are... instruction manual. (iv) Perform preventive maintenance as required by the instruction manual. (c) Chemical...

Studying complex interventions: reflections from the FEMHealth project on evaluating fee exemption policies in West Africa and Morocco.

PubMed

Marchal, Bruno; Van Belle, Sara; De Brouwere, Vincent; Witter, Sophie

2013-11-08

The importance of complexity in health care policy-making and interventions, as well as research and evaluation is now widely acknowledged, but conceptual confusion reigns and few applications of complexity concepts in research design have been published. Taking user fee exemption policies as an entry point, we explore the methodological consequences of 'complexity' for health policy research and evaluation. We first discuss the difference between simple, complicated and complex and introduce key concepts of complex adaptive systems theory. We then apply these to fee exemption policies. We describe how the FEMHealth research project attempts to address the challenges of complexity in its evaluation of fee exemption policies for maternal care. We present how the development of a programme theory for fee exemption policies was used to structure the overall design. This allowed for structured discussions on the hypotheses held by the researchers and helped to structure, integrate and monitor the sub-studies. We then show how the choice of data collection methods and tools for each sub-study was informed by the overall design. Applying key concepts from complexity theory proved useful in broadening our view on fee exemption policies and in developing the overall research design. However, we encountered a number of challenges, including maintaining adaptiveness of the design during the evaluation, and ensuring cohesion in the disciplinary diversity of the research teams. Whether the programme theory can fulfil its claimed potential to help making sense of the findings is yet to be tested. Experience from other studies allows for some moderate optimism. However, the biggest challenge complexity throws at health system researchers may be to deal with the unknown unknowns and the consequence that complex issues can only be understood in retrospect. From a complexity theory point of view, only plausible explanations can be developed, not predictive theories. Yet here, theory-driven approaches may help.

Hydro-environmental management of groundwater resources: A fuzzy-based multi-objective compromise approach

NASA Astrophysics Data System (ADS)

Alizadeh, Mohammad Reza; Nikoo, Mohammad Reza; Rakhshandehroo, Gholam Reza

2017-08-01

Sustainable management of water resources necessitates close attention to social, economic and environmental aspects such as water quality and quantity concerns and potential conflicts. This study presents a new fuzzy-based multi-objective compromise methodology to determine the socio-optimal and sustainable policies for hydro-environmental management of groundwater resources, which simultaneously considers the conflicts and negotiation of involved stakeholders, uncertainties in decision makers' preferences, existing uncertainties in the groundwater parameters and groundwater quality and quantity issues. The fuzzy multi-objective simulation-optimization model is developed based on qualitative and quantitative groundwater simulation model (MODFLOW and MT3D), multi-objective optimization model (NSGA-II), Monte Carlo analysis and Fuzzy Transformation Method (FTM). Best compromise solutions (best management policies) on trade-off curves are determined using four different Fuzzy Social Choice (FSC) methods. Finally, a unanimity fallback bargaining method is utilized to suggest the most preferred FSC method. Kavar-Maharloo aquifer system in Fars, Iran, as a typical multi-stakeholder multi-objective real-world problem is considered to verify the proposed methodology. Results showed an effective performance of the framework for determining the most sustainable allocation policy in groundwater resource management.

A mathematical model of sentimental dynamics accounting for marital dissolution.

PubMed

Rey, José-Manuel

2010-03-31

Marital dissolution is ubiquitous in western societies. It poses major scientific and sociological problems both in theoretical and therapeutic terms. Scholars and therapists agree on the existence of a sort of second law of thermodynamics for sentimental relationships. Effort is required to sustain them. Love is not enough. Building on a simple version of the second law we use optimal control theory as a novel approach to model sentimental dynamics. Our analysis is consistent with sociological data. We show that, when both partners have similar emotional attributes, there is an optimal effort policy yielding a durable happy union. This policy is prey to structural destabilization resulting from a combination of two factors: there is an effort gap because the optimal policy always entails discomfort and there is a tendency to lower effort to non-sustaining levels due to the instability of the dynamics. These mathematical facts implied by the model unveil an underlying mechanism that may explain couple disruption in real scenarios. Within this framework the apparent paradox that a union consistently planned to last forever will probably break up is explained as a mechanistic consequence of the second law.

A Mathematical Model of Sentimental Dynamics Accounting for Marital Dissolution

PubMed Central

Rey, José-Manuel

2010-01-01

Background Marital dissolution is ubiquitous in western societies. It poses major scientific and sociological problems both in theoretical and therapeutic terms. Scholars and therapists agree on the existence of a sort of second law of thermodynamics for sentimental relationships. Effort is required to sustain them. Love is not enough. Methodology/Principal Findings Building on a simple version of the second law we use optimal control theory as a novel approach to model sentimental dynamics. Our analysis is consistent with sociological data. We show that, when both partners have similar emotional attributes, there is an optimal effort policy yielding a durable happy union. This policy is prey to structural destabilization resulting from a combination of two factors: there is an effort gap because the optimal policy always entails discomfort and there is a tendency to lower effort to non-sustaining levels due to the instability of the dynamics. Conclusions/Significance These mathematical facts implied by the model unveil an underlying mechanism that may explain couple disruption in real scenarios. Within this framework the apparent paradox that a union consistently planned to last forever will probably break up is explained as a mechanistic consequence of the second law. PMID:20360987

Policy for equipment’s leasing period extension with minimum cost of maintenance

NASA Astrophysics Data System (ADS)

Lestari, C.; Kurniati, N.

2018-04-01

The cost structure for equipment investment including purchase cost and maintenance cost is getting more expensive. The company considers to lease the equipment instead of purchase it under a contractual agreement. Offering to extend the lease period, following to the base lease period, will provide more benefits for both the lessor (owner) and the lessee (user). Whenever the lease period extension offered at the beginning of the contract, there are some risks in finance e.g. uncertainty of the equipment performance and lessor responsibility. Therefore, this research attempts to model the optimal maintenance policy for lease period extension offered at the end of the contract. Minimal repair is performed to rectify a failed equipment, while imperfect preventive maintenance is conducted to improve the operational state of the equipment when reaches a certain control limit to avoid failures. The mathematical model is constructed to determine the optimal control limit, the number and degree of preventive maintenance, and the multiplication number of the lease period extension. Finally, numerical examples are given to illustrate the influences of the optimal length of the extended lease and the maintenance policy to minimize the maintenance cost.

Event-Based Robust Control for Uncertain Nonlinear Systems Using Adaptive Dynamic Programming.

PubMed

Zhang, Qichao; Zhao, Dongbin; Wang, Ding

2018-01-01

In this paper, the robust control problem for a class of continuous-time nonlinear system with unmatched uncertainties is investigated using an event-based control method. First, the robust control problem is transformed into a corresponding optimal control problem with an augmented control and an appropriate cost function. Under the event-based mechanism, we prove that the solution of the optimal control problem can asymptotically stabilize the uncertain system with an adaptive triggering condition. That is, the designed event-based controller is robust to the original uncertain system. Note that the event-based controller is updated only when the triggering condition is satisfied, which can save the communication resources between the plant and the controller. Then, a single network adaptive dynamic programming structure with experience replay technique is constructed to approach the optimal control policies. The stability of the closed-loop system with the event-based control policy and the augmented control policy is analyzed using the Lyapunov approach. Furthermore, we prove that the minimal intersample time is bounded by a nonzero positive constant, which excludes Zeno behavior during the learning process. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed control scheme.

Optimal Repair And Replacement Policy For A System With Multiple Components

DTIC Science & Technology

2016-06-17

Numerical Demonstration To implement the linear program, we use the Python Programming Language (PSF 2016) with the Pyomo optimization modeling language...opre.1040.0133. Hart, W.E., C. Laird, J. Watson, D.L. Woodruff. 2012. Pyomo–optimization modeling in python , vol. 67. Springer Science & Business...Media. Hart, W.E., J. Watson, D.L. Woodruff. 2011. Pyomo: modeling and solving mathematical programs in python . Mathematical Programming Computation 3(3

Optimal Foraging in Semantic Memory

ERIC Educational Resources Information Center

Hills, Thomas T.; Jones, Michael N.; Todd, Peter M.

2012-01-01

Do humans search in memory using dynamic local-to-global search strategies similar to those that animals use to forage between patches in space? If so, do their dynamic memory search policies correspond to optimal foraging strategies seen for spatial foraging? Results from a number of fields suggest these possibilities, including the shared…

Implications of the Value of Hydrologic Information to Reservoir Operations--Learning from the Past

ERIC Educational Resources Information Center

Hejazi, Mohamad Issa

2009-01-01

Closing the gap between theoretical reservoir operation and the real-world implementation remains a challenge in contemporary reservoir operations. Past research has focused on optimization algorithms and establishing optimal policies for reservoir operations. In this research, we attempt to understand operators' release decisions by investigating…

Off-label prescribing of medications for pain: maintaining optimal care at an intersection of law, public policy, and ethics.

PubMed

Ruble, James

2012-06-01

For more than 60 years, regulations limited marketing of medications for off-label uses to very low levels. Some key policy changes in the late 1990s ushered in an era of deregulation of off-label marketing. Policy changes included revised United States federal law as well as modifications of Food and Drug Administration (FDA) regulations. Subsequent investigations documented an explosion in scope off-label prescribing. Attempts to limit off-label advertising by manufacturers were vigorously challenged in the courts. Other modalities are needed to maintain a clinical care environment that places the patients' best interests first. In many circumstances, an off-label medication may be in the patient's best interests; however, where there is a lower level of clinical justification, the informed consent of the patient and shared decision making of the patient is essential to optimize outcome.

GMDPtoolbox: A Matlab library for designing spatial management policies. Application to the long-term collective management of an airborne disease

PubMed Central

Aubertot, Jean-Noël; Peyrard, Nathalie; Sabbadin, Régis

2017-01-01

Designing management policies in ecology and agroecology is complex. Several components must be managed together while they strongly interact spatially. Decision choices must be made under uncertainty on the results of the actions and on the system dynamics. Furthermore, the objectives pursued when managing ecological systems or agroecosystems are usually long term objectives, such as biodiversity conservation or sustainable crop production. The framework of Graph-Based Markov Decision Processes (GMDP) is well adapted to the qualitative modeling of such problems of sequential decision under uncertainty. Spatial interactions are easily modeled and integrated control policies (combining several action levers) can be designed through optimization. The provided policies are adaptive, meaning that management actions are decided at each time step (for instance yearly) and the chosen actions depend on the current system state. This framework has already been successfully applied to forest management and invasive species management. However, up to now, no “easy-to-use” implementation of this framework was available. We present GMDPtoolbox, a Matlab toolbox which can be used both for the design of new management policies and for comparing policies by simulation. We provide an illustration of the use of the toolbox on a realistic crop disease management problem: the design of long term management policy of blackleg of canola using an optimal combination of three possible cultural levers. This example shows how GMDPtoolbox can be used as a tool to support expert thinking. PMID:28982151

GMDPtoolbox: A Matlab library for designing spatial management policies. Application to the long-term collective management of an airborne disease.

PubMed

Cros, Marie-Josée; Aubertot, Jean-Noël; Peyrard, Nathalie; Sabbadin, Régis

2017-01-01

Designing management policies in ecology and agroecology is complex. Several components must be managed together while they strongly interact spatially. Decision choices must be made under uncertainty on the results of the actions and on the system dynamics. Furthermore, the objectives pursued when managing ecological systems or agroecosystems are usually long term objectives, such as biodiversity conservation or sustainable crop production. The framework of Graph-Based Markov Decision Processes (GMDP) is well adapted to the qualitative modeling of such problems of sequential decision under uncertainty. Spatial interactions are easily modeled and integrated control policies (combining several action levers) can be designed through optimization. The provided policies are adaptive, meaning that management actions are decided at each time step (for instance yearly) and the chosen actions depend on the current system state. This framework has already been successfully applied to forest management and invasive species management. However, up to now, no "easy-to-use" implementation of this framework was available. We present GMDPtoolbox, a Matlab toolbox which can be used both for the design of new management policies and for comparing policies by simulation. We provide an illustration of the use of the toolbox on a realistic crop disease management problem: the design of long term management policy of blackleg of canola using an optimal combination of three possible cultural levers. This example shows how GMDPtoolbox can be used as a tool to support expert thinking.

Dynamic health policies for controlling the spread of emerging infections: influenza as an example.

PubMed

Yaesoubi, Reza; Cohen, Ted

2011-01-01

The recent appearance and spread of novel infectious pathogens provide motivation for using models as tools to guide public health decision-making. Here we describe a modeling approach for developing dynamic health policies that allow for adaptive decision-making as new data become available during an epidemic. In contrast to static health policies which have generally been selected by comparing the performance of a limited number of pre-determined sequences of interventions within simulation or mathematical models, dynamic health policies produce "real-time" recommendations for the choice of the best current intervention based on the observable state of the epidemic. Using cumulative real-time data for disease spread coupled with current information about resource availability, these policies provide recommendations for interventions that optimally utilize available resources to preserve the overall health of the population. We illustrate the design and implementation of a dynamic health policy for the control of a novel strain of influenza, where we assume that two types of intervention may be available during the epidemic: (1) vaccines and antiviral drugs, and (2) transmission reducing measures, such as social distancing or mask use, that may be turned "on" or "off" repeatedly during the course of epidemic. In this example, the optimal dynamic health policy maximizes the overall population's health during the epidemic by specifying at any point of time, based on observable conditions, (1) the number of individuals to vaccinate if vaccines are available, and (2) whether the transmission-reducing intervention should be either employed or removed.

Insurance Coverage Policies for Pharmacogenomic and Multi-Gene Testing for Cancer.

PubMed

Lu, Christine Y; Loomer, Stephanie; Ceccarelli, Rachel; Mazor, Kathleen M; Sabin, James; Clayton, Ellen Wright; Ginsburg, Geoffrey S; Wu, Ann Chen

2018-05-16

Insurance coverage policies are a major determinant of patient access to genomic tests. The objective of this study was to examine differences in coverage policies for guideline-recommended pharmacogenomic tests that inform cancer treatment. We analyzed coverage policies from eight Medicare contractors and 10 private payers for 23 biomarkers (e.g., HER2 and EGFR ) and multi-gene tests. We extracted policy coverage and criteria, prior authorization requirements, and an evidence basis for coverage. We reviewed professional society guidelines and their recommendations for use of pharmacogenomic tests. Coverage for KRAS , EGFR , and BRAF tests were common across Medicare contractors and private payers, but few policies covered PML/RARA , CD25 , or G6PD . Thirteen payers cover multi-gene tests for nonsmall lung cancer, citing emerging clinical recommendations. Coverage policies for single and multi-gene tests for cancer treatments are consistent among Medicare contractors despite the lack of national coverage determinations. In contrast, coverage for these tests varied across private payers. Patient access to tests is governed by prior authorization among eight private payers. Substantial variations in how payers address guideline-recommended pharmacogenomic tests and the common use of prior authorization underscore the need for additional studies of the effects of coverage variation on cancer care and patient outcomes.

Optimization methods for decision making in disease prevention and epidemic control.

PubMed

Deng, Yan; Shen, Siqian; Vorobeychik, Yevgeniy

2013-11-01

This paper investigates problems of disease prevention and epidemic control (DPEC), in which we optimize two sets of decisions: (i) vaccinating individuals and (ii) closing locations, given respective budgets with the goal of minimizing the expected number of infected individuals after intervention. The spread of diseases is inherently stochastic due to the uncertainty about disease transmission and human interaction. We use a bipartite graph to represent individuals' propensities of visiting a set of location, and formulate two integer nonlinear programming models to optimize choices of individuals to vaccinate and locations to close. Our first model assumes that if a location is closed, its visitors stay in a safe location and will not visit other locations. Our second model incorporates compensatory behavior by assuming multiple behavioral groups, always visiting the most preferred locations that remain open. The paper develops algorithms based on a greedy strategy, dynamic programming, and integer programming, and compares the computational efficacy and solution quality. We test problem instances derived from daily behavior patterns of 100 randomly chosen individuals (corresponding to 195 locations) in Portland, Oregon, and provide policy insights regarding the use of the two DPEC models. Copyright © 2013 Elsevier Inc. All rights reserved.

Analyzing Personalized Policies for Online Biometric Verification

PubMed Central

Sadhwani, Apaar; Yang, Yan; Wein, Lawrence M.

2014-01-01

Motivated by India’s nationwide biometric program for social inclusion, we analyze verification (i.e., one-to-one matching) in the case where we possess similarity scores for 10 fingerprints and two irises between a resident’s biometric images at enrollment and his biometric images during his first verification. At subsequent verifications, we allow individualized strategies based on these 12 scores: we acquire a subset of the 12 images, get new scores for this subset that quantify the similarity to the corresponding enrollment images, and use the likelihood ratio (i.e., the likelihood of observing these scores if the resident is genuine divided by the corresponding likelihood if the resident is an imposter) to decide whether a resident is genuine or an imposter. We also consider two-stage policies, where additional images are acquired in a second stage if the first-stage results are inconclusive. Using performance data from India’s program, we develop a new probabilistic model for the joint distribution of the 12 similarity scores and find near-optimal individualized strategies that minimize the false reject rate (FRR) subject to constraints on the false accept rate (FAR) and mean verification delay for each resident. Our individualized policies achieve the same FRR as a policy that acquires (and optimally fuses) 12 biometrics for each resident, which represents a five (four, respectively) log reduction in FRR relative to fingerprint (iris, respectively) policies previously proposed for India’s biometric program. The mean delay is sec for our proposed policy, compared to 30 sec for a policy that acquires one fingerprint and 107 sec for a policy that acquires all 12 biometrics. This policy acquires iris scans from 32–41% of residents (depending on the FAR) and acquires an average of 1.3 fingerprints per resident. PMID:24787752

Efficient Discovery of De-identification Policies Through a Risk-Utility Frontier

PubMed Central

Xia, Weiyi; Heatherly, Raymond; Ding, Xiaofeng; Li, Jiuyong; Malin, Bradley

2014-01-01

Modern information technologies enable organizations to capture large quantities of person-specific data while providing routine services. Many organizations hope, or are legally required, to share such data for secondary purposes (e.g., validation of research findings) in a de-identified manner. In previous work, it was shown de-identification policy alternatives could be modeled on a lattice, which could be searched for policies that met a prespecified risk threshold (e.g., likelihood of re-identification). However, the search was limited in several ways. First, its definition of utility was syntactic - based on the level of the lattice - and not semantic - based on the actual changes induced in the resulting data. Second, the threshold may not be known in advance. The goal of this work is to build the optimal set of policies that trade-off between privacy risk (R) and utility (U), which we refer to as a R-U frontier. To model this problem, we introduce a semantic definition of utility, based on information theory, that is compatible with the lattice representation of policies. To solve the problem, we initially build a set of policies that define a frontier. We then use a probability-guided heuristic to search the lattice for policies likely to update the frontier. To demonstrate the effectiveness of our approach, we perform an empirical analysis with the Adult dataset of the UCI Machine Learning Repository. We show that our approach can construct a frontier closer to optimal than competitive approaches by searching a smaller number of policies. In addition, we show that a frequently followed de-identification policy (i.e., the Safe Harbor standard of the HIPAA Privacy Rule) is suboptimal in comparison to the frontier discovered by our approach. PMID:25520961

Efficient Discovery of De-identification Policies Through a Risk-Utility Frontier.

PubMed

Xia, Weiyi; Heatherly, Raymond; Ding, Xiaofeng; Li, Jiuyong; Malin, Bradley

2013-01-01

Modern information technologies enable organizations to capture large quantities of person-specific data while providing routine services. Many organizations hope, or are legally required, to share such data for secondary purposes (e.g., validation of research findings) in a de-identified manner. In previous work, it was shown de-identification policy alternatives could be modeled on a lattice, which could be searched for policies that met a prespecified risk threshold (e.g., likelihood of re-identification). However, the search was limited in several ways. First, its definition of utility was syntactic - based on the level of the lattice - and not semantic - based on the actual changes induced in the resulting data. Second, the threshold may not be known in advance. The goal of this work is to build the optimal set of policies that trade-off between privacy risk (R) and utility (U), which we refer to as a R-U frontier. To model this problem, we introduce a semantic definition of utility, based on information theory, that is compatible with the lattice representation of policies. To solve the problem, we initially build a set of policies that define a frontier. We then use a probability-guided heuristic to search the lattice for policies likely to update the frontier. To demonstrate the effectiveness of our approach, we perform an empirical analysis with the Adult dataset of the UCI Machine Learning Repository. We show that our approach can construct a frontier closer to optimal than competitive approaches by searching a smaller number of policies. In addition, we show that a frequently followed de-identification policy (i.e., the Safe Harbor standard of the HIPAA Privacy Rule) is suboptimal in comparison to the frontier discovered by our approach.

Analyzing personalized policies for online biometric verification.

PubMed

Sadhwani, Apaar; Yang, Yan; Wein, Lawrence M

2014-01-01

Motivated by India's nationwide biometric program for social inclusion, we analyze verification (i.e., one-to-one matching) in the case where we possess similarity scores for 10 fingerprints and two irises between a resident's biometric images at enrollment and his biometric images during his first verification. At subsequent verifications, we allow individualized strategies based on these 12 scores: we acquire a subset of the 12 images, get new scores for this subset that quantify the similarity to the corresponding enrollment images, and use the likelihood ratio (i.e., the likelihood of observing these scores if the resident is genuine divided by the corresponding likelihood if the resident is an imposter) to decide whether a resident is genuine or an imposter. We also consider two-stage policies, where additional images are acquired in a second stage if the first-stage results are inconclusive. Using performance data from India's program, we develop a new probabilistic model for the joint distribution of the 12 similarity scores and find near-optimal individualized strategies that minimize the false reject rate (FRR) subject to constraints on the false accept rate (FAR) and mean verification delay for each resident. Our individualized policies achieve the same FRR as a policy that acquires (and optimally fuses) 12 biometrics for each resident, which represents a five (four, respectively) log reduction in FRR relative to fingerprint (iris, respectively) policies previously proposed for India's biometric program. The mean delay is [Formula: see text] sec for our proposed policy, compared to 30 sec for a policy that acquires one fingerprint and 107 sec for a policy that acquires all 12 biometrics. This policy acquires iris scans from 32-41% of residents (depending on the FAR) and acquires an average of 1.3 fingerprints per resident.

OVERCOMING BROWNFIELD BARRIERS TO URBAN MANUFACTURING: COMPARATIVE STUDY OF POLICY NETWORKS AND CHANGING LOCAL ECONOMIC DEVELOPMENT STRATEGIES IN FOUR U.S. CITIES

EPA Science Inventory

This study suggests that growing optimism in the U.S. manufacturing’s recovery, coupled with evolving structures and functions of social (policy) networks involving diverse groups of local stakeholders concerned with brownfields, economic development, smart growth, environm...

Adaptive nearly optimal control for a class of continuous-time nonaffine nonlinear systems with inequality constraints.

PubMed

Fan, Quan-Yong; Yang, Guang-Hong

2017-01-01

The state inequality constraints have been hardly considered in the literature on solving the nonlinear optimal control problem based the adaptive dynamic programming (ADP) method. In this paper, an actor-critic (AC) algorithm is developed to solve the optimal control problem with a discounted cost function for a class of state-constrained nonaffine nonlinear systems. To overcome the difficulties resulting from the inequality constraints and the nonaffine nonlinearities of the controlled systems, a novel transformation technique with redesigned slack functions and a pre-compensator method are introduced to convert the constrained optimal control problem into an unconstrained one for affine nonlinear systems. Then, based on the policy iteration (PI) algorithm, an online AC scheme is proposed to learn the nearly optimal control policy for the obtained affine nonlinear dynamics. Using the information of the nonlinear model, novel adaptive update laws are designed to guarantee the convergence of the neural network (NN) weights and the stability of the affine nonlinear dynamics without the requirement for the probing signal. Finally, the effectiveness of the proposed method is validated by simulation studies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Kernel-Based Approximate Dynamic Programming Using Bellman Residual Elimination

DTIC Science & Technology

2010-02-01

framework is the ability to utilize stochastic system models, thereby allowing the system to make sound decisions even if there is randomness in the system ...approximate policy when a system model is unavailable. We present theoretical analysis of all BRE algorithms proving convergence to the optimal policy in...policies based on MDPs is that there may be parameters of the system model that are poorly known and/or vary with time as the system operates. System

Analysis and Research on the Optimal Allocation of Regional Water Resources

NASA Astrophysics Data System (ADS)

rui-chao, Xi; yu-jie, Gu

2018-06-01

Starting from the basic concept of optimal allocation of water resources, taking the allocation of water resources in Tianjin as an example, the present situation of water resources in Tianjin is analyzed, and the multi-objective optimal allocation model of water resources is used to optimize the allocation of water resources. We use LINGO to solve the model, get the optimal allocation plan that meets the economic and social benefits, and put forward relevant policies and regulations, so as to provide theoretical which is basis for alleviating and solving the problem of water shortage.

Field Scale Optimization for Long-Term Sustainability of Best Management Practices in Watersheds

NASA Astrophysics Data System (ADS)

Samuels, A.; Babbar-Sebens, M.

2012-12-01

Agricultural and urban land use changes have led to disruption of natural hydrologic processes and impairment of streams and rivers. Multiple previous studies have evaluated Best Management Practices (BMPs) as means for restoring existing hydrologic conditions and reducing impairment of water resources. However, planning of these practices have relied on watershed scale hydrologic models for identifying locations and types of practices at scales much coarser than the actual field scale, where landowners have to plan, design and implement the practices. Field scale hydrologic modeling provides means for identifying relationships between BMP type, spatial location, and the interaction between BMPs at a finer farm/field scale that is usually more relevant to the decision maker (i.e. the landowner). This study focuses on development of a simulation-optimization approach for field-scale planning of BMPs in the School Branch stream system of Eagle Creek Watershed, Indiana, USA. The Agricultural Policy Environmental Extender (APEX) tool is used as the field scale hydrologic model, and a multi-objective optimization algorithm is used to search for optimal alternatives. Multiple climate scenarios downscaled to the watershed-scale are used to test the long term performance of these alternatives and under extreme weather conditions. The effectiveness of these BMPs under multiple weather conditions are included within the simulation-optimization approach as a criteria/goal to assist landowners in identifying sustainable design of practices. The results from these scenarios will further enable efficient BMP planning for current and future usage.

Chaotic dynamics in optimal monetary policy

NASA Astrophysics Data System (ADS)

Gomes, O.; Mendes, V. M.; Mendes, D. A.; Sousa Ramos, J.

2007-05-01

There is by now a large consensus in modern monetary policy. This consensus has been built upon a dynamic general equilibrium model of optimal monetary policy as developed by, e.g., Goodfriend and King [ NBER Macroeconomics Annual 1997 edited by B. Bernanke and J. Rotemberg (Cambridge, Mass.: MIT Press, 1997), pp. 231 282], Clarida et al. [J. Econ. Lit. 37, 1661 (1999)], Svensson [J. Mon. Econ. 43, 607 (1999)] and Woodford [ Interest and Prices: Foundations of a Theory of Monetary Policy (Princeton, New Jersey, Princeton University Press, 2003)]. In this paper we extend the standard optimal monetary policy model by introducing nonlinearity into the Phillips curve. Under the specific form of nonlinearity proposed in our paper (which allows for convexity and concavity and secures closed form solutions), we show that the introduction of a nonlinear Phillips curve into the structure of the standard model in a discrete time and deterministic framework produces radical changes to the major conclusions regarding stability and the efficiency of monetary policy. We emphasize the following main results: (i) instead of a unique fixed point we end up with multiple equilibria; (ii) instead of saddle-path stability, for different sets of parameter values we may have saddle stability, totally unstable equilibria and chaotic attractors; (iii) for certain degrees of convexity and/or concavity of the Phillips curve, where endogenous fluctuations arise, one is able to encounter various results that seem intuitively correct. Firstly, when the Central Bank pays attention essentially to inflation targeting, the inflation rate has a lower mean and is less volatile; secondly, when the degree of price stickiness is high, the inflation rate displays a larger mean and higher volatility (but this is sensitive to the values given to the parameters of the model); and thirdly, the higher the target value of the output gap chosen by the Central Bank, the higher is the inflation rate and its volatility.

First Steps in the Smart Grid Framework: An Optimal and Feasible Pathway Toward Power System Reform in Mexico

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bracho, Riccardo; Linvill, Carl; Sedano, Richard

With the vision to transform the power sector, Mexico included in the new laws and regulations deployment of smart grid technologies and provided various attributes to the Ministry of Energy and the Energy Regulatory Commission to enact public policies and regulation. The use of smart grid technologies can have a significant impact on the integration of variable renewable energy resources while maintaining reliability and stability of the system, significantly reducing technical and non-technical electricity losses in the grid, improving cyber security, and allowing consumers to make distributed generation and demand response decisions. This report describes for Mexico's Ministry of Energymore » (SENER) an overall approach (Optimal Feasible Pathway) for moving forward with smart grid policy development in Mexico to enable increasing electric generation from renewable energy in a way that optimizes system stability and reliability in an efficient and cost-effective manner.« less

Market penetration of energy supply technologies

NASA Astrophysics Data System (ADS)

Condap, R. J.

1980-03-01

Techniques to incorporate the concepts of profit-induced growth and risk aversion into policy-oriented optimization models of the domestic energy sector are examined. After reviewing the pertinent market penetration literature, simple mathematical programs in which the introduction of new energy technologies is constrained primarily by the reinvestment of profits are formulated. The main results involve the convergence behavior of technology production levels under various assumptions about the form of the energy demand function. Next, profitability growth constraints are embedded in a full-scale model of U.S. energy-economy interactions. A rapidly convergent algorithm is developed to utilize optimal shadow prices in the computation of profitability for individual technologies. Allowance is made for additional policy variables such as government funding and taxation. The result is an optimal deployment schedule for current and future energy technologies which is consistent with the sector's ability to finance capacity expansion.

Fire assisted pastoralism vs. sustainable forestry--the implications of missing markets for carbon in determining optimal land use in the wet-dry tropics of Australia.

PubMed

Ockwell, David; Lovett, Jon C

2005-04-01

Using Cape York Peninsula, Queensland, Australia as a case study, this paper combines field sampling of woody vegetation with cost-benefit analysis to compare the social optimality of fire-assisted pastoralism with sustainable forestry. Carbon sequestration is estimated to be significantly higher in the absence of fire. Integration of carbon sequestration benefits for mitigating future costs of climate change into cost-benefit analysis demonstrates that sustainable forestry is a more socially optimal land use than fire-assisted pastoralism. Missing markets for carbon, however, imply that fire-assisted pastoralism will continue to be pursued in the absence of policy intervention. Creation of markets for carbon represents a policy solution that has the potential to drive land use away from fire-assisted pastoralism towards sustainable forestry and environmental conservation.

a Stochastic Approach to Multiobjective Optimization of Large-Scale Water Reservoir Networks

NASA Astrophysics Data System (ADS)

Bottacin-Busolin, A.; Worman, A. L.

2013-12-01

A main challenge for the planning and management of water resources is the development of multiobjective strategies for operation of large-scale water reservoir networks. The optimal sequence of water releases from multiple reservoirs depends on the stochastic variability of correlated hydrologic inflows and on various processes that affect water demand and energy prices. Although several methods have been suggested, large-scale optimization problems arising in water resources management are still plagued by the high dimensional state space and by the stochastic nature of the hydrologic inflows. In this work, the optimization of reservoir operation is approached using approximate dynamic programming (ADP) with policy iteration and function approximators. The method is based on an off-line learning process in which operating policies are evaluated for a number of stochastic inflow scenarios, and the resulting value functions are used to design new, improved policies until convergence is attained. A case study is presented of a multi-reservoir system in the Dalälven River, Sweden, which includes 13 interconnected reservoirs and 36 power stations. Depending on the late spring and summer peak discharges, the lowlands adjacent to Dalälven can often be flooded during the summer period, and the presence of stagnating floodwater during the hottest months of the year is the cause of a large proliferation of mosquitos, which is a major problem for the people living in the surroundings. Chemical pesticides are currently being used as a preventive countermeasure, which do not provide an effective solution to the problem and have adverse environmental impacts. In this study, ADP was used to analyze the feasibility of alternative operating policies for reducing the flood risk at a reasonable economic cost for the hydropower companies. To this end, mid-term operating policies were derived by combining flood risk reduction with hydropower production objectives. The performance of the resulting policies was evaluated by simulating the online operating process for historical inflow scenarios and synthetic inflow forecasts. The simulations are based on a combined mid- and short-term planning model in which the value function derived in the mid-term planning phase provides the value of the policy at the end of the short-term operating horizon. While a purely deterministic linear analysis provided rather optimistic results, the stochastic model allowed for a more accurate evaluation of trade-offs and limitations of alternative operating strategies for the Dalälven reservoir network.

Low utilization of HIV testing during pregnancy: What are the barriers to HIV testing for women in rural India?

PubMed

Sinha, Gita; Dyalchand, Ashok; Khale, Manisha; Kulkarni, Gopal; Vasudevan, Shubha; Bollinger, Robert C

2008-02-01

Sixty percent of India's HIV cases occur in rural residents. Despite government policy to expand antenatal HIV screening and prevention of maternal-to-child transmission (PMTCT), little is known about HIV testing among rural women during pregnancy. Between January and March 2006, a cross-sectional sample of 400 recently pregnant women from rural Maharashtra was administered a questionnaire regarding HIV awareness, risk, and history of antenatal HIV testing. Thirteen women (3.3%) reported receiving antenatal HIV testing. Neither antenatal care utilization nor history of sexually transmitted infection (STI) symptoms influenced odds of receiving HIV testing. Women who did not receive HIV testing, compared with women who did, were 95% less likely to have received antenatal HIV counseling (odds ratio = 0.05, 95% confidence interval: 0.02 to 0.17) and 80% less aware of an existing HIV testing facility (odds ratio = 0.19, 95% confidence interval: 0.04 to 0.75). Despite measurable HIV prevalence, high antenatal care utilization, and STI symptom history, recently pregnant rural Indian women report low HIV testing. Barriers to HIV testing during pregnancy include lack of discussion by antenatal care providers and lack of awareness of existing testing services. Provider-initiated HIV counseling and testing during pregnancy would optimize HIV prevention for women throughout rural India.

Welfare implications of energy and environmental policies: A general equilibrium approach

NASA Astrophysics Data System (ADS)

Iqbal, Mohammad Qamar

Government intervention and implementation of policies can impose a financial and social cost. To achieve a desired goal there could be several different alternative policies or routes, and government would like to choose the one which imposes the least social costs or/and generates greater social benefits. Therefore, applied welfare economics plays a vital role in public decision making. This paper recasts welfare measure such as equivalent variation, in terms of the prices of factors of production rather than product prices. This is made possible by using duality theory within a general equilibrium framework and by deriving alternative forms of indirect utility functions and expenditure functions in factor prices. Not only we are able to recast existing welfare measures in factor prices, we are able to perform a true cost-benefit analysis of government policies using comparative static analysis of different equilibria and breaking up monetary measure of welfare change such as equivalent variation into its components. A further advantage of our research is demonstrated by incorporating externalities and public goods in the utility function. It is interesting that under a general equilibrium framework optimal income tax tends to reduce inequalities. Results show that imposition of taxes at socially optimal rates brings a net gain to the society. It was also seen that even though a pollution tax may reduce GDP, it leads to an increase in the welfare of the society if it is imposed at an optimal rate.

Assessing Screening Policies for Childhood Obesity

PubMed Central

Wein, Lawrence M.; Yang, Yan; Goldhaber-Fiebert, Jeremy D.

2014-01-01

To address growing concerns over childhood obesity, the United States Preventive Services Task Force (USPSTF) recently recommended that children undergo obesity screening beginning at age 6 [1]. An Expert Committee recommends starting at age 2 [2]. Analysis is needed to assess these recommendations and investigate whether there are better alternatives. We model the age- and sex-specific population-wide distribution of body mass index (BMI) through age 18 using National Longitudinal Survey of Youth data [3]. The impact of treatment on BMI is estimated using the targeted systematic review performed to aid the USPSTF [4]. The prevalence of hypertension and diabetes at age 40 are estimated from the Panel Study of Income Dynamics [5]. We fix the screening interval at 2 years, and derive the age- and sex-dependent BMI thresholds that minimize adult disease prevalence, subject to referring a specified percentage of children for treatment yearly. We compare this optimal biennial policy to biennial versions of the USPSTF and Expert Committee recommendations. Compared to the USPSTF recommendation, the optimal policy reduces adult disease prevalence by 3% in relative terms (the absolute reductions are < 1%) at the same treatment referral rate, or achieves the same disease prevalence at a 28% reduction in treatment referral rate. If compared to the Expert Committee recommendation, the reductions change to 6% and 40%, respectively. The optimal policy treats mostly 16 year olds and few children under age 14. Our results suggest that adult disease is minimized by focusing childhood obesity screening and treatment on older adolescents. PMID:22240724

Regulation of suspended particulate matter (SPM) in Indian coal-based thermal power plants

NASA Astrophysics Data System (ADS)

Sengupta, Ishita

Air borne particulate matter, in major Indian cities is at least three times the standard prescribed by the WHO. Coal-based thermal power plants are the major emitters of particulate matter in India. The lack of severe penalty for non-compliance with the standards has worsened the situation and thus calls for an immediate need for investment in technologies to regulate particulate emissions. My dissertation studies the optimal investment decisions in a dynamic framework, for a random sample of forty Indian coal-based power plants to abate particulate emissions. I used Linear Programming to solve the double cost minimization problem for the optimal choices of coal, boiler and pollution-control equipment. A policy analysis is done to choose over various tax policies, which would induce the firms to adopt the energy efficient as well as cost efficient technology. The aim here is to reach the WHO standards. Using the optimal switching point model I show that in a dynamic set up, switching the boiler immediately is always the cost effective option for all the power plants even if there is no policy restriction. The switch to a baghouse depends upon the policy in place. Theoretically, even though an emission tax is considered the most efficient tax, an ash tax or a coal tax can also be considered to be a good substitute especially in countries like India where monitoring costs are very high. As SPM is a local pollutant the analysis here is mainly firm specific.

Quasi-experiments to establish causal effects of HIV care and treatment and to improve the cascade of care

PubMed Central

Bor, Jacob; Geldsetzer, Pascal; Venkataramani, Atheendar; Bärnighausen, Till

2015-01-01

Purpose of review Randomized, population-representative trials of clinical interventions are rare. Quasi-experiments have been used successfully to generate causal evidence on the cascade of HIV care in a broad range of real-world settings. Recent findings Quasi-experiments exploit exogenous, or quasi-random, variation occurring naturally in the world or because of an administrative rule or policy change to estimate causal effects. Well designed quasi-experiments have greater internal validity than typical observational research designs. At the same time, quasi-experiments may also have potential for greater external validity than experiments and can be implemented when randomized clinical trials are infeasible or unethical. Quasi-experimental studies have established the causal effects of HIV testing and initiation of antiretroviral therapy on health, economic outcomes and sexual behaviors, as well as indirect effects on other community members. Recent quasi-experiments have evaluated specific interventions to improve patient performance in the cascade of care, providing causal evidence to optimize clinical management of HIV. Summary Quasi-experiments have generated important data on the real-world impacts of HIV testing and treatment and on interventions to improve the cascade of care. With the growth in large-scale clinical and administrative data, quasi-experiments enable rigorous evaluation of policies implemented in real-world settings. PMID:26371463

Quasi-experiments to establish causal effects of HIV care and treatment and to improve the cascade of care.

PubMed

Bor, Jacob; Geldsetzer, Pascal; Venkataramani, Atheendar; Bärnighausen, Till

2015-11-01

Randomized, population-representative trials of clinical interventions are rare. Quasi-experiments have been used successfully to generate causal evidence on the cascade of HIV care in a broad range of real-world settings. Quasi-experiments exploit exogenous, or quasi-random, variation occurring naturally in the world or because of an administrative rule or policy change to estimate causal effects. Well designed quasi-experiments have greater internal validity than typical observational research designs. At the same time, quasi-experiments may also have potential for greater external validity than experiments and can be implemented when randomized clinical trials are infeasible or unethical. Quasi-experimental studies have established the causal effects of HIV testing and initiation of antiretroviral therapy on health, economic outcomes and sexual behaviors, as well as indirect effects on other community members. Recent quasi-experiments have evaluated specific interventions to improve patient performance in the cascade of care, providing causal evidence to optimize clinical management of HIV. Quasi-experiments have generated important data on the real-world impacts of HIV testing and treatment and on interventions to improve the cascade of care. With the growth in large-scale clinical and administrative data, quasi-experiments enable rigorous evaluation of policies implemented in real-world settings.

Demand side management in recycling and electricity retail pricing

NASA Astrophysics Data System (ADS)

Kazan, Osman

This dissertation addresses several problems from the recycling industry and electricity retail market. The first paper addresses a real-life scheduling problem faced by a national industrial recycling company. Based on their practices, a scheduling problem is defined, modeled, analyzed, and a solution is approximated efficiently. The recommended application is tested on the real-life data and randomly generated data. The scheduling improvements and the financial benefits are presented. The second problem is from electricity retail market. There are well-known patterns in daily usage in hours. These patterns change in shape and magnitude by seasons and days of the week. Generation costs are multiple times higher during the peak hours of the day. Yet most consumers purchase electricity at flat rates. This work explores analytic pricing tools to reduce peak load electricity demand for retailers. For that purpose, a nonlinear model that determines optimal hourly prices is established based on two major components: unit generation costs and consumers' utility. Both are analyzed and estimated empirically in the third paper. A pricing model is introduced to maximize the electric retailer's profit. As a result, a closed-form expression for the optimal price vector is obtained. Possible scenarios are evaluated for consumers' utility distribution. For the general case, we provide a numerical solution methodology to obtain the optimal pricing scheme. The models recommended are tested under various scenarios that consider consumer segmentation and multiple pricing policies. The recommended model reduces the peak load significantly in most cases. Several utility companies offer hourly pricing to their customers. They determine prices using historical data of unit electricity cost over time. In this dissertation we develop a nonlinear model that determines optimal hourly prices with parameter estimation. The last paper includes a regression analysis of the unit generation cost function obtained from Independent Service Operators. A consumer experiment is established to replicate the peak load behavior. As a result, consumers' utility function is estimated and optimal retail electricity prices are computed.

Policies of Global English Tests: Test-Takers' Perspectives on the IELTS Retake Policy

ERIC Educational Resources Information Center

Hamid, M. Obaidul

2016-01-01

Globalized English proficiency tests such as the International English Language Testing System (IELTS) are increasingly playing the role of gatekeepers in a globalizing world. Although the use of the IELTS as a "policy tool" for making decisions in the areas of study, work and migration impacts on test-takers' lives and life chances, not…

The Cultural Politics of National Testing and Test Result Release Policy in South Korea: A Critical Discourse Analysis

ERIC Educational Resources Information Center

Sung, Youl-Kwan; Kang, Mi Ok

2012-01-01

This paper examines the ideological construction of educational discourses embedded within the South Korean print media. Significantly, these discourses have recently promoted the resurrection of a sweeping national testing and test results release policy. Through careful examination of the "test plus release" policy, the authors show…

Optimal monetary policy and oil price shocks

NASA Astrophysics Data System (ADS)

Kormilitsina, Anna

This dissertation is comprised of two chapters. In the first chapter, I investigate the role of systematic U.S. monetary policy in the presence of oil price shocks. The second chapter is devoted to studying different approaches to modeling energy demand. In an influential paper, Bernanke, Gertler, and Watson (1997) and (2004) argue that systematic monetary policy exacerbated the recessions the U.S. economy experienced in the aftermath of post World War II oil price shocks. In the first chapter of this dissertation, I critically evaluate this claim in the context of an estimated medium-scale model of the U.S. business cycle. Specifically, I solve for the Ramsey optimal monetary policy in the medium-scale dynamic stochastic general equilibrium model (henceforth DSGE) of Schmitt-Grohe and Uribe (2005). To model the demand for oil, I use the approach of Finn (2000). According to this approach, the utilization of capital services requires oil usage. In the related literature on the macroeconomic effects of oil price shocks, it is common to calibrate structural parameters of the model. In contrast to this literature, I estimate the parameters of my DSGE model. The estimation strategy involves matching the impulse responses from the theoretical model to responses predicted by an empirical model. For estimation, I use the alternative to the classical Laplace type estimator proposed by Chernozhukov and Hong (2003). To obtain the empirical impulse responses, I identify an oil price shock in a structural VAR (SVAR) model of the U.S. business cycle. The SVAR model predicts that, in response to an oil price increase, GDP, investment, hours, capital utilization, and the real wage fall, while the nominal interest rate and inflation rise. These findings are economically intuitive and in line with the existing empirical evidence. Comparing the actual and the Ramsey optimal monetary policy response to an oil price shock, I find that the optimal policy allows for more inflation, a larger drop in wages, and a rise in hours compared to those actually observed. The central finding of this Chapter is that the optimal policy is associated with a smaller drop in GDP and other macroeconomic variables. The latter results therefore confirm the claim of Bernanke, Gertler and Watson that monetary policy was to a large extent responsible for the recessions that followed the oil price shocks. However, under the optimal policy, interest rates are tightened even more than what is predicted by the empirical model. This result contrasts sharply with the claim of Bernanke, Gertler, and Watson that the Federal Reserve exacerbated recessions by the excessive tightening of interest rates in response to the oil price increases. In contrast to related studies that focus on output stabilization, I find that eliminating the negative response of GDP to an oil price shock is not desirable. In the second chapter of this dissertation, I compare two approaches to modeling energy sector. Because the share of energy in GDP is small, models of energy have been criticized for their inability to explain sizeable effects of energy price increases on the economic activity. I find that if the price of energy is an exogenous AR(1) process, then the two modeling approaches produce the responses of GDP similar in size to responses observed in most empirical studies, but fail to produce the timing and the shape of the response. DSGE framework can solve the timing and the shape of impulse responses problem, however, fails to replicate the size of the impulse responses. Thus, in DSGE frameworks, amplifying mechanisms for the effect of the energy price shock and estimation based calibration of model parameters are needed to produce the size of the GDP response to the energy price shock.

On the performance of updating Stochastic Dynamic Programming policy using Ensemble Streamflow Prediction in a snow-covered region

NASA Astrophysics Data System (ADS)

Martin, A.; Pascal, C.; Leconte, R.

2014-12-01

Stochastic Dynamic Programming (SDP) is known to be an effective technique to find the optimal operating policy of hydropower systems. In order to improve the performance of SDP, this project evaluates the impact of re-updating the policy at every time step by using Ensemble Streamflow Prediction (ESP). We present a case study of the Kemano's hydropower system on the Nechako River in British Columbia, Canada. Managed by Rio Tinto Alcan (RTA), this system is subject to large streamflow volumes in spring due to important amount of snow depth during the winter season. Therefore, the operating policy should not only maximize production but also minimize the risk of flooding. The hydrological behavior of the system is simulated with CEQUEAU, a distributed and deterministic hydrological model developed by the Institut national de la recherche scientifique - Eau, Terre et Environnement (INRS-ETE) in Quebec, Canada. On each decision time step, CEQUEAU is used to generate ESP scenarios based on historical meteorological sequences and the current state of the hydrological model. These scenarios are used into the SDP to optimize the new release policy for the next time steps. This routine is then repeated over the entire simulation period. Results are compared with those obtained by using SDP on historical inflow scenarios.

Optimal harvesting of a stochastic delay logistic model with Lévy jumps

NASA Astrophysics Data System (ADS)

Qiu, Hong; Deng, Wenmin

2016-10-01

The optimal harvesting problem of a stochastic time delay logistic model with Lévy jumps is considered in this article. We first show that the model has a unique global positive solution and discuss the uniform boundedness of its pth moment with harvesting. Then we prove that the system is globally attractive and asymptotically stable in distribution under our assumptions. Furthermore, we obtain the existence of the optimal harvesting effort by the ergodic method, and then we give the explicit expression of the optimal harvesting policy and maximum yield.

Optimal and Approximately Optimal Control Policies for Queues in Heavy Traffic,

DTIC Science & Technology

1987-03-01

optimal and ’nearly optimal’ control problems for the open queueing networks in heavy traffic of the type dealt with in the fundamental papers of Reiman ...then the covariance is precisely that obtained by Reiman [1] (with a different notation used there). It is evident from (4.4) and the cited...wU’ ’U, d A K . " -50- References [1] M.I. Reiman , "Open queueing networks in heavy traffic", Math. of Operations Research, 9, 1984, p. 441-458. [2] J

Fixed and equilibrium endpoint problems in uneven-aged stand management

Treesearch

Robert G. Haight; Wayne M. Getz

1987-01-01

Studies in uneven-aged management have concentrated on the determination of optimal steady-state diameter distribution harvest policies for single and mixed species stands. To find optimal transition harvests for irregular stands, either fixed endpoint or equilibrium endpoint constraints can be imposed after finite transition periods. Penalty function and gradient...

Using Markov Models of Fault Growth Physics and Environmental Stresses to Optimize Control Actions

NASA Technical Reports Server (NTRS)

Bole, Brian; Goebel, Kai; Vachtsevanos, George

2012-01-01

A generalized Markov chain representation of fault dynamics is presented for the case that available modeling of fault growth physics and future environmental stresses can be represented by two independent stochastic process models. A contrived but representatively challenging example will be presented and analyzed, in which uncertainty in the modeling of fault growth physics is represented by a uniformly distributed dice throwing process, and a discrete random walk is used to represent uncertain modeling of future exogenous loading demands to be placed on the system. A finite horizon dynamic programming algorithm is used to solve for an optimal control policy over a finite time window for the case that stochastic models representing physics of failure and future environmental stresses are known, and the states of both stochastic processes are observable by implemented control routines. The fundamental limitations of optimization performed in the presence of uncertain modeling information are examined by comparing the outcomes obtained from simulations of an optimizing control policy with the outcomes that would be achievable if all modeling uncertainties were removed from the system.

Consequential Validity and the Transformation of Tests from Measurement Tools to Policy Tools

ERIC Educational Resources Information Center

Welner, Kevin G.

2013-01-01

Background/Context: Recent U.S. policy has brought a shift in assessment use, from measurement tools to policy levers. In particular, testing has become a core part of teacher evaluation policies in many states, with test results becoming akin to a job evaluation. Purpose: To explore the notion of consequential validity in assessment use and…

Rationalization and Student/School Personhood in U.S. College Admissions: The Rise of Test-Optional Policies, 1987 to 2015

ERIC Educational Resources Information Center

Furuta, Jared

2017-01-01

This article examines the rise of "test-optional" college admissions policies since the 1990s. I argue that the rationalization of college admissions policies after World War II contributed to the rise of "meritocratic" stratification (in policy) and standardized tests, like the SAT, but it also led to the expansion and…

Are Small Schools Better? School Size Considerations for Safety & Learning. Policy Brief.

ERIC Educational Resources Information Center

McRobbie, Joan

New studies from the 1990s have strengthened an already notable consensus on school size: smaller is better. This policy brief outlines research findings on why size makes a difference, how small is small enough, effective approaches to downsizing, and key barriers. No agreement exists at present on optimal school size, but research suggests a…

Leveraging University Research to Serve Economic Development: An Analysis of Policy Dynamics in and across Three US States

ERIC Educational Resources Information Center

Warshaw, Jarrett B.; Hearn, James C.

2014-01-01

As economic competition becomes more global and knowledge-based, US states have independently pursued initiatives in research and development (R&D) and science and technology (S&T). Policy efforts often entwine government, universities, and industry, aiming to stimulate socially optimal levels of innovation and economic growth.…

Shaping Aid Policy for Increasing Revenue: Examining Admission Status, Unmet Need, and Demographic Characteristics as Persistence Predictors

ERIC Educational Resources Information Center

Sheren, Deborah L.

2018-01-01

Increasing costs and discount rates and decreasing persistence have led to deteriorating net tuition revenue at many colleges and universities. The lack of clarity about the relationship between student persistence and incoming student characteristics was interfering with the development of optimal tuition discounting policy and required research.…

Corrective Control to Handle Forecast Uncertainty: A Chance Constrained Optimal Power Flow

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roald, Line; Misra, Sidhant; Krause, Thilo

Higher shares of electricity generation from renewable energy sources and market liberalization is increasing uncertainty in power systems operation. At the same time, operation is becoming more flexible with improved control systems and new technology such as phase shifting transformers (PSTs) and high voltage direct current connections (HVDC). Previous studies have shown that the use of corrective control in response to outages contributes to a reduction in operating cost, while maintaining N-1 security. In this work, we propose a method to extend the use of corrective control of PSTs and HVDCs to react to uncertainty. We characterize the uncertainty asmore » continuous random variables, and define the corrective control actions through affine control policies. This allows us to efficiently model control reactions to a large number of uncertainty sources. The control policies are then included in a chance constrained optimal power flow formulation, which guarantees that the system constraints are enforced with a desired probability. Lastly, by applying an analytical reformulation of the chance constraints, we obtain a second-order cone problem for which we develop an efficient solution algorithm. In a case study for the IEEE 118 bus system, we show that corrective control for uncertainty leads to a decrease in operational cost, while maintaining system security. Further, we demonstrate the scalability of the method by solving the problem for the IEEE 300 bus and the Polish system test cases.« less

Internet of People: Opportunities and challenges for engaging stakeholders in watershed planning via the Web

NASA Astrophysics Data System (ADS)

Babbar-Sebens, M.

2016-12-01

Social computing technologies are transforming the way our society interacts and generates content on the Web via collective intelligence. Previously unimagined possibilities have arisen for using these technologies to engage stakeholders and involve them in policy making and planning efforts. While the Internet has been used in the past to support education and communication endeavors, we have developed a novel, web-based, interactive planning tool that engages the community in using science-based methods for the design of potential conservation practices on their landscape, and thereby, reducing undesirable impacts of extreme hydroclimatic events. The tool, Watershed REstoration using Spatio-Temporal Optimization of Resources (WRESTORE), uses a democratic voting process coupled with visualization interfaces, computational simulation and optimization models, and user modeling techniques to support a human-centered design approach. This human-centered design approach, which is reinforced by use of Web 2.0 technologies, has the potential to enable policy makers to connect to a larger community of stakeholders and directly engage them in environmental stewardship efforts. Additionally, the design framework can be used by watershed groups to plug-in their own hydrologic models, climate observations and forecasts, and various other simulation models unique to their watersheds. In this presentation, we will demonstrate the effectiveness of WRESTORE for designing alternatives of conservation practices in a HUC-11 Midwestern watershed, results of various experiments with a diverse set of test users and stakeholders, and discuss potential for future developments.

Corrective Control to Handle Forecast Uncertainty: A Chance Constrained Optimal Power Flow

DOE PAGES

Roald, Line; Misra, Sidhant; Krause, Thilo; ...

2016-08-25

Higher shares of electricity generation from renewable energy sources and market liberalization is increasing uncertainty in power systems operation. At the same time, operation is becoming more flexible with improved control systems and new technology such as phase shifting transformers (PSTs) and high voltage direct current connections (HVDC). Previous studies have shown that the use of corrective control in response to outages contributes to a reduction in operating cost, while maintaining N-1 security. In this work, we propose a method to extend the use of corrective control of PSTs and HVDCs to react to uncertainty. We characterize the uncertainty asmore » continuous random variables, and define the corrective control actions through affine control policies. This allows us to efficiently model control reactions to a large number of uncertainty sources. The control policies are then included in a chance constrained optimal power flow formulation, which guarantees that the system constraints are enforced with a desired probability. Lastly, by applying an analytical reformulation of the chance constraints, we obtain a second-order cone problem for which we develop an efficient solution algorithm. In a case study for the IEEE 118 bus system, we show that corrective control for uncertainty leads to a decrease in operational cost, while maintaining system security. Further, we demonstrate the scalability of the method by solving the problem for the IEEE 300 bus and the Polish system test cases.« less

Physical, policy, and sociocultural characteristics of the primary school environment are positively associated with children's physical activity during class time.

PubMed

Martin, Karen; Bremner, Alexandra; Salmon, Jo; Rosenberg, Michael; Giles-Corti, Billie

2014-03-01

The objective of this study was to develop a multidomain model to identify key characteristics of the primary school environment associated with children's physical activity (PA) during class-time. Accelerometers were used to calculate time spent in moderate-to-vigorous physical activity during class-time (CMVPA) of 408 sixth-grade children (mean ± SD age 11.1 ± 0.43 years) attending 27 metropolitan primary schools in Perth Western Australia. Child and staff self-report instruments and a school physical environment scan administered by the research team were used to collect data about children and the class and school environments. Hierarchical modeling identified key variables associated with CMVPA. The final multilevel model explained 49% of CMVPA. A physically active physical education (PE) coordinator, fitness sessions incorporated into PE sessions and either a trained PE specialist, classroom teacher or nobody coordinating PE in the school, rather than the deputy principal, were associated with higher CMVPA. The amount of grassed area per student and sporting apparatus on grass were also associated with higher CMVPA. These results highlight the relevance of the school's sociocultural, policy and physical environments in supporting class-based PA. Interventions testing optimization of the school physical, sociocultural and policy environments to support physical activity are warranted.

Considerations When Including Students with Disabilities in Test Security Policies. NCEO Policy Directions. Number 23

ERIC Educational Resources Information Center

Lazarus, Sheryl; Thurlow, Martha

2015-01-01

Sound test security policies and procedures are needed to ensure test security and confidentiality, and to help prevent cheating. In this era when cheating on tests draws regular media attention, there is a need for thoughtful consideration of the ways in which possible test security measures may affect accessibility for some students with…

Using economic instruments to develop effective management of invasive species: insights from a bioeconomic model.

PubMed

McDermott, Shana M; Irwin, Rebecca E; Taylor, Brad W

2013-07-01

Economic growth is recognized as an important factor associated with species invasions. Consequently, there is increasing need to develop solutions that combine economics and ecology to inform invasive species management. We developed a model combining economic, ecological, and sociological factors to assess the degree to which economic policies can be used to control invasive plants. Because invasive plants often spread across numerous properties, we explored whether property owners should manage invaders cooperatively as a group by incorporating the negative effects of invader spread in management decisions (collective management) or independently, whereby the negative effects of invasive plant spread are ignored (independent management). Our modeling approach used a dynamic optimization framework, and we applied the model to invader spread using Linaria vulgaris. Model simulations allowed us to determine the optimal management strategy based on net benefits for a range of invader densities. We found that optimal management strategies varied as a function of initial plant densities. At low densities, net benefits were high for both collective and independent management to eradicate the invader, suggesting the importance of early detection and eradication. At moderate densities, collective management led to faster and more frequent invader eradication compared to independent management. When we used a financial penalty to ensure that independent properties were managed collectively, we found that the penalty would be most feasible when levied on a property's perimeter boundary to control spread among properties. At the highest densities, the optimal management strategy was "do nothing" because the economic costs of removal were too high relative to the benefits of removal. Spatial variation in L. vulgaris densities resulted in different optimal management strategies for neighboring properties, making a formal economic policy to encourage invasive species removal critical. To accomplish the management and enforcement of these economic policies, we discuss modification of existing agencies and infrastructure. Finally, a sensitivity analysis revealed that lowering the economic cost of invader removal would strongly increase the probability of invader eradication. Taken together, our results provide quantitative insight into management decisions and economic policy instruments that can encourage invasive species removal across a social landscape.

Eliminating Standardized Tests in College Admissions: The New Affirmative Action?

ERIC Educational Resources Information Center

Zwick, Rebecca

1999-01-01

Eliminating the Scholastic Aptitude Test for college admissions might seem a form of covert affirmative action. Although it is possible to design a workable admissions policy that excludes standardized tests (as 15 percent of colleges have done), banishing admissions tests to further a social-policy goal indirectly is unsound policy. (Contains 25…

Proposed Policy: Drug Testing of Hawaii's Public School Teachers

ERIC Educational Resources Information Center

Davis, Bebi

2007-01-01

Because of a proposed policy, public school teachers in Hawaii are facing the possibility of being randomly tested for illegal drugs. Random drug testing has many implications and its impact is questionable. In this article, the author scrutinizes the controversial drug-testing policy for both troubling and promising aspects and how educators may…

[Care continuity for patients with Prader-Willi syndrome during transition from childhood to adulthood].

PubMed

Saitoh, Shinji

2010-01-01

Prader-Willi syndrome(PWS) is a complex multisystem genetic disorder, of which characteristic phenotypes include neonatal hypotonia, hyperphagia resulting in obesity, mental retardation, hypogonadism, and behavioral and psychiatric problems. The diagnosis can be obtained as early as during neonatal period thanks to development of genetic testing. Clinical features of PWS will change depending on age, although core phenotypes of hyperphagia, obesity and psychiatric issues stay for lifetime. Therefore, integrated multidisciplinary approach starting from neonatal period is mandatory to ensure optimal management to improve lifelong quality of life. For successful transition from childhood to adulthood, multidisciplinary team need to share clinical information, and should keep the same policy about food, environment and psychiatric issues.

Optimal startup control of a jacketed tubular reactor.

NASA Technical Reports Server (NTRS)

Hahn, D. R.; Fan, L. T.; Hwang, C. L.

1971-01-01

The optimal startup policy of a jacketed tubular reactor, in which a first-order, reversible, exothermic reaction takes place, is presented. A distributed maximum principle is presented for determining weak necessary conditions for optimality of a diffusional distributed parameter system. A numerical technique is developed for practical implementation of the distributed maximum principle. This involves the sequential solution of the state and adjoint equations, in conjunction with a functional gradient technique for iteratively improving the control function.

On wavelet analysis of auditory evoked potentials.

PubMed

Bradley, A P; Wilson, W J

2004-05-01

To determine a preferred wavelet transform (WT) procedure for multi-resolution analysis (MRA) of auditory evoked potentials (AEP). A number of WT algorithms, mother wavelets, and pre-processing techniques were examined by way of critical theoretical discussion followed by experimental testing of key points using real and simulated auditory brain-stem response (ABR) waveforms. Conclusions from these examinations were then tested on a normative ABR dataset. The results of the various experiments are reported in detail. Optimal AEP WT MRA is most likely to occur when an over-sampled discrete wavelet transformation (DWT) is used, utilising a smooth (regularity >or=3) and symmetrical (linear phase) mother wavelet, and a reflection boundary extension policy. This study demonstrates the practical importance of, and explains how to minimize potential artefacts due to, 4 inter-related issues relevant to AEP WT MRA, namely shift variance, phase distortion, reconstruction smoothness, and boundary artefacts.

A cognitive prosthesis for complex decision-making.

PubMed

Tremblay, Sébastien; Gagnon, Jean-François; Lafond, Daniel; Hodgetts, Helen M; Doiron, Maxime; Jeuniaux, Patrick P J M H

2017-01-01

While simple heuristics can be ecologically rational and effective in naturalistic decision making contexts, complex situations require analytical decision making strategies, hypothesis-testing and learning. Sub-optimal decision strategies - using simplified as opposed to analytic decision rules - have been reported in domains such as healthcare, military operational planning, and government policy making. We investigate the potential of a computational toolkit called "IMAGE" to improve decision-making by developing structural knowledge and increasing understanding of complex situations. IMAGE is tested within the context of a complex military convoy management task through (a) interactive simulations, and (b) visualization and knowledge representation capabilities. We assess the usefulness of two versions of IMAGE (desktop and immersive) compared to a baseline. Results suggest that the prosthesis helped analysts in making better decisions, but failed to increase their structural knowledge about the situation once the cognitive prosthesis is removed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Co-Optimization of Electricity Transmission and Generation Resources for Planning and Policy Analysis: Review of Concepts and Modeling Approaches

DOE Office of Scientific and Technical Information (OSTI.GOV)

Krishnan, Venkat; Ho, Jonathan; Hobbs, Benjamin F.

2016-05-01

The recognition of transmission's interaction with other resources has motivated the development of co-optimization methods to optimize transmission investment while simultaneously considering tradeoffs with investments in electricity supply, demand, and storage resources. For a given set of constraints, co-optimized planning models provide solutions that have lower costs than solutions obtained from decoupled optimization (transmission-only, generation-only, or iterations between them). This paper describes co-optimization and provides an overview of approaches to co-optimizing transmission options, supply-side resources, demand-side resources, and natural gas pipelines. In particular, the paper provides an up-to-date assessment of the present and potential capabilities of existing co-optimization tools, andmore » it discusses needs and challenges for developing advanced co-optimization models.« less

Essays on carbon abatement and electricity markets

NASA Astrophysics Data System (ADS)

Taber, John Timothy

In the first chapter of this dissertation, I study the effects of a number of policies which affect the electric grid using the SuperOPF, a full AC optimization/simulation framework with optimal investment developed at Cornell University. A 36-node model of the Northeast Power Coordinating Council is used to test policies that aim to reduce CO2, other emissions, or otherwise impact the operation of the electric grid: a base case, with no new environmental legislation; enactment of the Kerry-Lieberman CO2 allowance proposal in 2012; following Fukishima, a retirement of all US nuclear plants by 2022 with and without Kerry-Lieberman; marginal damages from SO2 and NOX emissions charged to coal, gas and oil-fired generation; plug-in hybrid electric vehicle load filling; wind incentives in place; and two cases which combine these. The cases suggest that alternative policies may have very different outcomes in terms of electricity prices, emissions, and health outcomes. In all cases, however, the optimal strategy for future investment is investment in new natural gas combined cycle plants. Policies can change how much new generation is built, whether other plants are built, or what types of plants are retired. The second chapter of my dissertation utilizes the SuperOPF and the model of the Northeast Power Coordinating Council to analyze the issue of carbon leakage. I analyze the effects of a regionally-limited carbon cap and trade program, the Regional Greenhouse Initiative (RGGI), when additional generating assets in non-affected states are included in the analysis. In the face of different carbon prices on generating assets in covered and non-covered states, generation is expected to shift from states bound by RGGI to states outside of RGGI. This carbon leakage may undermine some or all of the benefits of RGGI while simultaneously increasing prices for customers in the area. Even though carbon prices under RGGI are very low, some leakage is occurring, and this leakage will worsen if carbon prices increase. Ultimately, a unified policy offers greater carbon reduction at a lower cost, which would increase popular acceptance of such policies. In the third chapter of this dissertation, my coauthors and I examine the issue of demand for carbon reductions. Recent large-scale field experiments have shown that peer information nudges can have significant effects on behavior, inducing people to reduce their production of negative externalities. Related work in psychology demonstrates that inducing feelings of personal culpability by showing people information about their peers can induce pro-social behavior. This study uses a contingent valuation experiment and a parallel lab experiment to further explore patterns of responses that have been suggested in the emerging literature on norm-based environmental interventions The field-level finding of asymmetric responses between those whose environmental or group impacts are above or below the norm is found to be robust across decision settings. However, substantial heterogeneity in responses to peer information is observed across a number of demographic and other respondent-specific dimensions not able to be explored in large scale field experiments, raising questions about the universality of peer-information effects and the design of such programs.

Strengthening policy research on infant and young child feeding: An imperative to support countries in scaling up impact on nutrition.

PubMed

Menon, Purnima; Thow, Anne Marie

2017-06-13

Enabling policy environments for nutrition require require evidence to support best practice and engagement with political and policy contexts, as well as leadership, resourcing, advocacy, and technical support. However, research on nutrition policy contexts is limited. The papers in this special supplement on policy contexts for infant and young child feeding (IYCF) in South Asia makes a valuable contribution to understanding the policy landscape and political dynamics in the region and the global literature. Studies included in this special supplement analyzed policy content and stakeholder influence on IYCF in Bangladesh, India, Nepal, Pakistan and Sri Lanka, and assess the role of advocacy in addressing multiple elements of the policy environment. These analyses highlight opportunities to harmonize and manage the demands and interests of multiple actors while strengthening policy to strategically support optimal IYCF as the ultimate goal. They also provide robust examples of research on policy environments and policy change. Further investments in research on policy contexts for nutrition can help to understand and support continued progress towards improved actions for nutrition.

A DSS for sustainable development and environmental protection of agricultural regions.

PubMed

Manos, Basil D; Papathanasiou, Jason; Bournaris, Thomas; Voudouris, Kostas

2010-05-01

This paper presents a decision support system (DSS) for sustainable development and environmental protection of agricultural regions developed in the framework of the Interreg-Archimed project entitled WaterMap (development and utilization of vulnerability maps for the monitoring and management of groundwater resources in the ARCHIMED areas). Its aim is to optimize the production plan of an agricultural region taking in account the available resources, the environmental parameters, and the vulnerability map of the region. The DSS is based on an optimization multicriteria model. The spatial integration of vulnerability maps in the DSS enables regional authorities to design policies for optimal agricultural development and groundwater protection from the agricultural land uses. The DSS can further be used to simulate different scenarios and policies by the local stakeholders due to changes on different social, economic, and environmental parameters. In this way, they can achieve alternative production plans and agricultural land uses as well as to estimate economic, social, and environmental impacts of different policies. The DSS is computerized and supported by a set of relational databases. The corresponding software has been developed in a Microsoft Windows XP platform, using Microsoft Visual Basic, Microsoft Access, and the LINDO library. For demonstration reasons, the paper includes an application of the DSS in a region of Northern Greece.

Temperature impacts on economic growth warrant stringent mitigation policy

NASA Astrophysics Data System (ADS)

Moore, Frances C.; Diaz, Delavane B.

2015-02-01

Integrated assessment models compare the costs of greenhouse gas mitigation with damages from climate change to evaluate the social welfare implications of climate policy proposals and inform optimal emissions reduction trajectories. However, these models have been criticized for lacking a strong empirical basis for their damage functions, which do little to alter assumptions of sustained gross domestic product (GDP) growth, even under extreme temperature scenarios. We implement empirical estimates of temperature effects on GDP growth rates in the DICE model through two pathways, total factor productivity growth and capital depreciation. This damage specification, even under optimistic adaptation assumptions, substantially slows GDP growth in poor regions but has more modest effects in rich countries. Optimal climate policy in this model stabilizes global temperature change below 2 °C by eliminating emissions in the near future and implies a social cost of carbon several times larger than previous estimates. A sensitivity analysis shows that the magnitude of climate change impacts on economic growth, the rate of adaptation, and the dynamic interaction between damages and GDP are three critical uncertainties requiring further research. In particular, optimal mitigation rates are much lower if countries become less sensitive to climate change impacts as they develop, making this a major source of uncertainty and an important subject for future research.

Managing technology licensing for stochastic R&D: from the perspective of an enterprise information system

NASA Astrophysics Data System (ADS)

Hong, Xianpei; Zhao, Dan; Wang, Zongjun

2016-10-01

Enterprise information technology (IT) plays an important role in technology innovation management for high-tech enterprises. However, to date most studies on enterprise technology innovation have assumed that the research and development (R&D) outcome is certain. This assumption does not always hold in practice. Motivated by the current practice of some IT industries, we establish a three-stage duopoly game model, including the R&D stage, the licensing stage and the output stage, to investigate the influence of bargaining power and technology spillover on the optimal licensing policy for the innovating enterprise when the outcome of R&D is uncertain. Our results demonstrate that (1) if the licensor has low (high) bargaining power, fixed-fee (royalty) licensing is always superior to royalty (fixed-fee) licensing to the licensor regardless of technology spillover; (2) if the licensor has moderate bargaining power and technology spillover is low (high) as well, fixed-fee (royalty) licensing is superior to royalty (fixed-fee) licensing; (3) under two-part tariff licensing and the assumption of licensors with full bargaining power, if a negative prepaid fixed fee is not allowed, two-part tariff licensing is equivalent to royalty licensing which is the optimal licensing policy; if negative prepaid fixed fee is allowed, the optimal policy is two-part tariff licensing.

Sensitivity Analysis in Sequential Decision Models.

PubMed

Chen, Qiushi; Ayer, Turgay; Chhatwal, Jagpreet

2017-02-01

Sequential decision problems are frequently encountered in medical decision making, which are commonly solved using Markov decision processes (MDPs). Modeling guidelines recommend conducting sensitivity analyses in decision-analytic models to assess the robustness of the model results against the uncertainty in model parameters. However, standard methods of conducting sensitivity analyses cannot be directly applied to sequential decision problems because this would require evaluating all possible decision sequences, typically in the order of trillions, which is not practically feasible. As a result, most MDP-based modeling studies do not examine confidence in their recommended policies. In this study, we provide an approach to estimate uncertainty and confidence in the results of sequential decision models. First, we provide a probabilistic univariate method to identify the most sensitive parameters in MDPs. Second, we present a probabilistic multivariate approach to estimate the overall confidence in the recommended optimal policy considering joint uncertainty in the model parameters. We provide a graphical representation, which we call a policy acceptability curve, to summarize the confidence in the optimal policy by incorporating stakeholders' willingness to accept the base case policy. For a cost-effectiveness analysis, we provide an approach to construct a cost-effectiveness acceptability frontier, which shows the most cost-effective policy as well as the confidence in that for a given willingness to pay threshold. We demonstrate our approach using a simple MDP case study. We developed a method to conduct sensitivity analysis in sequential decision models, which could increase the credibility of these models among stakeholders.

'Worth the test?' Pragmatism, pill testing and drug policy in Australia.

PubMed

Groves, Andrew

2018-04-10

Recent deaths of young Australian music festival attendees from 'party-drug' overdoses have sparked debate about the effectiveness of drug policies. Australia is widely lauded for its harm minimisation approach to drugs, and yet, over the last 30 years, it can be argued its policies have been fragmented, sometimes inconsistent and contradictory. The present article examines the root of this inconsistency, using it as a foundation to advocate for drug policy reform. In keeping with the goals of the National Drug Strategy to promote policy innovation, there is an opportunity to learn from international studies which have shown promising findings in the reduction of party-drug use and its harms through application of pill testing. This paper evaluates Australia's National Drug Strategy and pill testing through a lens of pragmatism, to determine whether there is space for testing practices in contemporary policy. Specifically, the paper analyses current drug policy literature and research studies, examining a range of key drug use indicators, social and political debate and research evidence. The need for policy reform, attitudinal and cultural shifts and development of stronger cross-sectoral partnerships is highlighted, to ensure a rational and logical approach that genuinely tackles drug policy-making and strategy from a broad public health perspective. Using a theoretical frame of pragmatism and drawing from national and international research evidence, this paper recommends the integration of pill testing into Australia's harm minimisation strategy.

Water-Energy-Food Nexus in Asia-Pacific Ring of Fire

NASA Astrophysics Data System (ADS)

Taniguchi, M.; Endo, A.; Gurdak, J. J.; Allen, D. M.; Siringan, F.; Delinom, R.; Shoji, J.; Fujii, M.; Baba, K.

2013-12-01

Climate change and economic development are causing increased pressure on water, energy and food resources, presenting communities with increased levels of tradeoffs and potential conflicts among these resources. Therefore, the water-energy-food nexus is one of the most important and fundamental global environmental issues facing the world. For the purposes of this research project, we define human-environmental security as the joint optimization between human and environmental security as well as the water-energy-food nexus. To optimize the governance and management within these inter-connected needs, it is desirable to increase human-environmental security by improving social managements for the water-energy-food nexus. In this research project, we intend to establish a method to manage and optimize the human-environmental security of the water-energy-food nexus by using integrated models, indices, and maps as well as social and natural investigations with stakeholder analyses. We base our approach on the viewpoint that it is important for a sustainable society to increase human-environmental security with decreasing risk and increasing resilience by optimizing the connections within the critical water-energy and water-food clusters. We will take a regional perspective to address these global environmental problems. The geological and geomorphological conditions in our proposed study area are heavily influenced by the so-called 'Ring of Fire,' around the Pacific Ocean. Within these areas including Japan and Southeast Asia, the hydro-meteorological conditions are dominated by the Asia monsoon. The populations that live under these natural conditions face elevated risk and potential disaster as negative impacts, while also benefitting from positive ecological goods and services. There are therefore tradeoffs and conflicts within the water-energy-food nexus, as well as among various stakeholders in the region. The objective of this project is to maximize human-environmental security (minimize the risk) by choosing management structures and policies that optimize both the water-food-energy nexus in Asia-Pacific coastal regions. We define joint security approach as optimized policy. Optimal policies will develop joint security approaches for human-environmental security in the coastal region of the Ring of Fire, including stakeholders and decision-makers.

Expected frontiers: Incorporating weather uncertainty into a policy analysis using an integrated bi-level multi-objective optimization framework

EPA Science Inventory

Weather is the main driver in both plant use of nutrients and fate and transport of nutrients in the environment. In previous work, we evaluated a green tax for control of agricultural nutrients in a bi-level optimization framework that linked deterministic models. In this study,...

Creating Optimal Learning Environments through Invitational Education: An Alternative to Control Oriented School Reform

ERIC Educational Resources Information Center

Fretz, Joan R.

2015-01-01

Understanding what motivates people to put forth effort, persevere in the face of obstacles, and choose their behaviors is key to creating an optimal learning environment--the type of school that policy makers desire, but are unknowingly sabotaging (Dweck, 2000). Many motivation and self-concept theories provide important insight with regard to…

Evaluation of the Waste Tire Resources Recovery Program and Environmental Health Policy in Taiwan

PubMed Central

Chen, Chia-Ching; Yamada, Tetsuji; Chiu, I-Ming; Liu, Yi-Kuen

2009-01-01

This paper examines the effectiveness of Taiwanese environmental health policies, whose aim is to improve environmental quality by reducing tire waste via the Tire Resource Recovery Program. The results confirm that implemented environmental health policies improve the overall health of the population (i.e. a decrease in death caused by bronchitis and other respiratory diseases). Current policy expenditures are far below the optimal level, as it is estimated that a ten percent increase in the subsidy would decrease the number of deaths caused by bronchitis and other respiratory diseases by 0.58% per county/city per year on average. PMID:19440434

Three essays on multi-level optimization models and applications

NASA Astrophysics Data System (ADS)

Rahdar, Mohammad

The general form of a multi-level mathematical programming problem is a set of nested optimization problems, in which each level controls a series of decision variables independently. However, the value of decision variables may also impact the objective function of other levels. A two-level model is called a bilevel model and can be considered as a Stackelberg game with a leader and a follower. The leader anticipates the response of the follower and optimizes its objective function, and then the follower reacts to the leader's action. The multi-level decision-making model has many real-world applications such as government decisions, energy policies, market economy, network design, etc. However, there is a lack of capable algorithms to solve medium and large scale these types of problems. The dissertation is devoted to both theoretical research and applications of multi-level mathematical programming models, which consists of three parts, each in a paper format. The first part studies the renewable energy portfolio under two major renewable energy policies. The potential competition for biomass for the growth of the renewable energy portfolio in the United States and other interactions between two policies over the next twenty years are investigated. This problem mainly has two levels of decision makers: the government/policy makers and biofuel producers/electricity generators/farmers. We focus on the lower-level problem to predict the amount of capacity expansions, fuel production, and power generation. In the second part, we address uncertainty over demand and lead time in a multi-stage mathematical programming problem. We propose a two-stage tri-level optimization model in the concept of rolling horizon approach to reducing the dimensionality of the multi-stage problem. In the third part of the dissertation, we introduce a new branch and bound algorithm to solve bilevel linear programming problems. The total time is reduced by solving a smaller relaxation problem in each node and decreasing the number of iterations. Computational experiments show that the proposed algorithm is faster than the existing ones.

A qualitative exploration of the human resource policy implications of voluntary counselling and testing scale-up in Kenya: applying a model for policy analysis

PubMed Central

2011-01-01

Background Kenya experienced rapid scale up of HIV testing and counselling services in government health services from 2001. We set out to examine the human resource policy implications of scaling up HIV testing and counselling in Kenya and to analyse the resultant policy against a recognised theoretical framework of health policy reform (policy analysis triangle). Methods Qualitative methods were used to gain in-depth insights from policy makers who shaped scale up. This included 22 in-depth interviews with Voluntary Counselling and Testing (VCT) task force members, critical analysis of 53 sets of minutes and diary notes. We explore points of consensus and conflict amongst policymakers in Kenya and analyse this content to assess who favoured and resisted new policies, how scale up was achieved and the importance of the local context in which scale up occurred. Results The scale up of VCT in Kenya had a number of human resource policy implications resulting from the introduction of lay counsellors and their authorisation to conduct rapid HIV testing using newly introduced rapid testing technologies. Our findings indicate that three key groups of actors were critical: laboratory professionals, counselling associations and the Ministry of Health. Strategic alliances between donors, NGOs and these three key groups underpinned the process. The process of reaching consensus required compromise and time commitment but was critical to a unified nationwide approach. Policies around quality assurance were integral in ensuring standardisation of content and approach. Conclusion The introduction and scale up of new health service initiatives such as HIV voluntary counselling and testing necessitates changes to existing health systems and modification of entrenched interests around professional counselling and laboratory testing. Our methodological approach enabled exploration of complexities of scale up of HIV testing and counselling in Kenya. We argue that a better understanding of the diverse actors, the context and the process, is required to mitigate risks and maximise impact. PMID:22008721

Bilevel formulation of a policy design problem considering multiple objectives and incomplete preferences

NASA Astrophysics Data System (ADS)

Hawthorne, Bryant; Panchal, Jitesh H.

2014-07-01

A bilevel optimization formulation of policy design problems considering multiple objectives and incomplete preferences of the stakeholders is presented. The formulation is presented for Feed-in-Tariff (FIT) policy design for decentralized energy infrastructure. The upper-level problem is the policy designer's problem and the lower-level problem is a Nash equilibrium problem resulting from market interactions. The policy designer has two objectives: maximizing the quantity of energy generated and minimizing policy cost. The stakeholders decide on quantities while maximizing net present value and minimizing capital investment. The Nash equilibrium problem in the presence of incomplete preferences is formulated as a stochastic linear complementarity problem and solved using expected value formulation, expected residual minimization formulation, and the Monte Carlo technique. The primary contributions in this article are the mathematical formulation of the FIT policy, the extension of computational policy design problems to multiple objectives, and the consideration of incomplete preferences of stakeholders for policy design problems.

Competing Air Quality and Water Conservation Co-benefits from Power Sector Decarbonization

NASA Astrophysics Data System (ADS)

Peng, W.; Wagner, F.; Mauzerall, D. L.; Ramana, M. V.; Zhai, H.; Small, M.; Zhang, X.; Dalin, C.

2016-12-01

Decarbonizing the power sector can reduce fossil-based generation and associated air pollution and water use. However, power sector configurations that prioritize air quality benefits can be different from those that maximize water conservation benefits. Despite extensive work to optimize the generation mix under an air pollution or water constraint, little research has examined electricity transmission networks and the choice of which fossil fuel units to displace in order to achieve both environmental objectives simultaneously. When air pollution and water stress occur in different regions, the optimal transmission and displacement decisions still depend on priorities placed on air quality and water conservation benefits even if low-carbon generation planning is fixed. Here we use China as a test case, and develop a new optimization framework to study transmission and displacement decisions and the resulting air quality and water use impacts for six power sector decarbonization scenarios in 2030 ( 50% of national generation is low carbon). We fix low-carbon generation in each scenario (e.g. type, location, quantity) and vary technology choices and deployment patterns across scenarios. The objective is to minimize the total physical costs (transmission costs and coal power generation costs) and the estimated environmental costs. Environmental costs are estimated by multiplying effective air pollutant emissions (EMeff, emissions weighted by population density) and effective water use (Weff, water use weighted by a local water stress index) by their unit economic values, Vem and Vw. We are hence able to examine the effect of varying policy priorities by imposing different combinations of Vem and Vw. In all six scenarios, we find that increasing the priority on air quality co-benefits (higher Vem) reduces air pollution impacts (lower EMeff) at the expense of lower water conservation (higher Weff); and vice versa. Such results can largely be explained by differences in optimal transmission decisions due to different locations of air pollution and water stress in China (severe in the east and north respectively). To achieve both co-benefits simultaneously, it is therefore critical to coordinate policies that reduce air pollution (pollution tax) and water use (water pricing) with power sector planning.

Association of School District Policies for Radon Testing and Radon-Resistant New Construction Practices with Indoor Radon Zones.

PubMed

Foster, Stephanie; Everett Jones, Sherry

2016-12-13

Radon is a naturally occurring, colorless, odorless, and tasteless radioactive gas. Without testing, its presence is unknown. Using nationally representative data from the 2012 School Health Policies and Practices Study, we examined whether the prevalence of school district policies for radon testing and for radon-resistant new construction practices varied by district location in relation to the U.S. Environmental Protection Agency Map of Radon Zones. Among school districts located in counties with high predicted average indoor radon, 42.4% had policies for radon testing and 37.5% had policies for radon-resistant new construction practices. These findings suggest a critical need for improved awareness among policy makers regarding potential radon exposure for both students and school staff.

Association of School District Policies for Radon Testing and Radon-Resistant New Construction Practices with Indoor Radon Zones

PubMed Central

Foster, Stephanie; Everett Jones, Sherry

2016-01-01

Radon is a naturally occurring, colorless, odorless, and tasteless radioactive gas. Without testing, its presence is unknown. Using nationally representative data from the 2012 School Health Policies and Practices Study, we examined whether the prevalence of school district policies for radon testing and for radon-resistant new construction practices varied by district location in relation to the U.S. Environmental Protection Agency Map of Radon Zones. Among school districts located in counties with high predicted average indoor radon, 42.4% had policies for radon testing and 37.5% had policies for radon-resistant new construction practices. These findings suggest a critical need for improved awareness among policy makers regarding potential radon exposure for both students and school staff. PMID:27983613

A Salzburg Global Seminar: "Optimizing Talent: Closing Education and Social Mobility Gaps Worldwide." Policy Notes. Volume 20, Number 3, Fall 2012

ERIC Educational Resources Information Center

Schwartz, Robert

2012-01-01

This issue of ETS Policy Notes (Vol. 20, No. 3) provides highlights from the Salzburg Global Seminar in December 2011. The seminar focused on bettering the educational and life prospects of students up to age 18 worldwide. [This article was written with the assistance of Beth Brody.

Many-objective reservoir policy identification and refinement to reduce institutional myopia in water management

NASA Astrophysics Data System (ADS)

Giuliani, Matteo; Herman, Jonathan D.; Castelletti, Andrea; Reed, Patrick M.

2014-05-01

Current water reservoir operating policies are facing growing water demands as well as increasing uncertainties associated with a changing climate. However, policy inertia and myopia strongly limit the possibility of adapting current water reservoir operations to the undergoing change. Historical agreements and regulatory constraints limit the rate that reservoir operations are innovated and creates policy inertia, where water institutions are unlikely to change their current practices in absence of dramatic failures. Yet, no guarantee exists that historical management policies will not fail in coming years. In reference to policy myopia, although it has long been recognized that water reservoir systems are generally framed in heterogeneous socio-economic contexts involving a myriad of conflicting, non-commensurable operating objectives, the broader understanding of the multi-objective consequences of current operating rules as well as their vulnerability to hydroclimatic uncertainties is severely limited. This study proposes a decision analytic framework to overcome both policy inertia and myopia in complex river basin management contexts. The framework combines reservoir policy identification, many-objective optimization under uncertainty, and visual analytics to characterize current operations and discover key tradeoffs between alternative policies for balancing evolving demands and system uncertainties. The approach is demonstrated on the Conowingo Dam, located within the Lower Susquehanna River, USA. The Lower Susquehanna River is an interstate water body that has been subject to intensive water management efforts due to the system's competing demands from urban water supply, atomic power plant cooling, hydropower production, and federally regulated environmental flows. The proposed framework initially uses available streamflow observations to implicitly identify the current but unknown operating policy of Conowingo Dam. The quality of the identified baseline policy was validated by its ability to replicate historical release dynamics. Starting from this baseline policy, we then combine evolutionary many-objective optimization with visual analytics to discover new operating policies that better balance the tradeoffs within the Lower Susquehanna. Results confirm that the baseline operating policy, which only considers deterministic historical inflows, significantly overestimates the reliability of the reservoir's competing demands. The proposed framework removes this bias by successfully identifying alternative reservoir policies that are more robust to hydroclimatic uncertainties, while also better addressing the tradeoffs across the Conowingo Dam's multi-sector services.

Optimal climate change: economics and climate science policy histories (from heuristic to normative).

PubMed

Randalls, Samuel

2011-01-01

Historical accounts of climate change science and policy have reflected rather infrequently upon the debates, discussions, and policy advice proffered by economists in the 1980s. While there are many forms of economic analysis, this article focuses upon cost-benefit analysis, especially as adopted in the work of William Nordhaus. The article addresses the way in which climate change economics subtly altered debates about climate policy from the late 1970s through the 1990s. These debates are often technical and complex, but the argument in this article is that the development of a philosophy of climate change as an issue for cost-benefit analysis has had consequences for how climate policy is made today.

Monitoring health systems readiness and inpatient malaria case-management at Kenyan county hospitals.

PubMed

Zurovac, Dejan; Machini, Beatrice; Kiptui, Rebecca; Memusi, Dorothy; Amboko, Beatrice; Kigen, Samuel; Njiri, Patricia; Waqo, Ejersa

2018-05-29

Change of severe malaria treatment policy from quinine to artesunate, a major malaria control advance in Africa, is compromised by scarce data to monitor policy translation into practice. In Kenya, hospital surveys were implemented to monitor health systems readiness and inpatient malaria case-management. All 47 county referral hospitals were surveyed in February and October 2016. Data collection included hospital assessments, interviews with inpatient health workers and retrospective review of patients' admission files. Analysis included 185 and 182 health workers, and 1162 and 1224 patients admitted with suspected malaria, respectively, in all 47 hospitals. Cluster-adjusted comparisons of the performance indicators with exploratory stratifications were performed. Malaria microscopy was universal during both surveys. Artesunate availability increased (63.8-85.1%), while retrospective stock-outs declined (46.8-19.2%). No significant changes were observed in the coverage of artesunate trained (42.2% vs 40.7%) and supervised health workers (8.7% vs 12.8%). The knowledge about treatment policy improved (73.5-85.7%; p = 0.002) while correct artesunate dosing knowledge increased for patients < 20 kg (42.7-64.6%; p < 0.001) and > 20 kg (70.3-80.8%; p = 0.052). Most patients were tested on admission (88.6% vs 92.1%; p = 0.080) while repeated malaria testing was low (5.2% vs 8.1%; p = 0.034). Artesunate treatment for confirmed severe malaria patients significantly increased (69.9-78.7%; p = 0.030). No changes were observed in artemether-lumefantrine treatment for non-severe test positive patients (8.0% vs 8.8%; p = 0.796). Among test negative patients, increased adherence to test results was observed for non-severe (68.6-78.0%; p = 0.063) but not for severe patients (59.1-62.1%; p = 0.673). Overall quality of malaria case-management improved (48.6-56.3%; p = 0.004), both for children (54.1-61.5%; p = 0.019) and adults (43.0-51.0%; p = 0.041), and in both high (51.1-58.1%; p = 0.024) and low malaria risk areas (47.5-56.0%; p = 0.029). Most health systems and malaria case-management indicators improved during 2016. Gaps, often specific to different inpatient populations and risk areas, however remain and further programmatic interventions including close monitoring is needed to optimize policy translation.

Routine testing for blood-borne viruses in prisons: a systematic review

PubMed Central

Pevalin, David J.; O’Moore, Éamonn

2015-01-01

Background: People in prison have a higher burden of blood-borne virus (BBV) infection than the general population, and prisons present an opportunity to test for BBVs in high-risk, underserved groups. Changes to the BBV testing policies in English prisons have recently been piloted. This review will enable existing evidence to inform policy revisions. We describe components of routine HIV, hepatitis B and C virus testing policies in prisons and quantify testing acceptance, coverage, result notification and diagnosis. Methods: We searched five databases for studies of both opt-in (testing offered to all and the individual chooses to have the test or not) and opt-out (the individual is informed the test will be performed unless they actively refuse) prison BBV testing policies. Results: Forty-four studies published between 1989 and 2013 met the inclusion criteria. Of these, 82% were conducted in the USA, 91% included HIV testing and most tested at the time of incarceration. HIV testing acceptance rates ranged from 22 to 98% and testing coverage from 3 to 90%. Mixed results were found for equity in uptake. Six studies reported reasons for declining a test including recent testing and fear. Conclusions: While the quality of evidence is mixed, this review suggests that reasonable rates of uptake can be achieved with opt-in and, even better, with opt-out HIV testing policies. Little evidence was found relating to hepatitis testing. Policies need to specify exclusion criteria and consider consent processes, type of test and timing of the testing offer to balance acceptability, competence and availability of individuals. PMID:26219884

Pricing and inventory policies for Hi-tech products under replacement warranty

NASA Astrophysics Data System (ADS)

Tsao, Yu-Chung; Teng, Wei-Guang; Chen, Ruey-Shii; Chou, Wang-Ying

2014-06-01

Companies, especially in the Hi-tech (high-technology) industry (such as computer, communication and consumer electronic products), often provide a replacement warranty period for purchased items. In reality, simultaneously determining the price and inventory decisions under warranty policy is an important issue. The objective of this paper is to develop a joint pricing and inventory model for Hi-tech products under replacement warranty policy. In the first model, we consider a Hi-tech product feature in which the selling price is declining in a trend. We determine the optimal inventory level for each period and retail price for the first period while maximising the total profit. In the second model, we further determine the optimal retail price and inventory level for each period in the dynamic demand market. This study develops solution approaches to solve the problems described above. Numerical analysis discusses the influence of system parameters on the company's decisions and behaviours. The results of this study could serve as a reference for business managers or administrators.

Repayment policy for multiple loans

PubMed Central

2017-01-01

The Repayment Policy for Multiple Loans is about a given set of loans and a monthly incoming cash flow: what is the best way to allocate the monthly income to repay such loans? In this article, we close the almost 20-year-old open question about how to model the repayment policy for multiple loans problem together with its computational complexity. Thus, we propose a mixed integer linear programming model that establishes an optimal repayment schedule by minimizing the total amount of cash required to repay the loans. We prove that the most employed repayment strategies, such as the highest interest debt and the debt snowball methods, are not optimal. Experimental results on simulated cases based on real data show that our methodology obtains on average more than 4% of savings, that is, the debtor pays approximately 4% less to the bank or loaner, which is a considerable amount in finances. In certain cases, the debtor can save up to 40%. PMID:28430786

Optimal (R, Q) policy and pricing for two-echelon supply chain with lead time and retailer's service-level incomplete information

NASA Astrophysics Data System (ADS)

Esmaeili, M.; Naghavi, M. S.; Ghahghaei, A.

2018-03-01

Many studies focus on inventory systems to analyze different real-world situations. This paper considers a two-echelon supply chain that includes one warehouse and one retailer with stochastic demand and an up-to-level policy. The retailer's lead time includes the transportation time from the warehouse to the retailer that is unknown to the retailer. On the other hand, the warehouse is unaware of retailer's service level. The relationship between the retailer and the warehouse is modeled based on the Stackelberg game with incomplete information. Moreover, their relationship is presented when the warehouse and the retailer reveal their private information using the incentive strategies. The optimal inventory and pricing policies are obtained using an algorithm based on bi-level programming. Numerical examples, including sensitivity analysis of some key parameters, will compare the results between the Stackelberg models. The results show that information sharing is more beneficial to the warehouse rather than the retailer.

The climate impacts of bioenergy systems depend on market and regulatory policy contexts.

PubMed

Lemoine, Derek M; Plevin, Richard J; Cohn, Avery S; Jones, Andrew D; Brandt, Adam R; Vergara, Sintana E; Kammen, Daniel M

2010-10-01

Biomass can help reduce greenhouse gas (GHG) emissions by displacing petroleum in the transportation sector, by displacing fossil-based electricity, and by sequestering atmospheric carbon. Which use mitigates the most emissions depends on market and regulatory contexts outside the scope of attributional life cycle assessments. We show that bioelectricity's advantage over liquid biofuels depends on the GHG intensity of the electricity displaced. Bioelectricity that displaces coal-fired electricity could reduce GHG emissions, but bioelectricity that displaces wind electricity could increase GHG emissions. The electricity displaced depends upon existing infrastructure and policies affecting the electric grid. These findings demonstrate how model assumptions about whether the vehicle fleet and bioenergy use are fixed or free parameters constrain the policy questions an analysis can inform. Our bioenergy life cycle assessment can inform questions about a bioenergy mandate's optimal allocation between liquid fuels and electricity generation, but questions about the optimal level of bioenergy use require analyses with different assumptions about fixed and free parameters.

South African HIV self-testing policy and guidance considerations.

PubMed

Venter, Francois; Majam, Mohammed; Jankelowitz, Lauren; Adams, Siraaj; Moorhouse, Michelle; Carmona, Sergio; Stevens, Wendy; Msimanga, Busisiwe R; Allen, David; Balani, Pooja; Nevhutalu, Zwoitwaho; Rhagnath, Naleni; Shroufi, Amir; Devillé, Walter; Kazangarare, Victoria; van der Wiel, Renee; Templeman, Hugo; Puren, Adrian; Tucker, Tim; van Cutsem, Gilles; Conradie, Francesca; Dong, Krista; Chidarikire, Thato; Gray, Andy

2017-01-01

The gap in HIV testing remains significant and new modalities such as HIV self-testing (HIVST) have been recommended to reach key and under-tested populations. In December 2016, the World Health Organization (WHO) released the Guidelines on HIV Self-Testing and Partner Notification: A Supplement to the Consolidated Guidelines on HIV Testing Services (HTS) and urged member countries to develop HIVST policy and regulatory frameworks. In South Africa, HIVST was included as a supplementary strategy in the National HIV Testing Services Policy in 2016, and recently, guidelines for HIVST were included in the South African National Strategic Plan for HIV, sexually transmitted infections and tuberculosis 2017-2022. This document serves as an additional guidance for the National HIV Testing Services Policy 2016, with specific focus on HIVST. It is intended for policy advocates, clinical and non-clinical HTS providers, health facility managers and healthcare providers in private and public health facilities, non-governmental, community-based and faith-based organisations involved in HTS and outreach, device manufacturers, workplace programmes and institutes of higher education.

Optimal dynamic pricing for deteriorating items with reference-price effects

NASA Astrophysics Data System (ADS)

Xue, Musen; Tang, Wansheng; Zhang, Jianxiong

2016-07-01

In this paper, a dynamic pricing problem for deteriorating items with the consumers' reference-price effect is studied. An optimal control model is established to maximise the total profit, where the demand not only depends on the current price, but also is sensitive to the historical price. The continuous-time dynamic optimal pricing strategy with reference-price effect is obtained through solving the optimal control model on the basis of Pontryagin's maximum principle. In addition, numerical simulations and sensitivity analysis are carried out. Finally, some managerial suggestions that firm may adopt to formulate its pricing policy are proposed.

Stochastic optimization algorithms for barrier dividend strategies

NASA Astrophysics Data System (ADS)

Yin, G.; Song, Q. S.; Yang, H.

2009-01-01

This work focuses on finding optimal barrier policy for an insurance risk model when the dividends are paid to the share holders according to a barrier strategy. A new approach based on stochastic optimization methods is developed. Compared with the existing results in the literature, more general surplus processes are considered. Precise models of the surplus need not be known; only noise-corrupted observations of the dividends are used. Using barrier-type strategies, a class of stochastic optimization algorithms are developed. Convergence of the algorithm is analyzed; rate of convergence is also provided. Numerical results are reported to demonstrate the performance of the algorithm.

Financial planning as a policy tool in the petroleum industry (the case study: ojsc ”SURGUTNEFTEGAS”)

NASA Astrophysics Data System (ADS)

Romanyuk, Vera; Karyakina, Anna; Vershkova, Elena; Grinkevish, Larisa; Pozdeeva, Galina

2016-09-01

The article deals with the financial planning of oil and gas company activities including capital structure optimization. One of the main tasks of up-to-date financial management is to optimize the capital structure of an organization and minimize the weighted average cost of capital. The applied method in capital structure optimization affects the research quality results, as well as management decisions. The study was conducted on the basis of OJSC "Surgutneftegas" financial statements.

Thoroughfares, crossroads and cul-de-sacs: drug testing of welfare recipients.

PubMed

Wincup, Emma

2014-09-01

Over the past five years, proposals to introduce drug testing for welfare recipients have proliferated across the globe. In England, it was included in the Welfare Reform Act 2009 (yet never implemented) and in 2013, the New Zealand government introduced legislation which requires claimants to take pre-employment drug tests when requested by a prospective employer or training provider. Similarly, in over 20 US states there have been attempts to initiate drug testing of welfare recipients as a condition of eligibility for welfare, although frequently these controversial plans have either stalled or once introduced they have been halted through legal challenge. This article examines the process of introducing drug testing of welfare claimants in the UK as part of a broader strategy to address worklessness among problem drug users. Using Hudson and Lowe's (2004) multi-level analytic framework, which disputes 'top down' rational models of policy-making, it explores the mechanisms used for challenging drug testing policies. In so doing, it identifies the key policy actors involved, noting the alliances forged and strategies adopted to persuade the government to pursue alternative policies. Whilst the primary focus of the article is on the UK, consideration of the US and New Zealand facilitates comparison of the types of policy networks which emerge to oppose similar policies proposed in different socio-political contexts, and the forms of argument and/or evidence they inject into policy discussions. It is argued that a heavy reliance on rights-based arguments was a feature of opposing drug testing in the UK, US and New Zealand, and these featured more heavily than attempts to refute evidence underpinning these policies. However, there were important differences between jurisdictions in relation to the mechanisms used to challenge drug testing policies. These do not simply reflect the nature of the policies proposed but instead are reflective of different modes of governance, which influence the character of the policy networks formed and their judgements about the most effective ways of opposing what they regard as essentially flawed policies. Copyright © 2014 Elsevier B.V. All rights reserved.

Opportunities and Benefits for Increasing Transmission Capacity between the US Eastern and Western Interconnections

NASA Astrophysics Data System (ADS)

Figueroa-Acevedo, Armando L.

Historically, the primary justification for building wide-area transmission lines in the US and around the world has been based on reliability and economic criteria. Today, the influence of renewable portfolio standards (RPS), Environmental Protection Agency (EPA) regulations, transmission needs, load diversity, and grid flexibility requirements drives interest in high capacity wide-area transmission. By making use of an optimization model to perform long-term (15 years) co-optimized generation and transmission expansion planning, this work explored the benefits of increasing transmission capacity between the US Eastern and Western Interconnections under different policy and futures assumptions. The model assessed tradeoffs between investments in cross-interconnection HVDC transmission, AC transmission needs within each interconnection, generation investment costs, and operational costs, while satisfying different policy compliance constraints. Operational costs were broken down into the following market products: energy, up-/down regulation reserve, and contingency reserve. In addition, the system operating flexibility requirements were modeled as a function of net-load variability so that the flexibility of the non-wind/non-solar resources increases with increased wind and solar investment. In addition, planning reserve constraints are imposed under the condition that they be deliverable to the load. Thus, the model allows existing and candidate generation resources for both operating reserves and deliverable planning reserves to be shared throughout the interconnections, a feature which significantly drives identification of least-cost investments. This model is used with a 169-bus representation of the North American power grid to design four different high-capacity wide-area transmission infrastructures. Results from this analysis suggest that, under policy that imposes a high-renewable future, the benefits of high capacity transmission between the Eastern and Western Interconnections outweigh its cost. A sensitivity analysis is included to test the robustness of each design under different future assumptions and approximate upper and lower bounds for cross-seam transmission between the Eastern and Western Interconnections.

Diagnostic Assessment of the Difficulty Using Direct Policy Search in Many-Objective Reservoir Control

NASA Astrophysics Data System (ADS)

Zatarain-Salazar, J.; Reed, P. M.; Herman, J. D.; Giuliani, M.; Castelletti, A.

2014-12-01

Globally reservoir operations provide fundamental services to water supply, energy generation, recreation, and ecosystems. The pressures of expanding populations, climate change, and increased energy demands are motivating a significant investment in re-operationalizing existing reservoirs or defining operations for new reservoirs. Recent work has highlighted the potential benefits of exploiting recent advances in many-objective optimization and direct policy search (DPS) to aid in addressing these systems' multi-sector demand tradeoffs. This study contributes to a comprehensive diagnostic assessment of multi-objective evolutionary optimization algorithms (MOEAs) efficiency, effectiveness, reliability, and controllability when supporting DPS for the Conowingo dam in the Lower Susquehanna River Basin. The Lower Susquehanna River is an interstate water body that has been subject to intensive water management efforts due to the system's competing demands from urban water supply, atomic power plant cooling, hydropower production, and federally regulated environmental flows. Seven benchmark and state-of-the-art MOEAs are tested on deterministic and stochastic instances of the Susquehanna test case. In the deterministic formulation, the operating objectives are evaluated over the historical realization of the hydroclimatic variables (i.e., inflows and evaporation rates). In the stochastic formulation, the same objectives are instead evaluated over an ensemble of stochastic inflows and evaporation rates realizations. The algorithms are evaluated in their ability to support DPS in discovering reservoir operations that compose the tradeoffs for six multi-sector performance objectives with thirty-two decision variables. Our diagnostic results highlight that many-objective DPS is very challenging for modern MOEAs and that epsilon dominance is critical for attaining high levels of performance. Epsilon dominance algorithms epsilon-MOEA, epsilon-NSGAII and the auto adaptive Borg MOEA, are statistically superior for the six-objective Susquehanna instance of this important class of problems. Additionally, shifting from deterministic history-based DPS to stochastic DPS significantly increases the difficulty of the problem.

Education Policy-Making and Time

ERIC Educational Resources Information Center

Thompson, Greg; Cook, Ian

2014-01-01

This paper examines the global policy convergence toward high-stakes testing in schools and the use of test results to "steer at a distance", particularly as it applies to policy-makers' promise to improve teacher quality. Using Deleuze's three syntheses of time in the context of the Australian policy blueprint Quality Education, this…

The Differential Effects of an Opt-Out HIV Testing Policy for Pregnant Women in Ethiopia When Accounting for Stigma: Secondary Analysis of DHS Data.

PubMed

Kaufman, Michelle R; Mooney, Alyssa; Gebretsadik, Lakew Abebe; Sudhakar, Morankar N; Rieder, Rachel; Limaye, Rupali J; Girma, Eshetu; Rimal, Rajiv N

2017-02-01

Individual factors associated with HIV testing have been studied across multiple populations; however, testing is not just an individual-level phenomenon. This secondary analysis of 2005 and 2011 Ethiopia Demographic and Health Survey data was conducted to determine the extent to which the 2007 institution of an opt-out policy of HIV testing during antenatal care increased testing among women, and whether effects differed by women's stigmatizing beliefs about HIV. A logit model with interaction between pre-/post-policy year and policy exposure (birth in the past year) was used to estimate the increased probability of past-year testing, which may be attributable to the policy. Results suggested the policy contributed to a nine-point increase in the probability of testing (95% CI 0.06-0.13, p < 0.0001). A three-way interaction was used to compare the effects of exposure to the policy among women holding higher and lower HIV stigmatizing beliefs. The increase in the probability of past-year testing was 16 percentage points greater among women with lower stigmatizing beliefs (95% CI 0.06-0.27, p = 0.002). Women with higher stigmatizing beliefs were less likely to report attending antenatal care (ANC), testing at their last ANC visit, or being offered a test at their last ANC visit. We encourage researchers and practitioners to explore interventions that operate at multiple levels of socio-ecological spheres of influence, addressing both stigma and structural barriers to testing, in order to achieve the greatest results in preventing HIV.

STD testing policies and practices in U.S. city and county jails.

PubMed

Parece, M S; Herrera, G A; Voigt, R F; Middlekauff, S L; Irwin, K L

1999-09-01

Studies have shown that sexually transmitted disease (STD) rates are high in the incarcerated population. However, little is known about STD testing policies or practices in jails. To assess STD testing policies and practices in jails. The Division of STD Prevention developed and distributed an e-mail survey to 94 counties reporting more than 40 primary and secondary cases in 1996 or having cities with more than 200,000 persons. State and local STD program managers completed the assessment in collaboration with health departments and the main jail facilities in the selected counties. Most facilities (52-77%) had a policy for STD screening based only on symptoms or by arrestee request, and in these facilities, 0.2% to 6% of arrestees were tested. Facilities having a policy of offering routine testing tested only 3% to 45% of arrestees. Large facilities, facilities using public providers, and facilities routinely testing for syphilis using Stat RPR tested significantly more arrestees (P<0.05). Approximately half of the arrestees were released within 48 hours after intake, whereas 45% of facilities did not have STD testing results until after 48 hours. Most facilities had a policy for STD screening based only on symptoms or by arrestee request. Facilities having a policy of routine STD testing are not testing most of the arrestees. There is a small window (<48 hours) for STD testing and treatment before release. Smaller jails and facilities using private providers may need additional resources to increase STD testing levels. Correctional facilities should be considered an important setting for STD public health intervention where routine rapid STD screening and treatment on-site could be implemented.

INDEXABILITY AND OPTIMAL INDEX POLICIES FOR A CLASS OF REINITIALISING RESTLESS BANDITS.

PubMed

Villar, Sofía S

2016-01-01

Motivated by a class of Partially Observable Markov Decision Processes with application in surveillance systems in which a set of imperfectly observed state processes is to be inferred from a subset of available observations through a Bayesian approach, we formulate and analyze a special family of multi-armed restless bandit problems. We consider the problem of finding an optimal policy for observing the processes that maximizes the total expected net rewards over an infinite time horizon subject to the resource availability. From the Lagrangian relaxation of the original problem, an index policy can be derived, as long as the existence of the Whittle index is ensured. We demonstrate that such a class of reinitializing bandits in which the projects' state deteriorates while active and resets to its initial state when passive until its completion possesses the structural property of indexability and we further show how to compute the index in closed form. In general, the Whittle index rule for restless bandit problems does not achieve optimality. However, we show that the proposed Whittle index rule is optimal for the problem under study in the case of stochastically heterogenous arms under the expected total criterion, and it is further recovered by a simple tractable rule referred to as the 1-limited Round Robin rule. Moreover, we illustrate the significant suboptimality of other widely used heuristic: the Myopic index rule, by computing in closed form its suboptimality gap. We present numerical studies which illustrate for the more general instances the performance advantages of the Whittle index rule over other simple heuristics.

INDEXABILITY AND OPTIMAL INDEX POLICIES FOR A CLASS OF REINITIALISING RESTLESS BANDITS

PubMed Central

Villar, Sofía S.

2016-01-01

Motivated by a class of Partially Observable Markov Decision Processes with application in surveillance systems in which a set of imperfectly observed state processes is to be inferred from a subset of available observations through a Bayesian approach, we formulate and analyze a special family of multi-armed restless bandit problems. We consider the problem of finding an optimal policy for observing the processes that maximizes the total expected net rewards over an infinite time horizon subject to the resource availability. From the Lagrangian relaxation of the original problem, an index policy can be derived, as long as the existence of the Whittle index is ensured. We demonstrate that such a class of reinitializing bandits in which the projects’ state deteriorates while active and resets to its initial state when passive until its completion possesses the structural property of indexability and we further show how to compute the index in closed form. In general, the Whittle index rule for restless bandit problems does not achieve optimality. However, we show that the proposed Whittle index rule is optimal for the problem under study in the case of stochastically heterogenous arms under the expected total criterion, and it is further recovered by a simple tractable rule referred to as the 1-limited Round Robin rule. Moreover, we illustrate the significant suboptimality of other widely used heuristic: the Myopic index rule, by computing in closed form its suboptimality gap. We present numerical studies which illustrate for the more general instances the performance advantages of the Whittle index rule over other simple heuristics. PMID:27212781

Impact of Fishery Policy on Fishery Manufacture Output, Economy and Welfare in Indonesia

NASA Astrophysics Data System (ADS)

Firmansyah; Oktavilia, Shanty; Sugiyanto, F. X.; Hamzah, Ibnu N.

2018-02-01

The fisheries sector and fish manufacturing industry are the bright prospect sectors of Indonesia, due to its huge potency, which has not been worked out optimally. In facts, these sectors can generate a large amount of foreign exchange. The Government has paid significant attention to the development of these sectors. This study simulates the impact of fishery policies on the production of fish manufacturing industry, national economic and welfare in Indonesia. By employing the Input-Output Analysis approach, impacts of various government policy scenarios are developed, covering fisheries technical policy, as well as infrastructure development policies in the fisheries sector. This study indicates that the policies in the fisheries sector increase the output of fishery, the production of fish manufacturing industry, the sectoral and national outputs, as well as the level of national income.

A game-theoretical approach to multimedia social networks security.

PubMed

Liu, Enqiang; Liu, Zengliang; Shao, Fei; Zhang, Zhiyong

2014-01-01

The contents access and sharing in multimedia social networks (MSNs) mainly rely on access control models and mechanisms. Simple adoptions of security policies in the traditional access control model cannot effectively establish a trust relationship among parties. This paper proposed a novel two-party trust architecture (TPTA) to apply in a generic MSN scenario. According to the architecture, security policies are adopted through game-theoretic analyses and decisions. Based on formalized utilities of security policies and security rules, the choice of security policies in content access is described as a game between the content provider and the content requester. By the game method for the combination of security policies utility and its influences on each party's benefits, the Nash equilibrium is achieved, that is, an optimal and stable combination of security policies, to establish and enhance trust among stakeholders.

A Game-Theoretical Approach to Multimedia Social Networks Security

PubMed Central

Liu, Enqiang; Liu, Zengliang; Shao, Fei; Zhang, Zhiyong

2014-01-01

The contents access and sharing in multimedia social networks (MSNs) mainly rely on access control models and mechanisms. Simple adoptions of security policies in the traditional access control model cannot effectively establish a trust relationship among parties. This paper proposed a novel two-party trust architecture (TPTA) to apply in a generic MSN scenario. According to the architecture, security policies are adopted through game-theoretic analyses and decisions. Based on formalized utilities of security policies and security rules, the choice of security policies in content access is described as a game between the content provider and the content requester. By the game method for the combination of security policies utility and its influences on each party's benefits, the Nash equilibrium is achieved, that is, an optimal and stable combination of security policies, to establish and enhance trust among stakeholders. PMID:24977226

An Inventory Model for Special Display Goods with Seasonal Demand

NASA Astrophysics Data System (ADS)

Kawakatsu, Hidefumi

2010-10-01

The present study discusses the retailer's optimal replenishment policy for seasonal products. The demand rate of seasonal merchandise such as clothes, sporting goods, children's toys and electrical home appearances tends to decrease with time after reaching its maximum value. In this study, we focus on "Special Display Goods", which are heaped up in end displays or special areas at retail stores. They are sold at a fast velocity when their quantity displayed is large, but are sold at a low velocity if the quantity becomes small. We develop the model with a finite time horizon (selling period) to determine the optimal replenishment policy, which maximizes the retailer's total profit. Numerical examples are presented to illustrate the theoretical underpinnings of the proposed model.

Optimization of Location-Routing Problem for Cold Chain Logistics Considering Carbon Footprint.

PubMed

Wang, Songyi; Tao, Fengming; Shi, Yuhe

2018-01-06

In order to solve the optimization problem of logistics distribution system for fresh food, this paper provides a low-carbon and environmental protection point of view, based on the characteristics of perishable products, and combines with the overall optimization idea of cold chain logistics distribution network, where the green and low-carbon location-routing problem (LRP) model in cold chain logistics is developed with the minimum total costs as the objective function, which includes carbon emission costs. A hybrid genetic algorithm with heuristic rules is designed to solve the model, and an example is used to verify the effectiveness of the algorithm. Furthermore, the simulation results obtained by a practical numerical example show the applicability of the model while provide green and environmentally friendly location-distribution schemes for the cold chain logistics enterprise. Finally, carbon tax policies are introduced to analyze the impact of carbon tax on the total costs and carbon emissions, which proves that carbon tax policy can effectively reduce carbon dioxide emissions in cold chain logistics network.

An aging society: opportunity or challenge?

PubMed

Cutler, D M; Poterba, J M; Sheiner, L M; Summers, L H

1990-01-01

"This paper steps back from the current political debate [in the United States] over the social security trust fund and examines the more general question of how serious a macroeconomic problem aging is and how policy should respond to it. We focus primarily on issues relating to saving and capital accumulation. We do not consider the broader question of whether the current U.S. national saving rate is too high or too low, but focus on the effect of demographic changes on the optimal level of national saving. In addition, we consider the effects of demographic change on productivity growth and the optimal timing of tax collections. Our general conclusion is that demographic changes will improve American standards of living in the near future, but lower them slightly over the very long term. Other things being equal, the optimal policy response to recent and anticipated demographic changes is almost certainly a reduction rather than an increase in the national saving rate." excerpt

Stochastic optimization model for order acceptance with multiple demand classes and uncertain demand/supply

NASA Astrophysics Data System (ADS)

Yang, Wen; Fung, Richard Y. K.

2014-06-01

This article considers an order acceptance problem in a make-to-stock manufacturing system with multiple demand classes in a finite time horizon. Demands in different periods are random variables and are independent of one another, and replenishments of inventory deviate from the scheduled quantities. The objective of this work is to maximize the expected net profit over the planning horizon by deciding the fraction of the demand that is going to be fulfilled. This article presents a stochastic order acceptance optimization model and analyses the existence of the optimal promising policies. An example of a discrete problem is used to illustrate the policies by applying the dynamic programming method. In order to solve the continuous problems, a heuristic algorithm based on stochastic approximation (HASA) is developed. Finally, the computational results of a case example illustrate the effectiveness and efficiency of the HASA approach, and make the application of the proposed model readily acceptable.

Optimal lot sizing in screening processes with returnable defective items

NASA Astrophysics Data System (ADS)

Vishkaei, Behzad Maleki; Niaki, S. T. A.; Farhangi, Milad; Rashti, Mehdi Ebrahimnezhad Moghadam

2014-07-01

This paper is an extension of Hsu and Hsu (Int J Ind Eng Comput 3(5):939-948, 2012) aiming to determine the optimal order quantity of product batches that contain defective items with percentage nonconforming following a known probability density function. The orders are subject to 100 % screening process at a rate higher than the demand rate. Shortage is backordered, and defective items in each ordering cycle are stored in a warehouse to be returned to the supplier when a new order is received. Although the retailer does not sell defective items at a lower price and only trades perfect items (to avoid loss), a higher holding cost incurs to store defective items. Using the renewal-reward theorem, the optimal order and shortage quantities are determined. Some numerical examples are solved at the end to clarify the applicability of the proposed model and to compare the new policy to an existing one. The results show that the new policy provides better expected profit per time.

Incentive-compatible guaranteed renewable health insurance premiums.

PubMed

Herring, Bradley; Pauly, Mark V

2006-05-01

Theoretical models of guaranteed renewable insurance display front-loaded premium schedules. Such schedules both cover lifetime total claims of low-risk and high-risk individuals and provide an incentive for those who remain low-risk to continue to purchase the policy. Questions have been raised of whether actual individual insurance markets in the US approximate the behavior predicted by these models, both because young consumers may not be able to "afford" front-loading and because insurers may behave strategically in ways that erode the value of protection against risk reclassification. In this paper, the optimal competitive age-based premium schedule for a benchmark guaranteed renewable health insurance policy is estimated using medical expenditure data. Several factors are shown to reduce the amount of front-loading necessary. Indeed, the resulting optimal premium path increases with age. Actual premium paths exhibited by purchasers of individual insurance are close to the optimal renewable schedule we estimate. Finally, consumer utility associated with the feature is examined.

Optimal transfer, ordering and payment policies for joint supplier-buyer inventory model with price-sensitive trapezoidal demand and net credit

NASA Astrophysics Data System (ADS)

Shah, Nita H.; Shah, Digeshkumar B.; Patel, Dushyantkumar G.

2015-07-01

This study aims at formulating an integrated supplier-buyer inventory model when market demand is variable price-sensitive trapezoidal and the supplier offers a choice between discount in unit price and permissible delay period for settling the accounts due against the purchases made. This type of trade credit is termed as 'net credit'. In this policy, if the buyer pays within offered time M1, then the buyer is entitled for a cash discount; otherwise the full account must be settled by the time M2; where M2 > M1 ⩾ 0. The goal is to determine the optimal selling price, procurement quantity, number of transfers from the supplier to the buyer and payment time to maximise the joint profit per unit time. An algorithm is worked out to obtain the optimal solution. A numerical example is given to validate the proposed model. The managerial insights based on sensitivity analysis are deduced.

The Regulation of a Spatially Heterogeneous Externality: Tradable Groundwater Permits to Protect Streams

NASA Astrophysics Data System (ADS)

Kuwayama, Y.; Brozovic, N.

2012-12-01

Groundwater pumping from aquifers can reduce the flow of surface water in nearby streams through a process known as stream depletion. In the United States, recent awareness of this externality has led to intra- and inter-state conflict and rapidly-changing water management policies and institutions. A factor that complicates the design of groundwater management policies to protect streams is the spatial heterogeneity of the stream depletion externality; the marginal damage of groundwater use on stream flows depends crucially on the location of pumping relative to streams. Under these circumstances, economic theory predicts that spatially differentiated policies can achieve an aggregate reduction in stream depletion cost effectively. However, whether spatially differentiated policies offer significant abatement cost savings and environmental improvements over simpler, alternative policies is an empirical question. In this paper, we analyze whether adopting a spatially differentiated groundwater permit system can lead to significant savings in compliance costs while meeting targets on stream protection. Using a population data set of active groundwater wells in the Nebraska portion of the Republican River Basin, we implement an optimization model of each well owner's crop choice, land use, and irrigation decisions to determine the distribution of regulatory costs. We model the externality of pumping on streams by employing an analytical solution from the hydrology literature that determines reductions in stream flow caused by groundwater pumping over space and time. The economic and hydrologic model components are then combined into one optimization framework, which allows us to measure farmer abatement costs and stream flow benefits under a constrained optimal market that features spatially differentiated, tradable groundwater permits. We compare this outcome to the efficiency of alternative second-best policies, including spatially uniform permit markets and pumping restrictions based on geographic zones. Our analysis considers static policies for which abatement is fixed over time, as well as dynamic policies that allow abatement to vary over time and future compliance costs to be subject to a discount rate. We find that if current levels of stream flow in the Republican River Basin are held fixed, regulators can generate most of the potential abatement cost savings by establishing a one-to-one tradable permit system that does not account for spatial heterogeneity. We obtain this surprising result because the agronomic and climatic parameters in our data set that determine farmer abatement costs are spatially correlated with hydrologic parameters that determine the marginal damage of groundwater use on streams. However, we also find that if future legal or ecological circumstances require regulators to increase significantly the protection of streams from current levels, spatially differentiated policies will generate sizable cost savings compared to policies that ignore spatial heterogeneity.

How to design the cost-effectiveness appraisal process of new healthcare technologies to maximise population health: A conceptual framework.

PubMed

Johannesen, Kasper M; Claxton, Karl; Sculpher, Mark J; Wailoo, Allan J

2018-02-01

This paper presents a conceptual framework to analyse the design of the cost-effectiveness appraisal process of new healthcare technologies. The framework characterises the appraisal processes as a diagnostic test aimed at identifying cost-effective (true positive) and non-cost-effective (true negative) technologies. Using the framework, factors that influence the value of operating an appraisal process, in terms of net gain to population health, are identified. The framework is used to gain insight into current policy questions including (a) how rigorous the process should be, (b) who should have the burden of proof, and (c) how optimal design changes when allowing for appeals, price reductions, resubmissions, and re-evaluations. The paper demonstrates that there is no one optimal appraisal process and the process should be adapted over time and to the specific technology under assessment. Optimal design depends on country-specific features of (future) technologies, for example, effect, price, and size of the patient population, which might explain the difference in appraisal processes across countries. It is shown that burden of proof should be placed on the producers and that the impact of price reductions and patient access schemes on the producer's price setting should be considered when designing the appraisal process. Copyright © 2017 John Wiley & Sons, Ltd.

Pricing policy for declining demand using item preservation technology.

PubMed

Khedlekar, Uttam Kumar; Shukla, Diwakar; Namdeo, Anubhav

2016-01-01

We have designed an inventory model for seasonal products in which deterioration can be controlled by item preservation technology investment. Demand for the product is considered price sensitive and decreases linearly. This study has shown that the profit is a concave function of optimal selling price, replenishment time and preservation cost parameter. We simultaneously determined the optimal selling price of the product, the replenishment cycle and the cost of item preservation technology. Additionally, this study has shown that there exists an optimal selling price and optimal preservation investment to maximize the profit for every business set-up. Finally, the model is illustrated by numerical examples and sensitive analysis of the optimal solution with respect to major parameters.

Using Optimization to Improve Test Planning

DTIC Science & Technology

2017-09-01

friendly and to display the output differently, the test and evaluation test schedule optimization model would be a good tool for the test and... evaluation schedulers. 14. SUBJECT TERMS schedule optimization, test planning 15. NUMBER OF PAGES 223 16. PRICE CODE 17. SECURITY CLASSIFICATION OF...make the input more user-friendly and to display the output differently, the test and evaluation test schedule optimization model would be a good tool

Optimal Bayesian Adaptive Design for Test-Item Calibration.

PubMed

van der Linden, Wim J; Ren, Hao

2015-06-01

An optimal adaptive design for test-item calibration based on Bayesian optimality criteria is presented. The design adapts the choice of field-test items to the examinees taking an operational adaptive test using both the information in the posterior distributions of their ability parameters and the current posterior distributions of the field-test parameters. Different criteria of optimality based on the two types of posterior distributions are possible. The design can be implemented using an MCMC scheme with alternating stages of sampling from the posterior distributions of the test takers' ability parameters and the parameters of the field-test items while reusing samples from earlier posterior distributions of the other parameters. Results from a simulation study demonstrated the feasibility of the proposed MCMC implementation for operational item calibration. A comparison of performances for different optimality criteria showed faster calibration of substantial numbers of items for the criterion of D-optimality relative to A-optimality, a special case of c-optimality, and random assignment of items to the test takers.

Validity Theory: Reform Policies, Accountability Testing, and Consequences

ERIC Educational Resources Information Center

Chalhoub-Deville, Micheline

2016-01-01

Educational policies such as Race to the Top in the USA affirm a central role for testing systems in government-driven reform efforts. Such reform policies are often referred to as the global education reform movement (GERM). Changes observed with the GERM style of testing demand socially engaged validity theories that include consequential…

Money Changes Everything: Funding Shocks and Optimal Admissions and Financial Aid Policies in Higher Education

ERIC Educational Resources Information Center

Nagler, Matthew G.

2006-01-01

The paper examines the effect of a shock to university funding on tuition net of financial aid, admissions selectivity, and enrollment levels chosen by an optimizing university. Whereas a positive shock, such as a major donation, results in lower net tuition and greater selectivity with respect to all students, its effect on enrollment may not be…

Distributed Optimal Consensus Control for Multiagent Systems With Input Delay.

PubMed

Zhang, Huaipin; Yue, Dong; Zhao, Wei; Hu, Songlin; Dou, Chunxia; Huaipin Zhang; Dong Yue; Wei Zhao; Songlin Hu; Chunxia Dou; Hu, Songlin; Zhang, Huaipin; Dou, Chunxia; Yue, Dong; Zhao, Wei

2018-06-01

This paper addresses the problem of distributed optimal consensus control for a continuous-time heterogeneous linear multiagent system subject to time varying input delays. First, by discretization and model transformation, the continuous-time input-delayed system is converted into a discrete-time delay-free system. Two delicate performance index functions are defined for these two systems. It is shown that the performance index functions are equivalent and the optimal consensus control problem of the input-delayed system can be cast into that of the delay-free system. Second, by virtue of the Hamilton-Jacobi-Bellman (HJB) equations, an optimal control policy for each agent is designed based on the delay-free system and a novel value iteration algorithm is proposed to learn the solutions to the HJB equations online. The proposed adaptive dynamic programming algorithm is implemented on the basis of a critic-action neural network (NN) structure. Third, it is proved that local consensus errors of the two systems and weight estimation errors of the critic-action NNs are uniformly ultimately bounded while the approximated control policies converge to their target values. Finally, two simulation examples are presented to illustrate the effectiveness of the developed method.

Near Optimal Event-Triggered Control of Nonlinear Discrete-Time Systems Using Neurodynamic Programming.

PubMed

Sahoo, Avimanyu; Xu, Hao; Jagannathan, Sarangapani

2016-09-01

This paper presents an event-triggered near optimal control of uncertain nonlinear discrete-time systems. Event-driven neurodynamic programming (NDP) is utilized to design the control policy. A neural network (NN)-based identifier, with event-based state and input vectors, is utilized to learn the system dynamics. An actor-critic framework is used to learn the cost function and the optimal control input. The NN weights of the identifier, the critic, and the actor NNs are tuned aperiodically once every triggered instant. An adaptive event-trigger condition to decide the trigger instants is derived. Thus, a suitable number of events are generated to ensure a desired accuracy of approximation. A near optimal performance is achieved without using value and/or policy iterations. A detailed analysis of nontrivial inter-event times with an explicit formula to show the reduction in computation is also derived. The Lyapunov technique is used in conjunction with the event-trigger condition to guarantee the ultimate boundedness of the closed-loop system. The simulation results are included to verify the performance of the controller. The net result is the development of event-driven NDP.

Antibiotic Policies and Utilization in Oregon Hospice Programs.

PubMed

Novak, Rachel L; Noble, Brie N; Fromme, Erik K; Tice, Michael O; McGregor, Jessina C; Furuno, Jon P

2016-09-01

Antibiotics are frequently used in hospice care, despite limited data on safety and effectiveness in this patient population. We surveyed Oregon hospice programs on antibiotic policies and prescribing practices. Among 39 responding hospice programs, the median reported proportion of current census using antibiotics was 10% (interquartile range = 3.5%-20.0%). Approximately 31% of responding hospice programs had policies for antibiotic initiation, 17% of hospice programs had policies for antibiotic discontinuation, and 95% of hospice programs had policies for managing drug interactions. Diarrhea, nausea/vomiting, and yeast infections were the most frequently reported antibiotic-associated adverse events, occurring "sometimes" or "often" among 62%, 47%, and 62% of respondents, respectively. In conclusion, less than a third of participating hospice programs reported having a policy for antibiotic initiation and even less frequently a policy for discontinuation. More data are needed on the risks and benefits of antibiotic use in hospice care to inform these policies and optimize outcomes in this vulnerable patient population. © The Author(s) 2015.

Quality standards versus nutritional taxes: Health and welfare impacts with strategic firms.

PubMed

Réquillart, Vincent; Soler, Louis-Georges; Zang, Yu

2016-12-01

The goal of this paper is to better understand firms' strategic reactions to nutritional policies targeting food quality improvements and to derive optimal policies. We propose a model of product differentiation, taking into account the taste and health characteristics of products. We study how two firms react to alternative policies: an MQS policy, linear taxation of the two goods on the market, and taxation of the low-quality good. The MQS and the taxation of the low-quality product are the preferred options by a social planner. If taste is moderately important, the MQS policy is chosen by a populist and a paternalist social planner. If taste is a major component of choice, the populist planner chooses to tax the low-quality product whereas the paternalist planner prefers the MQS policy. Finally, for a paternalist social planner, an MQS-based policy always allows for higher levels of welfare than an information policy alone. Copyright © 2016 Elsevier B.V. All rights reserved.

Evaluation of linearly solvable Markov decision process with dynamic model learning in a mobile robot navigation task.

PubMed

Kinjo, Ken; Uchibe, Eiji; Doya, Kenji

2013-01-01

Linearly solvable Markov Decision Process (LMDP) is a class of optimal control problem in which the Bellman's equation can be converted into a linear equation by an exponential transformation of the state value function (Todorov, 2009b). In an LMDP, the optimal value function and the corresponding control policy are obtained by solving an eigenvalue problem in a discrete state space or an eigenfunction problem in a continuous state using the knowledge of the system dynamics and the action, state, and terminal cost functions. In this study, we evaluate the effectiveness of the LMDP framework in real robot control, in which the dynamics of the body and the environment have to be learned from experience. We first perform a simulation study of a pole swing-up task to evaluate the effect of the accuracy of the learned dynamics model on the derived the action policy. The result shows that a crude linear approximation of the non-linear dynamics can still allow solution of the task, despite with a higher total cost. We then perform real robot experiments of a battery-catching task using our Spring Dog mobile robot platform. The state is given by the position and the size of a battery in its camera view and two neck joint angles. The action is the velocities of two wheels, while the neck joints were controlled by a visual servo controller. We test linear and bilinear dynamic models in tasks with quadratic and Guassian state cost functions. In the quadratic cost task, the LMDP controller derived from a learned linear dynamics model performed equivalently with the optimal linear quadratic regulator (LQR). In the non-quadratic task, the LMDP controller with a linear dynamics model showed the best performance. The results demonstrate the usefulness of the LMDP framework in real robot control even when simple linear models are used for dynamics learning.

ON CONTINUOUS-REVIEW (S-1,S) INVENTORY POLICIES WITH STATE-DEPENDENT LEADTIMES,

DTIC Science & Technology

INVENTORY CONTROL, *REPLACEMENT THEORY), MATHEMATICAL MODELS, LEAD TIME , MANAGEMENT ENGINEERING, DISTRIBUTION FUNCTIONS, PROBABILITY, QUEUEING THEORY, COSTS, OPTIMIZATION, STATISTICAL PROCESSES, DIFFERENCE EQUATIONS

WITHDRAWN: The Optimal Treatment Options of Septated Chronic Subdural Hematoma: A Retrospective Comparison of Craniotomy Versus Endoscopic-Assisted Burr-Hole Craniostomy.

PubMed

Zhang, Jibo; Fan, Xingyue; Liu, Xuemeng; Chen, Jincao; Wang, Wei; Fu, Kai

2017-11-11

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal. Copyright © 2017 Elsevier Inc. All rights reserved.

Essays on environmental regulations in electricity markets

NASA Astrophysics Data System (ADS)

Sun, Yanming

Reducing the Greenhouse Gas pollution and promoting energy efficiency among consumers' energy use have been major public policy issues recently. Currently, both the United States and the European Union have set up explicit percentage requirements that require energy generators or consumers to undertake a certain percentage of their energy production or consumption from renewable sources. To achieve their renewable targets, the Tradable Green Certificates (TGC) system has been introduced in their electricity markets. Moreover, in order to promote energy conservation and achieve energy efficiency targets, price policies and price changes derived from environmental regulations have played a more important role in reducing electricity consumption. My research studies problems associated with these policy implementations. In Chapter 1, I analyze a competitive electricity market with two countries operated under a common TGC system. By using geometric illustrations, I compare the two countries' welfare when the renewable quota is chosen optimally under the common certificate market with three different situations. The policy recommendation is that when the value of damage parameter is sufficiently small, full integration with a TGC market is welfare superior to full integration of an all fossil-fuel based market with an optimal emissions standard. In Chapter 2, by analyzing a stylized theoretical model and numerical examples, I investigate the performance of the optimal renewables policy under full separation and full integration scenarios for two countries' electricity markets operated under TGC systems. In my third chapter, I look at residential electricity consumption responsiveness to increases of electricity price in the U.S. and the different effect of a price increase on electricity use for states of different income levels. My analysis reveals that raising the energy price in the short run will not give consumers much incentive to adjust their appliances and make energy conservation investments to reduce electricity use, while in the long run, consumers are more likely to lower their electricity consumption, facing the higher electricity price induced from regulation policies. In addition, for states of higher per capita GDP, raising the electricity price may be more effective to ensure a cut in electricity consumption.

Balancing hydropower production and river bed incision in operating a run-of-river hydropower scheme along the River Po

NASA Astrophysics Data System (ADS)

Denaro, Simona; Dinh, Quang; Bizzi, Simone; Bernardi, Dario; Pavan, Sara; Castelletti, Andrea; Schippa, Leonardo; Soncini-Sessa, Rodolfo

2013-04-01

Water management through dams and reservoirs is worldwide necessary to support key human-related activities ranging from hydropower production to water allocation, and flood risk mitigation. Reservoir operations are commonly planned in order to maximize these objectives. However reservoirs strongly influence river geomorphic processes causing sediment deficit downstream, altering the flow regime, leading, often, to process of river bed incision: for instance the variations of river cross sections over few years can notably affect hydropower production, flood mitigation, water supply strategies and eco-hydrological processes of the freshwater ecosystem. The river Po (a major Italian river) has experienced severe bed incision in the last decades. For this reason infrastructure stability has been negatively affected, and capacity to derive water decreased, navigation, fishing and tourism are suffering economic damages, not to mention the impact on the environment. Our case study analyzes the management of Isola Serafini hydropower plant located on the main Po river course. The plant has a major impact to the geomorphic river processes downstream, affecting sediment supply, connectivity (stopping sediment upstream the dam) and transport capacity (altering the flow regime). Current operation policy aims at maximizing hydropower production neglecting the effects in term of geomorphic processes. A new improved policy should also consider controlling downstream river bed incision. The aim of this research is to find suitable modeling framework to identify an operating policy for Isola Serafini reservoir able to provide an optimal trade-off between these two conflicting objectives: hydropower production and river bed incision downstream. A multi-objective simulation-based optimization framework is adopted. The operating policy is parameterized as a piecewise linear function and the parameters optimized using an interactive response surface approach. Global and local response surface are comparatively assessed. Preliminary results show that a range of potentially interesting trade-off policies exist able to better control river bed incision downstream without significantly decreasing hydropower production.

Scenario-based fitted Q-iteration for adaptive control of water reservoir systems under uncertainty

NASA Astrophysics Data System (ADS)

Bertoni, Federica; Giuliani, Matteo; Castelletti, Andrea

2017-04-01

Over recent years, mathematical models have largely been used to support planning and management of water resources systems. Yet, the increasing uncertainties in their inputs - due to increased variability in the hydrological regimes - are a major challenge to the optimal operations of these systems. Such uncertainty, boosted by projected changing climate, violates the stationarity principle generally used for describing hydro-meteorological processes, which assumes time persisting statistical characteristics of a given variable as inferred by historical data. As this principle is unlikely to be valid in the future, the probability density function used for modeling stochastic disturbances (e.g., inflows) becomes an additional uncertain parameter of the problem, which can be described in a deterministic and set-membership based fashion. This study contributes a novel method for designing optimal, adaptive policies for controlling water reservoir systems under climate-related uncertainty. The proposed method, called scenario-based Fitted Q-Iteration (sFQI), extends the original Fitted Q-Iteration algorithm by enlarging the state space to include the space of the uncertain system's parameters (i.e., the uncertain climate scenarios). As a result, sFQI embeds the set-membership uncertainty of the future inflow scenarios in the action-value function and is able to approximate, with a single learning process, the optimal control policy associated to any scenario included in the uncertainty set. The method is demonstrated on a synthetic water system, consisting of a regulated lake operated for ensuring reliable water supply to downstream users. Numerical results show that the sFQI algorithm successfully identifies adaptive solutions to operate the system under different inflow scenarios, which outperform the control policy designed under historical conditions. Moreover, the sFQI policy generalizes over inflow scenarios not directly experienced during the policy design, thus alleviating the risk of mis-adaptation, namely the design of a solution fully adapted to a scenario that is different from the one that will actually realize.

Utilisation of eye-care services: the effect of Scotland's free eye examination policy.

PubMed

Dickey, Heather; Ikenwilo, Divine; Norwood, Patricia; Watson, Verity; Zangelidis, Alexandros

2012-12-01

To examine how the introduction of free eye examinations in Scotland affected people's use of eye care services. Particularly, to assess if more people are now having their eyes examined regularly, and whether there are differences in the way people responded to the policy across socio-economic groups. Using the British Household Panel Survey, eye test uptake and frequency in Scotland is compared to the rest of the UK pre and post policy. Propensity to have eye tests and responsiveness to the policy is compared across socio-economic groups. In addition, using data available from a chain of private ophthalmic opticians, clinical characteristics of eye examination patients are compared pre- and post-policy. There is evidence that suggests that people responded positively to the policy. In particular, a higher percentage of people in Scotland have their eyes tested after the free eye care policy was introduced. Interestingly, the response to the policy varies between the different socio-economic groups. For the highest earners and most educated groups, the proportion of people having an eye test increased more than for those groups with lower income or lower education. Although the policy succeeded in getting more people to have their eyes tested, the socio-economic differences observed suggest that the policy has not reached the more vulnerable segments in society to the same extent, in particular, those with low education and low income. As a result, eye care services utilisation inequalities have widened in Scotland after the free eye care policy was introduced. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

An image based method for crop yield prediction using remotely sensed and crop canopy data: the case of Paphos district, western Cyprus

NASA Astrophysics Data System (ADS)

Papadavid, G.; Hadjimitsis, D.

2014-08-01

Remote sensing techniques development have provided the opportunity for optimizing yields in the agricultural procedure and moreover to predict the forthcoming yield. Yield prediction plays a vital role in Agricultural Policy and provides useful data to policy makers. In this context, crop and soil parameters along with NDVI index which are valuable sources of information have been elaborated statistically to test if a) Durum wheat yield can be predicted and b) when is the actual time-window to predict the yield in the district of Paphos, where Durum wheat is the basic cultivation and supports the rural economy of the area. 15 plots cultivated with Durum wheat from the Agricultural Research Institute of Cyprus for research purposes, in the area of interest, have been under observation for three years to derive the necessary data. Statistical and remote sensing techniques were then applied to derive and map a model that can predict yield of Durum wheat in this area. Indeed the semi-empirical model developed for this purpose, with very high correlation coefficient R2=0.886, has shown in practice that can predict yields very good. Students T test has revealed that predicted values and real values of yield have no statistically significant difference. The developed model can and will be further elaborated with more parameters and applied for other crops in the near future.

Cost-effectiveness of MODY genetic testing: translating genomic advances into practical health applications.

PubMed

Naylor, Rochelle N; John, Priya M; Winn, Aaron N; Carmody, David; Greeley, Siri Atma W; Philipson, Louis H; Bell, Graeme I; Huang, Elbert S

2014-01-01

OBJECTIVE To evaluate the cost-effectiveness of a genetic testing policy for HNF1A-, HNF4A-, and GCK-MODY in a hypothetical cohort of type 2 diabetic patients 25-40 years old with a MODY prevalence of 2%. RESEARCH DESIGN AND METHODS We used a simulation model of type 2 diabetes complications based on UK Prospective Diabetes Study data, modified to account for the natural history of disease by genetic subtype to compare a policy of genetic testing at diabetes diagnosis versus a policy of no testing. Under the screening policy, successful sulfonylurea treatment of HNF1A-MODY and HNF4A-MODY was modeled to produce a glycosylated hemoglobin reduction of -1.5% compared with usual care. GCK-MODY received no therapy. Main outcome measures were costs and quality-adjusted life years (QALYs) based on lifetime risk of complications and treatments, expressed as the incremental cost-effectiveness ratio (ICER) (USD/QALY). RESULTS The testing policy yielded an average gain of 0.012 QALYs and resulted in an ICER of 205,000 USD. Sensitivity analysis showed that if the MODY prevalence was 6%, the ICER would be ~50,000 USD. If MODY prevalence was >30%, the testing policy was cost saving. Reducing genetic testing costs to 700 USD also resulted in an ICER of ~50,000 USD. CONCLUSIONS Our simulated model suggests that a policy of testing for MODY in selected populations is cost-effective for the U.S. based on contemporary ICER thresholds. Higher prevalence of MODY in the tested population or decreased testing costs would enhance cost-effectiveness. Our results make a compelling argument for routine coverage of genetic testing in patients with high clinical suspicion of MODY.

Model-Based Comprehensive Analysis of School Closure Policies for Mitigating Influenza Epidemics and Pandemics

PubMed Central

Fumanelli, Laura; Ajelli, Marco; Merler, Stefano; Ferguson, Neil M.; Cauchemez, Simon

2016-01-01

School closure policies are among the non-pharmaceutical measures taken into consideration to mitigate influenza epidemics and pandemics spread. However, a systematic review of the effectiveness of alternative closure policies has yet to emerge. Here we perform a model-based analysis of four types of school closure, ranging from the nationwide closure of all schools at the same time to reactive gradual closure, starting from class-by-class, then grades and finally the whole school. We consider policies based on triggers that are feasible to monitor, such as school absenteeism and national ILI surveillance system. We found that, under specific constraints on the average number of weeks lost per student, reactive school-by-school, gradual, and county-wide closure give comparable outcomes in terms of optimal infection attack rate reduction, peak incidence reduction or peak delay. Optimal implementations generally require short closures of one week each; this duration is long enough to break the transmission chain without leading to unnecessarily long periods of class interruption. Moreover, we found that gradual and county closures may be slightly more easily applicable in practice as they are less sensitive to the value of the excess absenteeism threshold triggering the start of the intervention. These findings suggest that policy makers could consider school closure policies more diffusely as response strategy to influenza epidemics and pandemics, and the fact that some countries already have some experience of gradual or regional closures for seasonal influenza outbreaks demonstrates that logistic and feasibility challenges of school closure strategies can be to some extent overcome. PMID:26796333

Model-Based Comprehensive Analysis of School Closure Policies for Mitigating Influenza Epidemics and Pandemics.

PubMed

Fumanelli, Laura; Ajelli, Marco; Merler, Stefano; Ferguson, Neil M; Cauchemez, Simon

2016-01-01

School closure policies are among the non-pharmaceutical measures taken into consideration to mitigate influenza epidemics and pandemics spread. However, a systematic review of the effectiveness of alternative closure policies has yet to emerge. Here we perform a model-based analysis of four types of school closure, ranging from the nationwide closure of all schools at the same time to reactive gradual closure, starting from class-by-class, then grades and finally the whole school. We consider policies based on triggers that are feasible to monitor, such as school absenteeism and national ILI surveillance system. We found that, under specific constraints on the average number of weeks lost per student, reactive school-by-school, gradual, and county-wide closure give comparable outcomes in terms of optimal infection attack rate reduction, peak incidence reduction or peak delay. Optimal implementations generally require short closures of one week each; this duration is long enough to break the transmission chain without leading to unnecessarily long periods of class interruption. Moreover, we found that gradual and county closures may be slightly more easily applicable in practice as they are less sensitive to the value of the excess absenteeism threshold triggering the start of the intervention. These findings suggest that policy makers could consider school closure policies more diffusely as response strategy to influenza epidemics and pandemics, and the fact that some countries already have some experience of gradual or regional closures for seasonal influenza outbreaks demonstrates that logistic and feasibility challenges of school closure strategies can be to some extent overcome.

Many-Objective Reservoir Policy Identification and Refinement to Reduce Institutional Myopia in Water Management

NASA Astrophysics Data System (ADS)

Giuliani, M.; Herman, J. D.; Castelletti, A.; Reed, P. M.

2013-12-01

Institutional inertia strongly limits our ability to adapt water reservoir operations to better manage growing water demands as well as their associated uncertainties in a changing climate. Although it has long been recognized that these systems are generally framed in heterogeneous socio-economic contexts involving a myriad of conflicting, non-commensurable operating objectives, our broader understanding of the multiobjective consequences of current operating rules as well as their vulnerability to hydroclimatic uncertainties is severely limited. This study proposes a decision analytic framework to overcome policy inertia and myopia in complex river basin management contexts. The framework combines reservoir policy identification and many-objective optimization under uncertainty to characterize current operations and discover key tradeoffs between alternative policies for balancing evolving demands and system uncertainties. The approach is demonstrated on the Conowingo Dam, located within the Lower Susquehanna River, USA. The Lower Susquehanna River is an interstate water body that has been subject to intensive water management efforts due to the system's competing demands from urban water supply, atomic power plant cooling, hydropower production, and federally regulated environmental flows. Initially our proposed framework uses available streamflow observations to implicitly identify the Conowingo Dam's current but unknown operating policy. This baseline policy is identified by fitting radial basis functions to existing system dynamics. Our assumption in the baseline policy is that the dam operator is represented as a rational agent seeking to maximize primary operational objectives (i.e., guaranteeing the public water supply and maximizing the hydropower revenue). The quality of the identified baseline policy is evaluated by its ability to replicate historical release dynamics. Once identified, the historical baseline policy then provides a means of representing the decision preferences guiding current operations. Our results show that the estimated policy closely captures the dynamics of current releases and flows for the Lower Susquehanna. After identifying the historical baseline policy, our proposed decision analytic framework then combines evolutionary many-objective optimization with visual analytics to discover improved operating policies. Our Lower Susquehanna results confirm that the system's current history-based operations are negatively biased to overestimate the reliability of the reservoir's multi-sector services. Moreover, our proposed framework has successfully identified alternative reservoir policies that are more robust to hydroclimatic uncertainties while being capable of better addressing the tradeoffs across the Conowingo Dam's multi-sector services.

South African HIV self-testing policy and guidance considerations

PubMed Central

Jankelowitz, Lauren; Adams, Siraaj; Msimanga, Busisiwe R.; Nevhutalu, Zwoitwaho; Rhagnath, Naleni; Shroufi, Amir; Devillé, Walter; Kazangarare, Victoria; van der Wiel, Renee; Templeman, Hugo; Conradie, Francesca; Chidarikire, Thato; Gray, Andy

2017-01-01

The gap in HIV testing remains significant and new modalities such as HIV self-testing (HIVST) have been recommended to reach key and under-tested populations. In December 2016, the World Health Organization (WHO) released the Guidelines on HIV Self-Testing and Partner Notification: A Supplement to the Consolidated Guidelines on HIV Testing Services (HTS) and urged member countries to develop HIVST policy and regulatory frameworks. In South Africa, HIVST was included as a supplementary strategy in the National HIV Testing Services Policy in 2016, and recently, guidelines for HIVST were included in the South African National Strategic Plan for HIV, sexually transmitted infections and tuberculosis 2017–2022. This document serves as an additional guidance for the National HIV Testing Services Policy 2016, with specific focus on HIVST. It is intended for policy advocates, clinical and non-clinical HTS providers, health facility managers and healthcare providers in private and public health facilities, non-governmental, community-based and faith-based organisations involved in HTS and outreach, device manufacturers, workplace programmes and institutes of higher education. PMID:29568643

Modeling contextual influences on parents with intellectual disability and their children.

PubMed

Wade, Catherine; Llewellyn, Gwynnyth; Matthews, Jan

2011-11-01

Many parents with intellectual disability experience living conditions associated with risk for children and parents. This study used structural equation modeling to test a theoretical model of the relationships among parent, child, family, and contextual variables in 120 Australian families where a parent had an intellectual disability. Findings revealed that parenting practices had a direct effect on children's well being, that social support was associated with children's well being through the mediator of parenting practices, and that access to social support had a direct influence on parenting practices. Implications of the findings for research, intervention, and policy are explored, with the goal of promoting optimal well being for children who are raised by parents with intellectual disability.

Speaking Out: The Use of Tests in the Policy Arena. Proceedings of the New Jersey Conference.

ERIC Educational Resources Information Center

Educational Testing Service, Washington, DC. Education Policy Research Inst.

In February, 1976, the Education Policy Research Institute sponsored this conference on the use of tests in educational policy formation; these six papers were presented at that conference. In the first paper, Jenne K. Britell discusses four points that have historically characterized debates about testing; psychometric and technical issues;…

Urineschool: A Study of the Impact of the Earls Decision on High School Random Drug Testing Policies.

ERIC Educational Resources Information Center

Conlon, Cynthia Kelly

2003-01-01

Examines impact of Supreme Court's 2002 decision in "Board of Education v. Earls" on high school random drug-testing policies and practices. Court held that random drug-testing policy at Tecumseh, Oklahoma, school district did not violate students' Fourth Amendment right against unreasonable searches. (Contains 46 references.) (PKP)

The Influence of Drug Testing Attributes, Participation, and Personality on Potential Applicant's Attitudes and Job Pursuit Intentions.

ERIC Educational Resources Information Center

Stoffey, Ronald W.

Researchers are increasingly aware of the importance of job applicants' reactions to the personnel selection process. This study examines three variables in connection with drug testing policies: (1) the potential applicant's reactions to two different drug testing policies which varied in terms of drug policy characteristics and their impact on…

Formulation and Realisation of Evaluation Policy: Inconcistencies and Problematic Issues

ERIC Educational Resources Information Center

Skedsmo, Guri

2011-01-01

The introduction of the national evaluation system in the Norwegian education system can be described as a shift in the Norwegian educational policy from the use of input oriented policy instruments towards a more output oriented policy. The new tool-kit consists of different evaluation tools, such as standardised tests, diagonistic tests etc.…

Testing, Opportunity Allocation, and Asian and Pacific Americans. The Proceedings of a Hearing Co-Sponsored by the National Commission on Testing and Public Policy and the National Association for Asian and Pacific American Education. (Honolulu, Hawaii, April 11, 1987). A Report.

ERIC Educational Resources Information Center

Lam, Tony C. M.

The National Commission on Testing and Public Policy is conducting a 3-year policy-oriented investigation of the role of tests, especially standardized, norm-referenced tests, in the allocation of educational, training, and employment opportunities in the United States today. This document reports on the first hearing, which focused on the…

Assessing restrictiveness of national alcohol marketing policies.

PubMed

Esser, Marissa B; Jernigan, David H

2014-01-01

To develop an approach for monitoring national alcohol marketing policies globally, an area of the World Health Organization's (WHO) Global Alcohol Strategy. Data on restrictiveness of alcohol marketing policies came from the 2002 and 2008 WHO Global Surveys on Alcohol and Health. We included four scales in a sensitivity analysis to determine optimal weights to score countries on their marketing policies and applied the selected scale to assess national marketing policy restrictiveness. Nearly, 36% of countries had no marketing restrictions. The overall restrictiveness levels were not significantly different between 2002 and 2008. The number of countries with strict marketing regulations did not differ across years. This method of monitoring alcohol marketing restrictiveness helps track progress towards implementing WHO'S Global Alcohol Strategy. Findings indicate a consistent lack of restrictive policies over time, making this a priority area for national and global action. © The Author 2014. Medical Council on Alcohol and Oxford University Press. All rights reserved.

College Readiness Policy Implementation in the Massachusetts Public Higher Education System from Policy to Practice: An Analysis of the Implementation of the State College Placement Testing (CPT) Policies at the Four Year Public Institutions

ERIC Educational Resources Information Center

Solomon, Jibril

2013-01-01

In 1998, the Massachusetts Board of Higher Education, with assistance from the Developmental Assessment and Placement Advisory Committee, adopted an assessment policy that set standards for college placement testing at Massachusetts public colleges and universities. The purposes of the policy were to place students more adequately suited for…

Estimating the Technology of Cognitive and Noncognitive Skill Formation*

PubMed Central

Cunha, Flavio; Heckman, James; Schennach, Susanne

2009-01-01

This paper formulates and estimates multistage production functions for child cognitive and noncognitive skills. Output is determined by parental environments and investments at different stages of childhood. We estimate the elasticity of substitution between investments in one period and stocks of skills in that period to assess the benefits of early investment in children compared to later remediation. We establish nonparametric identification of a general class of nonlinear factor models. A by-product of our approach is a framework for evaluating childhood interventions that does not rely on arbitrarily scaled test scores as outputs and recognizes the differential effects of skills in different tasks. Using the estimated technology, we determine optimal targeting of interventions to children with different parental and personal birth endowments. Substitutability decreases in later stages of the life cycle for the production of cognitive skills. It increases in later stages of the life cycle for the production of noncognitive skills. This finding has important implications for the design of policies that target the disadvantaged. For some configurations of disadvantage and outcomes, it is optimal to invest relatively more in the later stages of childhood. PMID:20563300

Optimal and Autonomous Control Using Reinforcement Learning: A Survey.

PubMed

Kiumarsi, Bahare; Vamvoudakis, Kyriakos G; Modares, Hamidreza; Lewis, Frank L

2018-06-01

This paper reviews the current state of the art on reinforcement learning (RL)-based feedback control solutions to optimal regulation and tracking of single and multiagent systems. Existing RL solutions to both optimal and control problems, as well as graphical games, will be reviewed. RL methods learn the solution to optimal control and game problems online and using measured data along the system trajectories. We discuss Q-learning and the integral RL algorithm as core algorithms for discrete-time (DT) and continuous-time (CT) systems, respectively. Moreover, we discuss a new direction of off-policy RL for both CT and DT systems. Finally, we review several applications.

Oil shocks in New Keynesian models: Positive and normative implications

NASA Astrophysics Data System (ADS)

Chang, Jian

Chapter 1 investigates optimal monetary policy response towards oil shocks in a New Keynesian model. We find that optimal policy, in general, becomes contractionary in response to an adverse oil shock. However, the optimal policy rule and the inflation-output trade-off depend on the specific structure of the model. The benchmark economy consists of a flexible-price energy sector and a sticky-price manufacturing sector where energy is used as an intermediate input. We show that optimal policy is to stabilize the sticky (core) price level. We then show that after incorporating a less oil-dependent sticky-price service sector, the model exhibits a trade-off in stabilizing prices and output gaps in the different sticky-price sectors. It predicts that central bank should not try to stabilize the core price level, and the economy will experience higher inflation and rising output gaps, even if central banks respond optimally. Chapter 2 addresses the observed volatility and persistence of real exchange rates and the terms of trade. It contributes to the literature with a quantitative study on the U.S. and Canada. A two-country New Keynesian model consisting of traded, non-traded, and oil production sectors is proposed to examine the time series properties of the real exchange rate, the terms of trade and the real oil price. We find that after incorporating several realistic features (namely oil price shocks, sector specific labor, non-traded goods, asymmetric pricing decisions of exporters and asymmetric consumer preferences over tradables), the benchmark model broadly matches the volatilities of the relative prices and some business cycle correlations. The model matches the data more closely after adding real demand shocks, suggesting their importance in explaining the relative price movements between the US and Canada. Chapter 3 explores several sources and transmission channels of international relative price movements. In particular, we elaborate on the role of imperfect labor mobility, pricing decisions of exporting firms, oil price shocks and asymmetric consumer preferences over tradables. Our results suggest that: Incorporating both producer currency pricing and local currency pricing assumptions produces more reasonable relative price movements. A model with imperfect labor mobility generates larger relative price volatility. Oil price shocks only contribute to terms of trade variability when oil is modeled as part of the traded basket. And asymmetric consumer preferences contribute to the volatility of the real exchange rate.

Harnessing a community for sustainable disaster response and recovery: an operational model for integrating nongovernmental organizations.

PubMed

Acosta, Joie; Chandra, Anita

2013-08-01

Nongovernmental organizations (NGOs) are important to a community during times of disaster and routine operations. However, their effectiveness is reduced without an operational framework that integrates response and recovery efforts. Without integration, coordination among NGOs is challenging and use of government resources is inefficient. We developed an operational model to specify NGO roles and responsibilities before, during, and after a disaster. We conducted an analysis of peer-reviewed literature, relevant policy, and federal guidance to characterize the capabilities of NGOs, contextual factors that determine their involvement in disaster operations, and key services they provide during disaster response and recovery. We also identified research questions that should be prioritized to improve coordination and communication between NGOs and government. Our review showed that federal policy stresses the importance of partnerships between NGOs and government agencies and among other NGOs. Such partnerships can build deep local networks and broad systems that reach from local communities to the federal government. Understanding what capacities NGOs need and what factors influence their ability to perform during a disaster informs an operational model that could optimize NGO performance. Although the operational model needs to be applied and tested in community planning and disaster response, it holds promise as a unifying framework across new national preparedness and recovery policy, and provides structure to community planning, resource allocation, and metrics on which to evaluate NGO disaster involvement.

Life cycle assessment of a national policy proposal - the case of a Swedish waste incineration tax.

PubMed

Björklund, Anna E; Finnveden, Göran

2007-01-01

At the core of EU and Swedish waste policy is the so-called waste hierarchy, according to which waste should first be prevented, but should otherwise be treated in the following order of prioritisation: reuse, recycling when environmentally motivated, energy recovery, and last landfilling. Some recent policy decisions in Sweden aim to influence waste management in the direction of the waste hierarchy. In 2001 a governmental commission assessed the economic and environmental impacts of introducing a weight-based tax on waste incineration, the purpose of which would be to encourage waste reduction and increase materials recycling and biological treatment. This paper presents the results of a life cycle assessment (LCA) of the waste incineration tax proposal. It was done in the context of a larger research project concerning the development and testing of a framework for Strategic Environmental Assessment (SEA). The aim of this paper is to assess the life cycle environmental impacts of the waste incineration tax proposal, and to investigate whether there are any possibilities of more optimal design of such a tax. The proposed design of the waste incineration tax results in increased recycling, but only in small environmental improvements. A more elaborate tax design is suggested, in which the tax level would partly be related to the fossil carbon content of the waste.

Three essays on monetary policy responses to oil price shocks

NASA Astrophysics Data System (ADS)

Plante, Michael

This dissertation contains three chapters which explore the question of how monetary policy should respond to changes in the price of oil. Each chapter explores the question from the perspective of a different economic environment. The first chapter examines welfare maximizing optimal monetary policy in a closed economy New Keynesian model that is extended to include household and firm demand for oil products, sticky wages, and capital accumulation. When households and firms demand oil products a natural difference arises between the Consumer Price Index (CPI), the core CPI, and the GDP deflator. I show that when nominal wages are flexible then the optimal policy places a heavy emphasis on stabilizing the inflation rate of the core CPI. If aggregate nominal wages are sticky then the central bank should focus on stabilizing some combination of core inflation and nominal wage inflation. Under no case examined is it optimal to stabilize either GDP deflator or CPI inflation. The second chapter examines monetary policy responses to oil price shocks in a small open economy with traded and non-traded goods. Oil and labor are used to produce the traded and non-traded goods and prices are sticky in the non-traded sector. I show analytically that the ratio of the oil and labor cost shares in the traded and non-traded sectors is crucial for determining the dynamic behavior of many macroeconomic variables after a rise in the price of oil. A policy of fixed exchange rates can produce higher or lower inflation in the non-traded sector depending upon the ratio. Likewise, a policy that stabilizes the inflation rate of prices in the non-traded sector can cause the nominal exchange rate to appreciate or depreciate. For the proper calibration, a policy that stabilizes core inflation produces results very close to the one that stabilizes non-traded inflation. Analytical results show that the fixed exchange rate always produces a unique solution. The policy of stabilizing non-traded inflation produces a unique solution so long as the nominal interest rate is raised more than one for one with increases in non-traded inflation. A policy of stabilizing core inflation, however, produces a unique solution only if the response is greater than one for one and less then one divided by one minus the share of the non-traded good in the CPI. In the third chapter I consider monetary and fiscal policy responses to oil price shocks in a low income oil importing country. The model used in this chapter differs from the model in the second chapter in that there is currency substitution, household demand for oil products, and a potential subsidy on the purchase of oil products by households. I examine the dynamic properties and the welfare implications of a set of inflation targeting policies and a group of policies that subsidize the price of oil and finance the subsidy through a combination of raising lump sum taxes and printing money. The dynamic properties of the inflation targeting policies are similar in many regards to those in the second chapter as the key assumptions driving the results are the same in the two models. For the policies which subsidize the price of oil I show that both the choice to have the subsidy and how to finance it matter a great deal for the behavior of the macroeconomic variables. In terms of welfare, for most calibrations there are only minor differences between the inflation targeting polices, the policy with a subsidy funded by lump sum taxes, and the baseline policy with no subsidy. The policy with a subsidy financed by the inflation tax generally causes significant welfare losses compared to the policy with no pass through.

Measuring mechanisms for quality assurance in primary care systems in transition: test of a new instrument in Slovenia and Uzbekistan.

PubMed

Kringos, Dionne Sofia; Boerma, Wienke; Pellny, Martina

2009-01-01

This World Health Organization (WHO) study aimed to develop and field test an instrument to assess the availability of structures and mechanisms for managing quality in primary care in countries in transition. The instrument is based on a literature study, consensus meetings with experts, and observations in these countries. It consists of three parts: a semi-structured questionnaire on national policies and mechanisms; a structured questionnaire for general practitioners (GPs); and a structured questionnaire for use with managers of primary care facilities. The instrument has been field tested in 2007 in Slovenia and Uzbekistan. In Slovenia, leadership on quality improvement was weak and local managers reported few incentives and resources to control quality. There was a lack of external support for quality improvement activities. Availability and use of clinical guidelines for GPs were not optimal. GPs found teamwork and communication with patients inadequate. In Uzbekistan, primary care quality and standards in health centres were extensively regulated and laid down in numerous manuals, instructions and other documents. Managers, however, indicated the need for more financial and non-financial levers for quality improvement and they wanted to know more about modern healthcare management. GPs reported strong involvement in activities such as peer review and clinical audit, and reported frequent use of clinical guidelines. Overall, the information gathered with the provisional instrument has resulted in policy recommendations. At the same time, the pilot resulted in improvements to the instrument. Application of the instrument helps decision makers to identify improvement areas in the infrastructure for managing the quality of primary care.

What is the impact of disease prevalence upon health technology assessment?

PubMed

Rotily, Michel; Roze, Stéphane

2013-12-01

As national budgets for health care will remain under stress for the foreseeable future, health technology assessment (HTA) aimed at offering guidance to policy-making will have an increasing role to play in optimizing resources. The emergence of new treatment paradigms and health technologies, and the prevalence studies which determine when a disease is a current or future burden for patients and the community are in the roots of the HTA process. Analysing studies on screening test strategies and health care policy, this paper revisits two key concepts in epidemiology, prevalence and incidence, in order to show their major impact upon HTA. Utilization of the predictive values of screening tests that include prevalence in their calculations, and analysing all options for screening strategies are necessary in HTA. Cost-effectiveness analyses and statistical models should include potential externalities, especially the impact of prevention and treatment on infectious disease prevalence. Beyond estimates of cost-effectiveness ratios, decision makers also need to know by how much their annual health care budget is likely to increase or decrease in the years following the emergence of new technologies: hence the importance of incidence- or prevalence-based economic evaluations. As new paradigms are occurring, especially in the field of oncology, with treatments targeted to 'small' groups of patients identified through genetic testing, prevalence data are strongly needed. Precise estimates of disease prevalence, in general populations as well as in risk or targeted groups, will therefore be necessary to improve HTA process. Copyright © 2013 Elsevier Ltd. All rights reserved.

MoGIRE: A Model for Integrated Water Management

NASA Astrophysics Data System (ADS)

Reynaud, A.; Leenhardt, D.

2008-12-01

Climate change and growing water needs have resulted in many parts of the world in water scarcity problems that must by managed by public authorities. Hence, policy-makers are more and more often asked to define and to implement water allocation rules between competitive users. This requires to develop new tools aiming at designing those rules for various scenarios of context (climatic, agronomic, economic). If models have been developed for each type of water use however, very few integrated frameworks link these different uses, while such an integrated approach is a relevant stake for designing regional water and land policies. The lack of such integrated models can be explained by the difficulty of integrating models developed by very different disciplines and by the problem of scale change (collecting data on large area, arbitrate between the computational tractability of models and their level of aggregation). However, modelers are more and more asked to deal with large basin scales while analyzing some policy impacts at very high detailed levels. These contradicting objectives require to develop new modeling tools. The CALVIN economically-driven optimization model developed for managing water in California is a good example of this type of framework, Draper et al. (2003). Recent reviews of the literature on integrated water management at the basin level include Letcher et al. (2007) or Cai (2008). We present here an original framework for integrated water management at the river basin scale called MoGIRE ("Modèle pour la Gestion Intégrée de la Ressource en Eau"). It is intended to optimize water use at the river basin level and to evaluate scenarios (agronomic, climatic or economic) for a better planning of agricultural and non-agricultural water use. MoGIRE includes a nodal representation of the water network. Agricultural, urban and environmental water uses are also represented using mathematical programming and econometric approaches. The model then optimizes at each date (10 days step) the allocation of water across agricultural and urban water demands in order to maximize the social surplus derived from water consumption given the constraints imposed by the water network. An application of the model is proposed for the Neste system located in South-West of France. 67 regions competing for water allocation have been identified in the Neste system. Those regions are characterized by specific cropping systems, specific climate and soil characteristics and by their connections to the water network. The model, including the nodal representation of the water network, has been coded using the algebraic modeling language GAMS. We are currently analyzing the robustness of the approach through scenario testing. Keywords : Integrated water management, optimization-simulation model, agronomic-economic modeling, river basin.

Ordering policy for stock-dependent demand rate under progressive payment scheme: a comment

NASA Astrophysics Data System (ADS)

Glock, Christoph H.; Ries, Jörg M.; Schwindl, Kurt

2015-04-01

In a recent paper, Soni and Shah developed a model for finding the optimal ordering policy for a retailer facing stock-dependent demand and a supplier offering a progressive payment scheme. In this comment, we correct several errors in the formulation of the models of Soni and Shah and modify some assumptions to increase the model's applicability. Numerical examples illustrate the benefits of our modifications.

Aligning and Inventing Practices to Achieve Inclusive Assessment Policies: A Decade of Work toward Optimal Access for US Students with Disabilities 2001-2011

ERIC Educational Resources Information Center

Weigert, Susan C.

2012-01-01

The decade following the publication of the No Child Left Behind Act 2001 was an innovative period with respect to inclusive assessment practices for students with disabilities (SWDs). As the United States educational policies under the Obama Administration's Race to the Top initiative re-conceive the inclusion of SWDs in state assessment-based…

Evaluation of the Navys Sea/Shore Flow Policy

DTIC Science & Technology

2016-06-01

Std. Z39.18 i Abstract CNA developed an independent Discrete -Event Simulation model to evaluate and assess the effect of...a more steady manning level, but the variability remains, even if the system is optimized. In building a Discrete -Event Simulation model, we...steady-state model. In FY 2014, CNA developed a Discrete -Event Simulation model to evaluate the impact of sea/shore flow policy (the DES-SSF model

The Politics of Identity: Re-Examining the Appetite for Affirmative Action Policies in Higher Education among African-Americans in a Post-Racial Society

ERIC Educational Resources Information Center

White, Tarsha D.

2014-01-01

Broad inferences have been made that the election of a Black American President indicates that America now functions in a post-racist society. This optimism has fueled a major discussion for changes in American policies which directly affect minorities; in particular, those related to affirmative action in higher education are under attack. Due to…

Optimal Ozone Control with Inclusion of Spatiotemporal Marginal Damages and Electricity Demand.

PubMed

Mesbah, S Morteza; Hakami, Amir; Schott, Stephan

2015-07-07

Marginal damage (MD), or damage per ton of emission, is a policy metric used for effective pollution control and reducing the corresponding adverse health impacts. However, for a pollutant such as NOx, the MD varies by the time and location of the emissions, a complication that is not adequately accounted for in the currently implemented economic instruments. Policies accounting for MD information would aim to encourage emitters with large MDs to reduce their emissions. An optimization framework is implemented to account for NOx spatiotemporal MDs calculated through adjoint sensitivity analysis and to simulate power plants' behavior under emission and simplified electricity constraints. The results from a case study of U.S. power plants indicate that time-specific MDs are high around noon and low in the evening. Furthermore, an emissions reduction of about 40% and a net benefit of about $1200 million can be gained for this subset of power plants if a larger fraction of the electricity demand is supplied by power plants at low-damage times and in low-damage locations. The results also indicate that the consideration of temporal effects in NOx control policies results in a comparable net benefit to the consideration of spatial or spatiotemporal effects, thus providing a promising option for policy development.

Energy Technology Allocation for Distributed Energy Resources: A Technology-Policy Framework

NASA Astrophysics Data System (ADS)

Mallikarjun, Sreekanth

Distributed energy resources (DER) are emerging rapidly. New engineering technologies, materials, and designs improve the performance and extend the range of locations for DER. In contrast, constructing new or modernizing existing high voltage transmission lines for centralized generation are expensive and challenging. In addition, customer demand for reliability has increased and concerns about climate change have created a pull for swift renewable energy penetration. In this context, DER policy makers, developers, and users are interested in determining which energy technologies to use to accommodate different end-use energy demands. We present a two-stage multi-objective strategic technology-policy framework for determining the optimal energy technology allocation for DER. The framework simultaneously considers economic, technical, and environmental objectives. The first stage utilizes a Data Envelopment Analysis model for each end-use to evaluate the performance of each energy technology based on the three objectives. The second stage incorporates factor efficiencies determined in the first stage, capacity limitations, dispatchability, and renewable penetration for each technology, and demand for each end-use into a bottleneck multi-criteria decision model which provides the Pareto-optimal energy resource allocation. We conduct several case studies to understand the roles of various distributed energy technologies in different scenarios. We construct some policy implications based on the model results of set of case studies.

Optimal maintenance policy incorporating system level and unit level for mechanical systems

NASA Astrophysics Data System (ADS)

Duan, Chaoqun; Deng, Chao; Wang, Bingran

2018-04-01

The study works on a multi-level maintenance policy combining system level and unit level under soft and hard failure modes. The system experiences system-level preventive maintenance (SLPM) when the conditional reliability of entire system exceeds SLPM threshold, and also undergoes a two-level maintenance for each single unit, which is initiated when a single unit exceeds its preventive maintenance (PM) threshold, and the other is performed simultaneously the moment when any unit is going for maintenance. The units experience both periodic inspections and aperiodic inspections provided by failures of hard-type units. To model the practical situations, two types of economic dependence have been taken into account, which are set-up cost dependence and maintenance expertise dependence due to the same technology and tool/equipment can be utilised. The optimisation problem is formulated and solved in a semi-Markov decision process framework. The objective is to find the optimal system-level threshold and unit-level thresholds by minimising the long-run expected average cost per unit time. A formula for the mean residual life is derived for the proposed multi-level maintenance policy. The method is illustrated by a real case study of feed subsystem from a boring machine, and a comparison with other policies demonstrates the effectiveness of our approach.

System-wide and Superemitter Policy Options for the Abatement of Methane Emissions from the U.S. Natural Gas System

NASA Astrophysics Data System (ADS)

Mayfield, E. N.; Robinson, A. L.; Cohon, J. L.

2017-12-01

This work assesses trade-offs between system-wide and superemitter policy options for reducing methane emissions from compressor stations in the U.S. transmission and storage system. Leveraging recently collected national emissions and activity data sets, we developed a new process-based emissions model implemented in a Monte Carlo simulation framework to estimate emissions for each component and facility in the system. We find that approximately 83% of emissions, given the existing suite of technologies, have the potential to be abated, with only a few emission categories comprising a majority of emissions. We then formulate optimization models to determine optimal abatement strategies. Most emissions across the system (approximately 80%) are efficient to abate, resulting in net benefits ranging from 160M to 1.2B annually across the system. The private cost burden is minimal under standard and tax instruments, and if firms market the abated natural gas, private net benefits may be generated. Superemitter policies, namely, those that target the highest emitting facilities, may reduce the private cost burden and achieve high emission reductions, especially if emissions across facilities are highly skewed. However, detection across all facilities is necessary regardless of the policy option and there are nontrivial net benefits resulting from abatement of relatively low-emitting sources.

System-wide and Superemitter Policy Options for the Abatement of Methane Emissions from the U.S. Natural Gas System.

PubMed

Mayfield, Erin N; Robinson, Allen L; Cohon, Jared L

2017-05-02

This work assesses trade-offs between system-wide and superemitter policy options for reducing methane emissions from compressor stations in the U.S. transmission and storage system. Leveraging recently collected national emissions and activity data sets, we developed a new process-based emissions model implemented in a Monte Carlo simulation framework to estimate emissions for each component and facility in the system. We find that approximately 83% of emissions, given the existing suite of technologies, have the potential to be abated, with only a few emission categories comprising a majority of emissions. We then formulate optimization models to determine optimal abatement strategies. Most emissions across the system (approximately 80%) are efficient to abate, resulting in net benefits ranging from $160M to $1.2B annually across the system. The private cost burden is minimal under standard and tax instruments, and if firms market the abated natural gas, private net benefits may be generated. Superemitter policies, namely, those that target the highest emitting facilities, may reduce the private cost burden and achieve high emission reductions, especially if emissions across facilities are highly skewed. However, detection across all facilities is necessary regardless of the policy option and there are nontrivial net benefits resulting from abatement of relatively low-emitting sources.

A Systems Engineering Approach to Multiple Attribute Utility Theory and Multiple Objective Optimization Theory: With Application To Aircraft Retrofit Design.

DTIC Science & Technology

1980-01-01

me produce this dissertation. I wish to thank Professors John E. Gibson and Chelsea C. White, III for their advice and contributions in this effort. My...National Meeting, Los Angeles, Ca., 1ov., 97$ Everett, J., Hax, A., Lewison , V. and 4utts, D., "Optimization of a Fleet of Large Tarkers and Bulkers...Arrow, C. J., Mardecai, K., Public Investment, The Rate of Return and Optimal Fiscal Policy, Johns Hopkins Press, Baltimore, Maryland, 1970. Banker

Limits to evidence-based health policymaking: policy hurdles to structural HIV prevention in Tanzania.

PubMed

Hunsmann, Moritz

2012-05-01

Despite the well-documented role of highly co-endemic biological cofactors in facilitating HIV transmission and the availability of comparatively inexpensive tools to control them, cofactor-related interventions are only hesitantly included into African HIV prevention strategies. Against this background, this study analyzes political obstacles to policy-uptake of evidence concerning structural HIV prevention. The data used stem from fieldwork conducted in Tanzania between 2007 and 2009. They include 92 in-depth interviews with key AIDS policymakers and observations of 8 national-level policy meetings. Adopting a political economy perspective, the study shows that 1) assuming cost-aversion as a spontaneous reflex of policymakers is empirically wrong and analytically misleading, 2) that political constituencies induce a path dependence of allocative decisions inconducive to structural prevention, 3) that interventions' political attractiveness depends on the nature of their outputs and the expected temporality of political returns, 4) that policy fragmentation entailed by vertical disease control disfavours the consideration of broader causalities, and 5) that cofactor-based measures are hampered by policymakers' perception of structural prevention as being excessively complex and ultimately tantamount to poverty eradication. Confronting the policy players' reading of the Tanzanian situation with recent and classical literature on evidence-based decision-making and the politics of public health, this paper shows that, far from being strictly evidence-driven, HIV prevention policies result from a politically negotiated aggregation of competing, frequently non-optimizing rationalities. A realistic appraisal of policy processes suggests that the failure to consider the invariably political nature of HIV-related policymaking hampers the formulation of effective, politically informed strategies for positive change. Consequently, developing policy practitioners' understanding of how to effectively engage in evidence-influenced political struggles over priorities might be more instrumental in improving HIV prevention strategies than attempts to sidestep these ineradicably antagonistic controversies though technical decision tools meant to optimize health outcomes via the formulation of 'rational consensus'. Copyright © 2012 Elsevier Ltd. All rights reserved.

Stackelberg Game of Buyback Policy in Supply Chain with a Risk-Averse Retailer and a Risk-Averse Supplier Based on CVaR

PubMed Central

Zhou, Yanju; Chen, Qian; Chen, Xiaohong; Wang, Zongrun

2014-01-01

This paper considers a decentralized supply chain in which a single supplier sells a perishable product to a single retailer facing uncertain demand. We assume that the supplier and the retailer are both risk averse and utilize Conditional Value at Risk (CVaR), a risk measure method which is popularized in financial risk management, to estimate their risk attitude. We establish a buyback policy model based on Stackelberg game theory under considering supply chain members' risk preference and get the expressions of the supplier's optimal repurchase price and the retailer's optimal order quantity which are compared with those under risk neutral case. Finally, a numerical example is applied to simulate that model and prove related conclusions. PMID:25247605

Stackelberg game of buyback policy in supply chain with a risk-averse retailer and a risk-averse supplier based on CVaR.

PubMed

Zhou, Yanju; Chen, Qian; Chen, Xiaohong; Wang, Zongrun

2014-01-01

This paper considers a decentralized supply chain in which a single supplier sells a perishable product to a single retailer facing uncertain demand. We assume that the supplier and the retailer are both risk averse and utilize Conditional Value at Risk (CVaR), a risk measure method which is popularized in financial risk management, to estimate their risk attitude. We establish a buyback policy model based on Stackelberg game theory under considering supply chain members' risk preference and get the expressions of the supplier's optimal repurchase price and the retailer's optimal order quantity which are compared with those under risk neutral case. Finally, a numerical example is applied to simulate that model and prove related conclusions.

Replenishment policy for an inventory model under inflation

NASA Astrophysics Data System (ADS)

Singh, Vikramjeet; Saxena, Seema; Singh, Pushpinder; Mishra, Nitin Kumar

2017-07-01

The purpose of replenishment is to keep the flow of inventory in the system. To determine an optimal replenishment policy is a great challenge in developing an inventory model. Inflation is defined as the rate at which the prices of goods and services are rising over a time period. The cost parameters are affected by the rate of inflation. High rate of inflation affects the organizations financial conditions. Based on the above backdrop the present paper proposes the retailers replenishment policy for deteriorating items with different cycle lengths under inflation. The shortages are partially backlogged. At last numerical examples validate the results.

Balancing exploration, uncertainty and computational demands in many objective reservoir optimization

NASA Astrophysics Data System (ADS)

Zatarain Salazar, Jazmin; Reed, Patrick M.; Quinn, Julianne D.; Giuliani, Matteo; Castelletti, Andrea

2017-11-01

Reservoir operations are central to our ability to manage river basin systems serving conflicting multi-sectoral demands under increasingly uncertain futures. These challenges motivate the need for new solution strategies capable of effectively and efficiently discovering the multi-sectoral tradeoffs that are inherent to alternative reservoir operation policies. Evolutionary many-objective direct policy search (EMODPS) is gaining importance in this context due to its capability of addressing multiple objectives and its flexibility in incorporating multiple sources of uncertainties. This simulation-optimization framework has high potential for addressing the complexities of water resources management, and it can benefit from current advances in parallel computing and meta-heuristics. This study contributes a diagnostic assessment of state-of-the-art parallel strategies for the auto-adaptive Borg Multi Objective Evolutionary Algorithm (MOEA) to support EMODPS. Our analysis focuses on the Lower Susquehanna River Basin (LSRB) system where multiple sectoral demands from hydropower production, urban water supply, recreation and environmental flows need to be balanced. Using EMODPS with different parallel configurations of the Borg MOEA, we optimize operating policies over different size ensembles of synthetic streamflows and evaporation rates. As we increase the ensemble size, we increase the statistical fidelity of our objective function evaluations at the cost of higher computational demands. This study demonstrates how to overcome the mathematical and computational barriers associated with capturing uncertainties in stochastic multiobjective reservoir control optimization, where parallel algorithmic search serves to reduce the wall-clock time in discovering high quality representations of key operational tradeoffs. Our results show that emerging self-adaptive parallelization schemes exploiting cooperative search populations are crucial. Such strategies provide a promising new set of tools for effectively balancing exploration, uncertainty, and computational demands when using EMODPS.

Adaptive Management of Bull Trout Populations in the Lemhi Basin

USGS Publications Warehouse

Peterson, James T.; Tyre, Andrew J.; Converse, Sarah J.; Bogich, Tiffany L.; Miller, Damien; Post van der Burg, Max; Thomas, Carmen; Thompson, Ralph J.; Wood, Jeri; Brewer, Donna; Runge, Michael C.

2011-01-01

The bull trout Salvelinus confluentus, a stream-living salmonid distributed in drainages of the northwestern United States, is listed as threatened under the Endangered Species Act because of rangewide declines. One proposed recovery action is the reconnection of tributaries in the Lemhi Basin. Past water use policies in this core area disconnected headwater spawning sites from downstream habitat and have led to the loss of migratory life history forms. We developed an adaptive management framework to analyze which types of streams should be prioritized for reconnection under a proposed Habitat Conservation Plan. We developed a Stochastic Dynamic Program that identified optimal policies over time under four different assumptions about the nature of the migratory behavior and the effects of brook trout Salvelinus fontinalis on subpopulations of bull trout. In general, given the current state of the system and the uncertainties about the dynamics, the optimal policy would be to connect streams that are currently occupied by bull trout. We also estimated the value of information as the difference between absolute certainty about which of our four assumptions were correct, and a model averaged optimization assuming no knowledge. Overall there is little to be gained by learning about the dynamics of the system in its current state, although in other parts of the state space reducing uncertainties about the system would be very valuable. We also conducted a sensitivity analysis; the optimal decision at the current state does not change even when parameter values are changed up to 75% of the baseline values. Overall, the exercise demonstrates that it is possible to apply adaptive management principles to threatened and endangered species, but logistical and data availability constraints make detailed analyses difficult.

A game theory-reinforcement learning (GT-RL) method to develop optimal operation policies for multi-operator reservoir systems

NASA Astrophysics Data System (ADS)

Madani, Kaveh; Hooshyar, Milad

2014-11-01

Reservoir systems with multiple operators can benefit from coordination of operation policies. To maximize the total benefit of these systems the literature has normally used the social planner's approach. Based on this approach operation decisions are optimized using a multi-objective optimization model with a compound system's objective. While the utility of the system can be increased this way, fair allocation of benefits among the operators remains challenging for the social planner who has to assign controversial weights to the system's beneficiaries and their objectives. Cooperative game theory provides an alternative framework for fair and efficient allocation of the incremental benefits of cooperation. To determine the fair and efficient utility shares of the beneficiaries, cooperative game theory solution methods consider the gains of each party in the status quo (non-cooperation) as well as what can be gained through the grand coalition (social planner's solution or full cooperation) and partial coalitions. Nevertheless, estimation of the benefits of different coalitions can be challenging in complex multi-beneficiary systems. Reinforcement learning can be used to address this challenge and determine the gains of the beneficiaries for different levels of cooperation, i.e., non-cooperation, partial cooperation, and full cooperation, providing the essential input for allocation based on cooperative game theory. This paper develops a game theory-reinforcement learning (GT-RL) method for determining the optimal operation policies in multi-operator multi-reservoir systems with respect to fairness and efficiency criteria. As the first step to underline the utility of the GT-RL method in solving complex multi-agent multi-reservoir problems without a need for developing compound objectives and weight assignment, the proposed method is applied to a hypothetical three-agent three-reservoir system.

Traveling-Wave Tube Cold-Test Circuit Optimization Using CST MICROWAVE STUDIO

NASA Technical Reports Server (NTRS)

Chevalier, Christine T.; Kory, Carol L.; Wilson, Jeffrey D.; Wintucky, Edwin G.; Dayton, James A., Jr.

2003-01-01

The internal optimizer of CST MICROWAVE STUDIO (MWS) was used along with an application-specific Visual Basic for Applications (VBA) script to develop a method to optimize traveling-wave tube (TWT) cold-test circuit performance. The optimization procedure allows simultaneous optimization of circuit specifications including on-axis interaction impedance, bandwidth or geometric limitations. The application of Microwave Studio to TWT cold-test circuit optimization is described.

Analysis and advocacy in home- and community-based care: an approach in three parts.

PubMed

Hudson, Robert B

2010-01-01

A new chapter in health policy presents both challenges and opportunities for aging policy analysts and advocates concerned with long-term care. Millions of long-term care recipients and providers live today in a pubic policy netherworld, one principally associated with Medicaid. I suggest here that moving policy forward will entail three key steps: (a) to overcome structural lag in key home and community-based care (HCBC) policy arenas; (b) to reverse a contemporary pattern of risk-shifting from institutions to individuals; and (c) to inform and empower caregivers to have their own pressing needs recognized. Recent developments in Washington provide new optimism on these fronts. Voluntary long-term care and community-based care (LTC/HCBC) proposals are on the table within the broad context of health care reform. Whether they remain will be, in large, part a function of how far we have moved along the fronts described: modernizing policies, recognizing risks, and activating neglected policy constituencies.

Excess capacity: markets regulation, and values.

PubMed Central

Madden, C W

1999-01-01

OBJECTIVE: To examine the conceptual bases for the conflicting views of excess capacity in healthcare markets and their application in the context of today's turbulent environment. STUDY SETTING: The policy and research literature of the past three decades. STUDY DESIGN: The theoretical perspectives of alternative economic schools of thought are used to support different policy positions with regard to excess capacity. Changes in these policy positions over time are linked to changes in the economic and political environment of the period. The social values implied by this history are articulated. DATA COLLECTION: Standard library search procedures are used to identify relevant literature. PRINCIPAL FINDINGS: Alternative policy views of excess capacity in healthcare markets rely on differing theoretical foundations. Changes in the context in which policy decisions are made over time affect the dominant theoretical framework and, therefore, the dominant policy view of excess capacity. CONCLUSIONS: In the 1990s, multiple perspectives of optimal capacity still exist. However, our evolving history suggests a set of persistent values that should guide future policy in this area. PMID:10029502

Efficient design and inference for multistage randomized trials of individualized treatment policies.

PubMed

Dawson, Ree; Lavori, Philip W

2012-01-01

Clinical demand for individualized "adaptive" treatment policies in diverse fields has spawned development of clinical trial methodology for their experimental evaluation via multistage designs, building upon methods intended for the analysis of naturalistically observed strategies. Because often there is no need to parametrically smooth multistage trial data (in contrast to observational data for adaptive strategies), it is possible to establish direct connections among different methodological approaches. We show by algebraic proof that the maximum likelihood (ML) and optimal semiparametric (SP) estimators of the population mean of the outcome of a treatment policy and its standard error are equal under certain experimental conditions. This result is used to develop a unified and efficient approach to design and inference for multistage trials of policies that adapt treatment according to discrete responses. We derive a sample size formula expressed in terms of a parametric version of the optimal SP population variance. Nonparametric (sample-based) ML estimation performed well in simulation studies, in terms of achieved power, for scenarios most likely to occur in real studies, even though sample sizes were based on the parametric formula. ML outperformed the SP estimator; differences in achieved power predominately reflected differences in their estimates of the population mean (rather than estimated standard errors). Neither methodology could mitigate the potential for overestimated sample sizes when strong nonlinearity was purposely simulated for certain discrete outcomes; however, such departures from linearity may not be an issue for many clinical contexts that make evaluation of competitive treatment policies meaningful.

Visibility monitoring in the southern California desert for the Department of Defense: Research on operations-limiting visual extinction, RESOLVE protocol

NASA Astrophysics Data System (ADS)

Blumenthal, D.; Trijonis, J.

1984-09-01

A decrease in visibility in the R2508 airspace (in the western Mojave Desert in southern California) since the mid-1940s, when flight test and training facilities were established in this region, is adversely affecting flight and test operations. The Joint Policy and Planning Board (JPPB) of the Department of Defense has initiated studies and discussions of the visibility issue with the goal of developing a management strategy to maintain and optimize the operational capabilities of the test facilities. To identify trends in and sources of visibility degradation in the desert, JPPB initiated two programs: (1) a compilation and review of the historical visibility and air quality data in the California desert region, to be coordinated by the California Desert Air Working Group (CDAWG) and funded by CDAWG participants; and (2) RESearch on Operations-Limiting Visual Extinction (RESOLVE), which involves measuring the visibility at key receptor sites (monitoring stations) in the R2508 region. The report describes the current status of and future plans for the RESOLVE program.

Comparative Logic Modeling for Policy Analysis: The Case of HIV Testing Policy Change at the Department of Veterans Affairs

PubMed Central

Langer, Erika M; Gifford, Allen L; Chan, Kee

2011-01-01

Objective Logic models have been used to evaluate policy programs, plan projects, and allocate resources. Logic Modeling for policy analysis has been used rarely in health services research but can be helpful in evaluating the content and rationale of health policies. Comparative Logic Modeling is used here on human immunodeficiency virus (HIV) policy statements from the Department of Veterans Affairs (VA) and Centers for Disease Control and Prevention (CDC). We created visual representations of proposed HIV screening policy components in order to evaluate their structural logic and research-based justifications. Data Sources and Study Design We performed content analysis of VA and CDC HIV testing policy documents in a retrospective case study. Data Collection Using comparative Logic Modeling, we examined the content and primary sources of policy statements by the VA and CDC. We then quantified evidence-based causal inferences within each statement. Principal Findings VA HIV testing policy structure largely replicated that of the CDC guidelines. Despite similar design choices, chosen research citations did not overlap. The agencies used evidence to emphasize different components of the policies. Conclusion Comparative Logic Modeling can be used by health services researchers and policy analysts more generally to evaluate structural differences in health policies and to analyze research-based rationales used by policy makers. PMID:21689094

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Stringent Mitigation Policy Implied By Temperature Impacts on Economic Growth

NASA Astrophysics Data System (ADS)

Moore, F.; Turner, D.

2014-12-01

Integrated assessment models (IAMs) compare the costs of greenhouse gas mitigation with damages from climate change in order to evaluate the social welfare implications of climate policy proposals and inform optimal emissions reduction trajectories. However, these models have been criticized for lacking a strong empirical basis for their damage functions, which do little to alter assumptions of sustained GDP growth, even under extreme temperature scenarios. We implement empirical estimates of temperature effects on GDP growth-rates in the Dynamic Integrated Climate and Economy (DICE) model via two pathways, total factor productivity (TFP) growth and capital depreciation. Even under optimistic adaptation assumptions, this damage specification implies that optimal climate policy involves the elimination of emissions in the near future, the stabilization of global temperature change below 2°C, and a social cost of carbon (SCC) an order of magnitude larger than previous estimates. A sensitivity analysis shows that the magnitude of growth effects, the rate of adaptation, and the dynamic interaction between damages from warming and GDP are three critical uncertainties and an important focus for future research.

Vendor-buyer inventory models with trade credit financing under both non-cooperative and integrated environments

NASA Astrophysics Data System (ADS)

Teng, Jinn-Tsair; Chang, Chun-Tao; Chern, Maw-Sheng

2012-11-01

Most researchers studied vendor-buyer supply chain inventory policies only from the perspective of an integrated model, which provides us the best cooperative solution. However, in reality, not many vendors and buyers are wholly integrated. Hence, it is necessary to study the optimal policies not only under an integrated environment but also under a non-cooperative environment. In this article, we develop a supply chain vendor-buyer inventory model with trade credit financing linked to order quantity. We then study the optimal policies for both the vendor and the buyer under a non-cooperative environment first, and then under a cooperative integrated situation. Further, we provide some numerical examples to illustrate the theoretical results, compare the differences between these two distinct solutions, and obtain some managerial insights. For example, in a cooperative environment, to reduce the total cost for both parties, the vendor should either provide a simple permissible delay without order quantity restriction or offer a long permissible delay linked order quantity. By contrast, in a non-cooperative environment, the vendor should provide a short permissible delay to reduce its total cost.

Impacts analysis of car following models considering variable vehicular gap policies

NASA Astrophysics Data System (ADS)

Xin, Qi; Yang, Nan; Fu, Rui; Yu, Shaowei; Shi, Zhongke

2018-07-01

Due to the important roles playing in the vehicles' adaptive cruise control system, variable vehicular gap polices were employed to full velocity difference model (FVDM) to investigate the traffic flow properties. In this paper, two new car following models were put forward by taking constant time headway(CTH) policy and variable time headway(VTH) policy into optimal velocity function, separately. By steady state analysis of the new models, an equivalent optimal velocity function was defined. To determine the linear stable conditions of the new models, we introduce equivalent expressions of safe vehicular gap, and then apply small amplitude perturbation analysis and long terms of wave expansion techniques to obtain the new models' linear stable conditions. Additionally, the first order approximate solutions of the new models were drawn at the stable region, by transforming the models into typical Burger's partial differential equations with reductive perturbation method. The FVDM based numerical simulations indicate that the variable vehicular gap polices with proper parameters directly contribute to the improvement of the traffic flows' stability and the avoidance of the unstable traffic phenomena.

An analysis of the feasibility of carbon management policies as a mechanism to influence water conservation using optimization methods.

PubMed

Wright, Andrew; Hudson, Darren

2014-10-01

Studies of how carbon reduction policies would affect agricultural production have found that there is a connection between carbon emissions and irrigation. Using county level data we develop an optimization model that accounts for the gross carbon emitted during the production process to evaluate how carbon reducing policies applied to agriculture would affect the choices of what to plant and how much to irrigate by producers on the Texas High Plains. Carbon emissions were calculated using carbon equivalent (CE) calculations developed by researchers at the University of Arkansas. Carbon reduction was achieved in the model through a constraint, a tax, or a subsidy. Reducing carbon emissions by 15% resulted in a significant reduction in the amount of water applied to a crop; however, planted acreage changed very little due to a lack of feasible alternative crops. The results show that applying carbon restrictions to agriculture may have important implications for production choices in areas that depend on groundwater resources for agricultural production. Copyright © 2014 Elsevier Ltd. All rights reserved.

Statistical methodologies for the control of dynamic remapping

NASA Technical Reports Server (NTRS)

Saltz, J. H.; Nicol, D. M.

1986-01-01

Following an initial mapping of a problem onto a multiprocessor machine or computer network, system performance often deteriorates with time. In order to maintain high performance, it may be necessary to remap the problem. The decision to remap must take into account measurements of performance deterioration, the cost of remapping, and the estimated benefits achieved by remapping. We examine the tradeoff between the costs and the benefits of remapping two qualitatively different kinds of problems. One problem assumes that performance deteriorates gradually, the other assumes that performance deteriorates suddenly. We consider a variety of policies for governing when to remap. In order to evaluate these policies, statistical models of problem behaviors are developed. Simulation results are presented which compare simple policies with computationally expensive optimal decision policies; these results demonstrate that for each problem type, the proposed simple policies are effective and robust.

Improvements of the Penalty Avoiding Rational Policy Making Algorithm and an Application to the Othello Game

NASA Astrophysics Data System (ADS)

Miyazaki, Kazuteru; Tsuboi, Sougo; Kobayashi, Shigenobu

The purpose of reinforcement learning is to learn an optimal policy in general. However, in 2-players games such as the othello game, it is important to acquire a penalty avoiding policy. In this paper, we focus on formation of a penalty avoiding policy based on the Penalty Avoiding Rational Policy Making algorithm [Miyazaki 01]. In applying it to large-scale problems, we are confronted with the curse of dimensionality. We introduce several ideas and heuristics to overcome the combinational explosion in large-scale problems. First, we propose an algorithm to save the memory by calculation of state transition. Second, we describe how to restrict exploration by two type knowledge; KIFU database and evaluation funcion. We show that our learning player can always defeat against the well-known othello game program KITTY.

Optimal harvesting of fish stocks under a time-varying discount rate.

PubMed

Duncan, Stephen; Hepburn, Cameron; Papachristodoulou, Antonis

2011-01-21

Optimal control theory has been extensively used to determine the optimal harvesting policy for renewable resources such as fish stocks. In such optimisations, it is common to maximise the discounted utility of harvesting over time, employing a constant time discount rate. However, evidence from human and animal behaviour suggests that we have evolved to employ discount rates which fall over time, often referred to as "hyperbolic discounting". This increases the weight on benefits in the distant future, which may appear to provide greater protection of resources for future generations, but also creates challenges of time-inconsistent plans. This paper examines harvesting plans when the discount rate declines over time. With a declining discount rate, the planner reduces stock levels in the early stages (when the discount rate is high) and intends to compensate by allowing the stock level to recover later (when the discount rate will be lower). Such a plan may be feasible and optimal, provided that the planner remains committed throughout. However, in practice there is a danger that such plans will be re-optimized and adjusted in the future. It is shown that repeatedly restarting the optimization can drive the stock level down to the point where the optimal policy is to harvest the stock to extinction. In short, a key contribution of this paper is to identify the surprising severity of the consequences flowing from incorporating a rather trivial, and widely prevalent, "non-rational" aspect of human behaviour into renewable resource management models. These ideas are related to the collapse of the Peruvian anchovy fishery in the 1970's. Copyright © 2010 Elsevier Ltd. All rights reserved.

Cascaded Optimization for a Persistent Data Ferrying Unmanned Aircraft

NASA Astrophysics Data System (ADS)

Carfang, Anthony

This dissertation develops and assesses a cascaded method for designing optimal periodic trajectories and link schedules for an unmanned aircraft to ferry data between stationary ground nodes. This results in a fast solution method without the need to artificially constrain system dynamics. Focusing on a fundamental ferrying problem that involves one source and one destination, but includes complex vehicle and Radio-Frequency (RF) dynamics, a cascaded structure to the system dynamics is uncovered. This structure is exploited by reformulating the nonlinear optimization problem into one that reduces the independent control to the vehicle's motion, while the link scheduling control is folded into the objective function and implemented as an optimal policy that depends on candidate motion control. This formulation is proven to maintain optimality while reducing computation time in comparison to traditional ferry optimization methods. The discrete link scheduling problem takes the form of a combinatorial optimization problem that is known to be NP-Hard. A derived necessary condition for optimality guides the development of several heuristic algorithms, specifically the Most-Data-First Algorithm and the Knapsack Adaptation. These heuristics are extended to larger ferrying scenarios, and assessed analytically and through Monte Carlo simulation, showing better throughput performance in the same order of magnitude of computation time in comparison to other common link scheduling policies. The cascaded optimization method is implemented with a novel embedded software system on a small, unmanned aircraft to validate the simulation results with field experiments. To address the sensitivity of results on trajectory tracking performance, a system that combines motion and link control with waypoint-based navigation is developed and assessed through field experiments. The data ferrying algorithms are further extended by incorporating a Gaussian process to opportunistically learn the RF environment. By continuously improving RF models, the cascaded planner can continually improve the ferrying system's overall performance.

Use of the Means/Ends Test to Evaluate Public School Dress-Code Policies

ERIC Educational Resources Information Center

Workman, Jane E.; Studak, Cathryn M.

2008-01-01

The purpose of this article is to explain how a means/ends test can be adapted for the school environment. Public school officials can use a means/ends test to document an analysis of whether dress-code policies will be effective in diminishing risks to the health, safety, or morality of the school population. Elements of policy evaluation--ends,…

Flight Test Validation of Optimal Input Design and Comparison to Conventional Inputs

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

1997-01-01

A technique for designing optimal inputs for aerodynamic parameter estimation was flight tested on the F-18 High Angle of Attack Research Vehicle (HARV). Model parameter accuracies calculated from flight test data were compared on an equal basis for optimal input designs and conventional inputs at the same flight condition. In spite of errors in the a priori input design models and distortions of the input form by the feedback control system, the optimal inputs increased estimated parameter accuracies compared to conventional 3-2-1-1 and doublet inputs. In addition, the tests using optimal input designs demonstrated enhanced design flexibility, allowing the optimal input design technique to use a larger input amplitude to achieve further increases in estimated parameter accuracy without departing from the desired flight test condition. This work validated the analysis used to develop the optimal input designs, and demonstrated the feasibility and practical utility of the optimal input design technique.

Mechanism of cross-sectoral coordination between nature protection and forestry in the Natura 2000 formulation process in Slovakia.

PubMed

Sarvašová, Zuzana; Sálka, Jaroslav; Dobšinská, Zuzana

2013-09-01

Nature protection as a policy sector is not isolated and is directly or indirectly influenced by many other sectors (e.g. forestry, water management, rural development, energy, etc.). These policy sectors are neither completely segmented nor unaffected by the decisions taken in other policy sectors. Policy formulation in nature protection is therefore also influenced by different sectors. For that reason it is inevitable to stress the need for inter-sectoral coordination to assure their policy coherence. The aim of this article is to describe the mechanism and modes of cross-sectoral coordination and to analyze the relevant actors and their interaction, using the case of the Natura 2000 formulation process in Slovakia. The European Union (EU) set up an ecological network of special protected areas, known as Natura 2000 to ensure biodiversity by conserving natural habitats and wild fauna and flora in the territory of the Member States. An optimized nature protection must therefore carefully consider existing limits and crossdisciplinary relationships at the EU, national and regional levels. The relations between forestry and biodiversity protection are analyzed using the advocacy coalition framework (ACF). The ACF is used for analyzing how two coalitions, in this case ecological and forest owners' coalitions, advocate or pursue their beliefs from the nature protection and forestry policy field. The whole process is illustrated at the regional scale on the case study of Natura 2000 sites formulation in the Slovak Republic. For better reliability and validity of research, a combination of various empiric research methods was used, supported by existing theories. So called triangulation of sociological research or triangulation of methods consists of mutual results testing of individual methodological steps through identifying corresponding political-science theories, assessing their formal points using primary and secondary document analysis and assessing their informal points with standardized interviews with experts. We can conclude that adequate cross-sectoral coordination represented by new modes is missing and the formulation of the Natura 2000 network in Slovakia shows deficits resulting from different policy beliefs concerning nature protection and forestry coalition. Copyright © 2012 Elsevier Ltd. All rights reserved.

Optimal periodic proof test based on cost-effective and reliability criteria

NASA Technical Reports Server (NTRS)

Yang, J.-N.

1976-01-01

An exploratory study for the optimization of periodic proof tests for fatigue-critical structures is presented. The optimal proof load level and the optimal number of periodic proof tests are determined by minimizing the total expected (statistical average) cost, while the constraint on the allowable level of structural reliability is satisfied. The total expected cost consists of the expected cost of proof tests, the expected cost of structures destroyed by proof tests, and the expected cost of structural failure in service. It is demonstrated by numerical examples that significant cost saving and reliability improvement for fatigue-critical structures can be achieved by the application of the optimal periodic proof test. The present study is relevant to the establishment of optimal maintenance procedures for fatigue-critical structures.

A Holistic Approach to Networked Information Systems Design and Analysis

DTIC Science & Technology

2016-04-15

attain quite substantial savings. 11. Optimal algorithms for energy harvesting in wireless networks. We use a Markov- decision-process (MDP) based...approach to obtain optimal policies for transmissions . The key advantage of our approach is that it holistically considers information and energy in a...Coding technique to minimize delays and the number of transmissions in Wireless Systems. As we approach an era of ubiquitous computing with information

Beyond the drugs: nonpharmacologic strategies to optimize procedural care in children.

PubMed

Leroy, Piet L; Costa, Luciane R; Emmanouil, Dimitris; van Beukering, Alice; Franck, Linda S

2016-03-01

Painful and/or stressful medical procedures mean a substantial burden for sick children. There is good evidence that procedural comfort can be optimized by a comprehensive comfort-directed policy containing the triad of nonpharmacological strategies (NPS) in all cases, timely or preventive procedural analgesia if pain is an issue, and procedural sedation. Based both on well-established theoretical frameworks as well as an increasing body of scientific evidence NPS need to be regarded an inextricable part of procedural comfort care. Procedural comfort care must always start with a child-friendly, nonthreatening environment in which well-being, confidence, and self-efficacy are optimized and maintained. This requires a reconsideration of the medical spaces where we provide care, reduction of sensory stimulation, normalized professional behavior, optimal logistics, and coordination and comfort-directed and age-appropriate verbal and nonverbal expression by professionals. Next, age-appropriate distraction techniques and/or hypnosis should be readily available. NPS are useful for all types of medical and dental procedures and should always precede and accompany procedural sedation. NPS should be embedded into a family-centered, care-directed policy as it has been shown that family-centered care can lead to safer, more personalized, and effective care, improved healthcare experiences and patient outcomes, and more responsive organizations.

Optimal Assembly of Psychological and Educational Tests.

ERIC Educational Resources Information Center

van der Linden, Wim J.

1998-01-01

Reviews optimal test-assembly literature and introduces the contributions to this special issue. Discusses four approaches to computerized test assembly: (1) heuristic-based test assembly; (2) 0-1 linear programming; (3) network-flow programming; and (4) an optimal design approach. Contains a bibliography of 90 sources on test assembly.…

A Real-Time Greedy-Index Dispatching Policy for using PEVs to Provide Frequency Regulation Service

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ke, Xinda; Wu, Di; Lu, Ning

This article presents a real-time greedy-index dispatching policy (GIDP) for using plug-in electric vehicles (PEVs) to provide frequency regulation services. A new service cost allocation mechanism is proposed to award PEVs based on the amount of service they provided, while considering compensations for delayed-charging and reduction of battery lifetime due to participation of the service. The GIDP transforms the optimal dispatch problem from a high-dimensional space into a one-dimensional space while preserving the solution optimality. When solving the transformed problem in real-time, the global optimality of the GIDP solution can be guaranteed by mathematically proved “indexability”. Because the GIDP indexmore » can be calculated upon the PEV’s arrival and used for the entire decision making process till its departure, the computational burden is minimized and the complexity of the aggregator dispatch process is significantly reduced. Finally, simulation results are used to evaluate the proposed GIDP, and to demonstrate the potential profitability from providing frequency regulation service by using PEVs.« less

An Alternative Approach to the Operation of Multinational Reservoir Systems: Application to the Amistad & Falcon System (Lower Rio Grande/Rí-o Bravo)

NASA Astrophysics Data System (ADS)

Serrat-Capdevila, A.; Valdes, J. B.

2005-12-01

An optimization approach for the operation of international multi-reservoir systems is presented. The approach uses Stochastic Dynamic Programming (SDP) algorithms, both steady-state and real-time, to develop two models. In the first model, the reservoirs and flows of the system are aggregated to yield an equivalent reservoir, and the obtained operating policies are disaggregated using a non-linear optimization procedure for each reservoir and for each nation water balance. In the second model a multi-reservoir approach is applied, disaggregating the releases for each country water share in each reservoir. The non-linear disaggregation algorithm uses SDP-derived operating policies as boundary conditions for a local time-step optimization. Finally, the performance of the different approaches and methods is compared. These models are applied to the Amistad-Falcon International Reservoir System as part of a binational dynamic modeling effort to develop a decision support system tool for a better management of the water resources in the Lower Rio Grande Basin, currently enduring a severe drought.

A Novel Artificial Bee Colony Approach of Live Virtual Machine Migration Policy Using Bayes Theorem

PubMed Central

Xu, Gaochao; Hu, Liang; Fu, Xiaodong

2013-01-01

Green cloud data center has become a research hotspot of virtualized cloud computing architecture. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on the VM placement selection of live migration for power saving. We present a novel heuristic approach which is called PS-ABC. Its algorithm includes two parts. One is that it combines the artificial bee colony (ABC) idea with the uniform random initialization idea, the binary search idea, and Boltzmann selection policy to achieve an improved ABC-based approach with better global exploration's ability and local exploitation's ability. The other one is that it uses the Bayes theorem to further optimize the improved ABC-based process to faster get the final optimal solution. As a result, the whole approach achieves a longer-term efficient optimization for power saving. The experimental results demonstrate that PS-ABC evidently reduces the total incremental power consumption and better protects the performance of VM running and migrating compared with the existing research. It makes the result of live VM migration more high-effective and meaningful. PMID:24385877

Optimization of Location–Routing Problem for Cold Chain Logistics Considering Carbon Footprint

PubMed Central

Wang, Songyi; Tao, Fengming; Shi, Yuhe

2018-01-01

In order to solve the optimization problem of logistics distribution system for fresh food, this paper provides a low-carbon and environmental protection point of view, based on the characteristics of perishable products, and combines with the overall optimization idea of cold chain logistics distribution network, where the green and low-carbon location–routing problem (LRP) model in cold chain logistics is developed with the minimum total costs as the objective function, which includes carbon emission costs. A hybrid genetic algorithm with heuristic rules is designed to solve the model, and an example is used to verify the effectiveness of the algorithm. Furthermore, the simulation results obtained by a practical numerical example show the applicability of the model while provide green and environmentally friendly location-distribution schemes for the cold chain logistics enterprise. Finally, carbon tax policies are introduced to analyze the impact of carbon tax on the total costs and carbon emissions, which proves that carbon tax policy can effectively reduce carbon dioxide emissions in cold chain logistics network. PMID:29316639

A novel artificial bee colony approach of live virtual machine migration policy using Bayes theorem.

PubMed

Xu, Gaochao; Ding, Yan; Zhao, Jia; Hu, Liang; Fu, Xiaodong

2013-01-01

Green cloud data center has become a research hotspot of virtualized cloud computing architecture. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on the VM placement selection of live migration for power saving. We present a novel heuristic approach which is called PS-ABC. Its algorithm includes two parts. One is that it combines the artificial bee colony (ABC) idea with the uniform random initialization idea, the binary search idea, and Boltzmann selection policy to achieve an improved ABC-based approach with better global exploration's ability and local exploitation's ability. The other one is that it uses the Bayes theorem to further optimize the improved ABC-based process to faster get the final optimal solution. As a result, the whole approach achieves a longer-term efficient optimization for power saving. The experimental results demonstrate that PS-ABC evidently reduces the total incremental power consumption and better protects the performance of VM running and migrating compared with the existing research. It makes the result of live VM migration more high-effective and meaningful.

An intelligent agent for optimal river-reservoir system management

NASA Astrophysics Data System (ADS)

Rieker, Jeffrey D.; Labadie, John W.

2012-09-01

A generalized software package is presented for developing an intelligent agent for stochastic optimization of complex river-reservoir system management and operations. Reinforcement learning is an approach to artificial intelligence for developing a decision-making agent that learns the best operational policies without the need for explicit probabilistic models of hydrologic system behavior. The agent learns these strategies experientially in a Markov decision process through observational interaction with the environment and simulation of the river-reservoir system using well-calibrated models. The graphical user interface for the reinforcement learning process controller includes numerous learning method options and dynamic displays for visualizing the adaptive behavior of the agent. As a case study, the generalized reinforcement learning software is applied to developing an intelligent agent for optimal management of water stored in the Truckee river-reservoir system of California and Nevada for the purpose of streamflow augmentation for water quality enhancement. The intelligent agent successfully learns long-term reservoir operational policies that specifically focus on mitigating water temperature extremes during persistent drought periods that jeopardize the survival of threatened and endangered fish species.

A Real-Time Greedy-Index Dispatching Policy for using PEVs to Provide Frequency Regulation Service

DOE PAGES

Ke, Xinda; Wu, Di; Lu, Ning

2017-09-18

This article presents a real-time greedy-index dispatching policy (GIDP) for using plug-in electric vehicles (PEVs) to provide frequency regulation services. A new service cost allocation mechanism is proposed to award PEVs based on the amount of service they provided, while considering compensations for delayed-charging and reduction of battery lifetime due to participation of the service. The GIDP transforms the optimal dispatch problem from a high-dimensional space into a one-dimensional space while preserving the solution optimality. When solving the transformed problem in real-time, the global optimality of the GIDP solution can be guaranteed by mathematically proved “indexability”. Because the GIDP indexmore » can be calculated upon the PEV’s arrival and used for the entire decision making process till its departure, the computational burden is minimized and the complexity of the aggregator dispatch process is significantly reduced. Finally, simulation results are used to evaluate the proposed GIDP, and to demonstrate the potential profitability from providing frequency regulation service by using PEVs.« less

Integrating Test-Form Formatting into Automated Test Assembly

ERIC Educational Resources Information Center

Diao, Qi; van der Linden, Wim J.

2013-01-01

Automated test assembly uses the methodology of mixed integer programming to select an optimal set of items from an item bank. Automated test-form generation uses the same methodology to optimally order the items and format the test form. From an optimization point of view, production of fully formatted test forms directly from the item pool using…

Optimized Non-Obstructive Particle Damping (NOPD) Treatment for Composite Honeycomb Structures

NASA Technical Reports Server (NTRS)

Panossian, H.

2008-01-01

Non-Obstructive Particle Damping (NOPD) technology is a passive vibration damping approach whereby metallic or non-metallic particles in spherical or irregular shapes, of heavy or light consistency, and even liquid particles are placed inside cavities or attached to structures by an appropriate means at strategic locations, to absorb vibration energy. The objective of the work described herein is the development of a design optimization procedure and discussion of test results for such a NOPD treatment on honeycomb (HC) composite structures, based on finite element modeling (FEM) analyses, optimization and tests. Modeling and predictions were performed and tests were carried out to correlate the test data with the FEM. The optimization procedure consisted of defining a global objective function, using finite difference methods, to determine the optimal values of the design variables through quadratic linear programming. The optimization process was carried out by targeting the highest dynamic displacements of several vibration modes of the structure and finding an optimal treatment configuration that will minimize them. An optimal design was thus derived and laboratory tests were conducted to evaluate its performance under different vibration environments. Three honeycomb composite beams, with Nomex core and aluminum face sheets, empty (untreated), uniformly treated with NOPD, and optimally treated with NOPD, according to the analytically predicted optimal design configuration, were tested in the laboratory. It is shown that the beam with optimal treatment has the lowest response amplitude. Described below are results of modal vibration tests and FEM analyses from predictions of the modal characteristics of honeycomb beams under zero, 50% uniform treatment and an optimal NOPD treatment design configuration and verification with test data.

Infinite horizon optimal impulsive control with applications to Internet congestion control

NASA Astrophysics Data System (ADS)

Avrachenkov, Konstantin; Habachi, Oussama; Piunovskiy, Alexey; Zhang, Yi

2015-04-01

We investigate infinite-horizon deterministic optimal control problems with both gradual and impulsive controls, where any finitely many impulses are allowed simultaneously. Both discounted and long-run time-average criteria are considered. We establish very general and at the same time natural conditions, under which the dynamic programming approach results in an optimal feedback policy. The established theoretical results are applied to the Internet congestion control, and by solving analytically and nontrivially the underlying optimal control problems, we obtain a simple threshold-based active queue management scheme, which takes into account the main parameters of the transmission control protocols, and improves the fairness among the connections in a given network.

Challenges in implementing the new BASHH guidelines for the management of gonorrhoea.

PubMed

Rodgers, S; Murgatroyd, M; Perez, K; Kingston, M; Lee, V

2014-02-01

Neisseria gonorrhoeae has progressively developed reduced sensitivity to different classes of antibiotics. The British Association for Sexual Health and HIV (BASHH) updated guidelines for the diagnosis and management of gonorrhoea in 2011. New recommendations include an increased dose of ceftriaxone with adjuvant use of azithromycin, as well as test of cure (TOC) in all cases. We present an audit of adherence to new antibiotic prescribing guidelines as well as TOC uptake in an inner city genitourinary medicine clinic. Among the 271 (242 male, 29 female) patients included, 96% (n = 260) received the new first-line treatment. Test of cure uptake was found to be suboptimal at 55% (n = 149) with the majority (67%) of these taking place within 20 days of treatment. The new first-line treatment for gonorrhoea is feasible and generally accepted by patients. However the TOC uptake is low, emphasising the need for robust follow-up and recall policies. Further study is required into the optimal timing for TOC.

Optimal operating rules definition in complex water resource systems combining fuzzy logic, expert criteria and stochastic programming

NASA Astrophysics Data System (ADS)

Macian-Sorribes, Hector; Pulido-Velazquez, Manuel

2016-04-01

This contribution presents a methodology for defining optimal seasonal operating rules in multireservoir systems coupling expert criteria and stochastic optimization. Both sources of information are combined using fuzzy logic. The structure of the operating rules is defined based on expert criteria, via a joint expert-technician framework consisting in a series of meetings, workshops and surveys carried out between reservoir managers and modelers. As a result, the decision-making process used by managers can be assessed and expressed using fuzzy logic: fuzzy rule-based systems are employed to represent the operating rules and fuzzy regression procedures are used for forecasting future inflows. Once done that, a stochastic optimization algorithm can be used to define optimal decisions and transform them into fuzzy rules. Finally, the optimal fuzzy rules and the inflow prediction scheme are combined into a Decision Support System for making seasonal forecasts and simulate the effect of different alternatives in response to the initial system state and the foreseen inflows. The approach presented has been applied to the Jucar River Basin (Spain). Reservoir managers explained how the system is operated, taking into account the reservoirs' states at the beginning of the irrigation season and the inflows previewed during that season. According to the information given by them, the Jucar River Basin operating policies were expressed via two fuzzy rule-based (FRB) systems that estimate the amount of water to be allocated to the users and how the reservoir storages should be balanced to guarantee those deliveries. A stochastic optimization model using Stochastic Dual Dynamic Programming (SDDP) was developed to define optimal decisions, which are transformed into optimal operating rules embedding them into the two FRBs previously created. As a benchmark, historical records are used to develop alternative operating rules. A fuzzy linear regression procedure was employed to foresee future inflows depending on present and past hydrological and meteorological variables actually used by the reservoir managers to define likely inflow scenarios. A Decision Support System (DSS) was created coupling the FRB systems and the inflow prediction scheme in order to give the user a set of possible optimal releases in response to the reservoir states at the beginning of the irrigation season and the fuzzy inflow projections made using hydrological and meteorological information. The results show that the optimal DSS created using the FRB operating policies are able to increase the amount of water allocated to the users in 20 to 50 Mm3 per irrigation season with respect to the current policies. Consequently, the mechanism used to define optimal operating rules and transform them into a DSS is able to increase the water deliveries in the Jucar River Basin, combining expert criteria and optimization algorithms in an efficient way. This study has been partially supported by the IMPADAPT project (CGL2013-48424-C2-1-R) with Spanish MINECO (Ministerio de Economía y Competitividad) and FEDER funds. It also has received funding from the European Union's Horizon 2020 research and innovation programme under the IMPREX project (grant agreement no: 641.811).

Can trained lay providers perform HIV testing services? A review of national HIV testing policies.

PubMed

Flynn, David E; Johnson, Cheryl; Sands, Anita; Wong, Vincent; Figueroa, Carmen; Baggaley, Rachel

2017-01-04

Only an estimated 54% of people living with HIV are aware of their status. Despite progress scaling up HIV testing services (HTS), a testing gap remains. Delivery of HTS by lay providers may help close this testing gap, while also increasing uptake and acceptability of HIV testing among key populations and other priority groups. 50 National HIV testing policies were collated from WHO country intelligence databases, contacts and testing program websites. Data regarding lay provider use for HTS was extracted and collated. Our search had no geographical or language restrictions. This data was then compared with reported data from the Global AIDS Response Progress Reporting (GARPR) from July 2015. Forty-two percent of countries permit lay providers to perform HIV testing and 56% permit lay providers to administer pre-and post-test counseling. Comparative analysis with GARPR found that less than half (46%) of reported data from countries were consistent with their corresponding national HIV testing policy. Given the low uptake of lay provider use globally and their proven use in increasing HIV testing, countries should consider revising policies to support lay provider testing using rapid diagnostic tests.

77 FR 73286 - Codification of Animal Testing Policy

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-10

... to post the test method on the animal testing Web site. In the final statement of policy, we refer to... case-by-case basis and, upon review, determine whether to post the test method on the animal testing... on a case-by- case basis and, upon review, determine whether to post the test method on the animal...

Ethics, policy, and educational issues in genetic testing.

PubMed

Williams, Janet K; Skirton, Heather; Masny, Agnes

2006-01-01

Analyze ethics, public policy, and education issues that arise in the United States (US) and the United Kingdom (UK) when genomic information acquired as a result of genetic testing is introduced into healthcare services. Priorities in the Ethical, Legal, and Social Issues Research Program include privacy, integration of genetic services into clinical health care, and educational preparation of the nursing workforce. These constructs are used to examine health policies in the US and UK, and professional interactions of individuals and families with healthcare providers. Individual, family, and societal goals may conflict with current healthcare practices and policies when genetic testing is done. Current health policies do not fully address these concerns. Unresolved issues include protection of privacy of individuals while considering genetic information needs of family members, determination of appropriate monitoring of genetic tests, addressing genetic healthcare discrepancies, and assuring appropriate nursing workforce preparation. Introduction of genetic testing into health care requires that providers are knowledgeable regarding ethical, policy, and practice issues in order to minimize risk for harm, protect the rights of individuals and families, and consider societal context in the management of genetic test results. Understanding of these issues is a component of genetic nursing competency that must be addressed at all levels of nursing education.

Policies, Procedures, and Practices Regarding Sport-Related Concussion in Community College Athletes.

PubMed

Paddack, Michael; DeWolf, Ryan; Covassin, Tracey; Kontos, Anthony

2016-01-01

College sport organizations and associations endorse concussion-management protocols and policies. To date, little information is available on concussion policies and practices at community college institutions. To assess and describe current practices and policies regarding the assessment, management, and return-to-play criteria for sport-related concussion (SRC) among member institutions of the California Community College Athletic Association (CCCAA). Cross-sectional study. Web-based survey. A total of 55 head athletic trainers (ATs) at CCCAA institutions. Data about policies, procedures, and practices regarding SRC were collected over a 3-week period in March 2012 and analyzed using descriptive statistics, the Fisher exact test, and the Spearman test. Almost half (47%) of ATs stated they had a policy for SRC assessment, management, and return to play at their institution. They reported being in compliance with baseline testing guidelines (25%), management guidelines (34.5%), and return-to-play guidelines (30%). Nearly 31% of ATs described having an SRC policy in place for academic accommodations. Conference attendance was positively correlated with institutional use of academic accommodations after SRC (r = 0.44, P = .01). The number of meetings ATs attended and their use of baseline testing were also positively correlated (r = 0.38, P = .01). At the time of this study, nearly half of CCCAA institutions had concussion policies and 31% had academic-accommodation policies. However, only 18% of ATs at CCCAA institutions were in compliance with all of their concussion policies. Our findings demonstrate improvements in the management of SRCs by ATs at California community colleges compared with previous research but a need for better compliance with SRC policies.

Drug Testing in Schools: Implications for Policy.

ERIC Educational Resources Information Center

Bozeman, William C.; And Others

1987-01-01

Public concern about substance abuse, fueled by political and media attention, is causing school administrators to consider a variety of approaches beyond traditional drug education. No procedures, methods, or rules regarding drug testing should be established in the absence of clear school board policy, and no policy decisions should be made…

Policy Borrowing, Policy Learning: Testing Times in Australian Schooling

ERIC Educational Resources Information Center

Lingard, Bob

2010-01-01

This paper provides a contextualised and critical policy analysis of the Rudd government's national schooling agenda in Australia. The specific focus is on the introduction of national literacy and numeracy testing and the recent creation by the Australian Curriculum, Assessment and Reporting Authority of the website "My School", which…

Execution of Multidisciplinary Design Optimization Approaches on Common Test Problems

NASA Technical Reports Server (NTRS)

Balling, R. J.; Wilkinson, C. A.

1997-01-01

A class of synthetic problems for testing multidisciplinary design optimization (MDO) approaches is presented. These test problems are easy to reproduce because all functions are given as closed-form mathematical expressions. They are constructed in such a way that the optimal value of all variables and the objective is unity. The test problems involve three disciplines and allow the user to specify the number of design variables, state variables, coupling functions, design constraints, controlling design constraints, and the strength of coupling. Several MDO approaches were executed on two sample synthetic test problems. These approaches included single-level optimization approaches, collaborative optimization approaches, and concurrent subspace optimization approaches. Execution results are presented, and the robustness and efficiency of these approaches an evaluated for these sample problems.

Measurement and Testing Considerations for Native American Education.

ERIC Educational Resources Information Center

Blanchard, Joseph D.

Some of the general considerations and requirements for the establishment of a testing policy and procedures for a large education system are presented. Testing policy is considered necessary to establish a common understanding of the position of testing in the education program, the facilitation of the use of tests for academic gain, and to…

Vertical integration and optimal reimbursement policy.

PubMed

Afendulis, Christopher C; Kessler, Daniel P

2011-09-01

Health care providers may vertically integrate not only to facilitate coordination of care, but also for strategic reasons that may not be in patients' best interests. Optimal Medicare reimbursement policy depends upon the extent to which each of these explanations is correct. To investigate, we compare the consequences of the 1997 adoption of prospective payment for skilled nursing facilities (SNF PPS) in geographic areas with high versus low levels of hospital/SNF integration. We find that SNF PPS decreased spending more in high integration areas, with no measurable consequences for patient health outcomes. Our findings suggest that integrated providers should face higher-powered reimbursement incentives, i.e., less cost-sharing. More generally, we conclude that purchasers of health services (and other services subject to agency problems) should consider the organizational form of their suppliers when choosing a reimbursement mechanism.

Vertical integration and optimal reimbursement policy

PubMed Central

Afendulis, Christopher C.

2011-01-01

Health care providers may vertically integrate not only to facilitate coordination of care, but also for strategic reasons that may not be in patients’ best interests. Optimal Medicare reimbursement policy depends upon the extent to which each of these explanations is correct. To investigate, we compare the consequences of the 1997 adoption of prospective payment for skilled nursing facilities (SNF PPS) in geographic areas with high versus low levels of hospital/SNF integration. We find that SNF PPS decreased spending more in high integration areas, with no measurable consequences for patient health outcomes. Our findings suggest that integrated providers should face higher-powered reimbursement incentives, i.e., less cost-sharing. More generally, we conclude that purchasers of health services (and other services subject to agency problems) should consider the organizational form of their suppliers when choosing a reimbursement mechanism. PMID:21850551

Green roof adoption in atlanta, georgia: the effects of building characteristics and subsidies on net private, public, and social benefits.

PubMed

Mullen, Jeffrey D; Lamsal, Madhur; Colson, Greg

2013-10-01

This research draws on and expands previous studies that have quantified the costs and benefits associated with conventional roofs versus green roofs. Using parameters from those studies to define alternative scenarios, we estimate from a private, public, and social perspective the costs and benefits of installing and maintaining an extensive green roof in Atlanta, GA. Results indicate net private benefits are a decreasing function of roof size and vary considerably across scenarios. In contrast, net public benefits are highly stable across scenarios, ranging from $32.49 to $32.90 m(-2). In addition, we evaluate two alternative subsidy regimes: (i) a general subsidy provided to every building that adopts a green roof and (ii) a targeted subsidy provided only to buildings for which net private benefits are negative but net public benefits are positive. In 6 of the 12 general subsidy scenarios the optimal public policy is not to offer a subsidy; in 5 scenarios the optimal subsidy rate is between $20 and $27 m(-2); and in 1 scenario the optimal rate is $5 m(-2). The optimal rate with a targeted subsidy is between $20 and $27 m(-2) in 11 scenarios and no subsidy is optimal in the twelfth. In most scenarios, a significant portion of net public benefits are generated by buildings for which net private benefits are positive. This suggests a policy focused on information dissemination and technical assistance may be more cost-effective than direct subsidy payments.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deshmukh, Ranjit; Bharvirkar, Ranjit; Gambhir, Ashwin

Although solar costs are dropping rapidly, solar power is still more expensive than conventional and other renewable energy options. The solar sector still needs continuing government policy support. These policies are driven by objectives that go beyond the goal of achieving grid parity. The need to achieve multiple objectives and ensure sufficient political support for solar power makes it diffi cult for policy makers to design the optimal solar power policy. The dynamic and uncertain nature of the solar industry, combined with the constraints offered by broader economic, political and social conditions further complicates the task of policy making. Thismore » report presents an analysis of solar promotion policies in seven countries - Germany, Spain, the United States, Japan, China, Taiwan, and India - in terms of their outlook, objectives, policy mechanisms and outcomes. The report presents key insights, primarily in qualitative terms, and recommendations for two distinct audiences. The first audience consists of global policy makers who are exploring various mechanisms to increase the penetration of solar power in markets to mitigate climate change. The second audience consists of key Indian policy makers who are developing a long-term implementation plan under the Jawaharlal Nehru National Solar Mission and various state initiatives.« less

Convexity of Ruin Probability and Optimal Dividend Strategies for a General Lévy Process

PubMed Central

Yuen, Kam Chuen; Shen, Ying

2015-01-01

We consider the optimal dividends problem for a company whose cash reserves follow a general Lévy process with certain positive jumps and arbitrary negative jumps. The objective is to find a policy which maximizes the expected discounted dividends until the time of ruin. Under appropriate conditions, we use some recent results in the theory of potential analysis of subordinators to obtain the convexity properties of probability of ruin. We present conditions under which the optimal dividend strategy, among all admissible ones, takes the form of a barrier strategy. PMID:26351655

Approximate dynamic programming for optimal stationary control with control-dependent noise.

PubMed

Jiang, Yu; Jiang, Zhong-Ping

2011-12-01

This brief studies the stochastic optimal control problem via reinforcement learning and approximate/adaptive dynamic programming (ADP). A policy iteration algorithm is derived in the presence of both additive and multiplicative noise using Itô calculus. The expectation of the approximated cost matrix is guaranteed to converge to the solution of some algebraic Riccati equation that gives rise to the optimal cost value. Moreover, the covariance of the approximated cost matrix can be reduced by increasing the length of time interval between two consecutive iterations. Finally, a numerical example is given to illustrate the efficiency of the proposed ADP methodology.

Applying reinforcement learning techniques to detect hepatocellular carcinoma under limited screening capacity.

PubMed

Lee, Elliot; Lavieri, Mariel S; Volk, Michael L; Xu, Yongcai

2015-09-01

We investigate the problem faced by a healthcare system wishing to allocate its constrained screening resources across a population at risk for developing a disease. A patient's risk of developing the disease depends on his/her biomedical dynamics. However, knowledge of these dynamics must be learned by the system over time. Three classes of reinforcement learning policies are designed to address this problem of simultaneously gathering and utilizing information across multiple patients. We investigate a case study based upon the screening for Hepatocellular Carcinoma (HCC), and optimize each of the three classes of policies using the indifference zone method. A simulation is built to gauge the performance of these policies, and their performance is compared to current practice. We then demonstrate how the benefits of learning-based screening policies differ across various levels of resource scarcity and provide metrics of policy performance.

PREEMPTIVE PHARMACOGENETIC TESTING: EXPLORING THE KNOWLEDGE AND PERSPECTIVES OF UNITED STATES PAYERS

PubMed Central

Keeling, Nicholas J.; Rosenthal, Meagen M.; West-Strum, Donna; Patel, Amit; Haidar, Cyrine E.; Hoffman, James M.

2017-01-01

PURPOSE Preemptive pharmacogenetic testing aims to optimize medication use by having genetic information at the point of prescribing. Payers’ decisions influence implementation of this technology. We investigated U.S. payers’ knowledge, awareness, and perspectives on preemptive pharmacogenetic testing. METHODS A qualitative study was conducted using semi-structured interviews. Participants were screened for eligibility through an online survey. A blended inductive and deductive approach was used to analyze the transcripts. Two authors conducted an iterative reading process to code and categorize the data. RESULTS Medical or pharmacy directors from 14 payer organizations covering 122 million U.S. lives were interviewed. Three concept domains and ten dimensions were developed. Key findings include: clinical utility concerns and limited exposure to preemptive germline testing, continued preference for outcomes from randomized controlled trials, interest in guideline development, importance of demonstrating an impact on clinical decision making, concerns of downstream costs and benefit predictability, and the impact of public stakeholders such as the FDA and CMS. CONCLUSION Both barriers and potential facilitators exist to developing cohesive reimbursement policy for pharmacogenetics, and there are unique challenges for the preemptive testing model. Prospective outcome studies, more precisely defining target populations, and predictive economic models are important considerations for future research. PMID:29261180

New and improved tuberculosis diagnostics: evidence, policy, practice, and impact.

PubMed

Pai, Madhukar; Minion, Jessica; Steingart, Karen; Ramsay, Andrew

2010-05-01

The aim is to summarize the evidence base for tuberculosis (TB) diagnostics, review recent policies on TB diagnostics, and discuss issues such as how evidence is translated into policy, limitations of the existing evidence base, and challenges involved in translating policies into impact. Case detection continues to be a major obstacle to global TB control. Fortunately, due to an unprecedented level of interest, funding, and activity, the new diagnostics pipeline for TB has rapidly expanded. There have been several new policies and guidelines on TB diagnostics. However, there are major gaps in the existing pipeline (e.g. lack of a point-of-care test) and the evidence base is predominantly made up of research studies of test accuracy. With the availability of new diagnostics and supporting policies, the next major step is translation of policy into practice. The impact of new tests will depend largely on the extent of their introduction and acceptance into the global public sector. This will itself depend in part on policy decisions by international technical agencies and national TB programs. With the engagement of all key stakeholders, we will need to translate evidence-based policies into epidemiological and public health impact.

A mobile asset sharing policy for hospitals with real time locating systems.

PubMed

Demircan-Yıldız, Ece Arzu; Fescioglu-Unver, Nilgun

2016-01-01

Each year, hospitals lose a considerable amount of time and money due to misplaced mobile assets. In addition the assets which remain in departments that frequently use them depreciate early, while other assets of the same type in different departments are rarely used. A real time locating system can prevent these losses when used with appropriate asset sharing policies. This research quantifies the amount of time a medium size hospital saves by using real time locating system and proposes an asset selection rule to eliminate the asset usage imbalance problem. The asset selection rule proposed is based on multi objective optimization techniques. The effectiveness of this rule on asset to patient time and asset utilization rate variance performance measures were tested using discrete event simulation method. Results show that the proposed asset selection rule improved the usage balance significantly. Sensitivity analysis showed that the proposed rule is robust to changes in demand rates and user preferences. Real time locating systems enable saving considerable amount of time in hospitals, and they can still be improved by integrating decision support mechanisms. Combining tracking technology and asset selection rules helps improve healthcare services.

Measuring the effect of food stamps on food insecurity and hunger: research and policy considerations.

PubMed

Wilde, Parke E

2007-02-01

The federal government has estimated the prevalence of household "food insecurity" and "food insecurity with hunger" since 1995. Early observers believed that the new measure could be used to assess and improve the Food Stamp Program (FSP). Ten years of research have tempered the initial optimism. The prevalence of food insecurity with hunger (12.3% of all low-income households in 2004) is much higher among food stamp participant households (18.6% in 2004) than among low-income nonparticipant households (10.1% in 2004), due to strong self-selection effects. Households facing greater hardship are more likely to join the program. This article reviews 6 types of nonexperimental research designs that have been used to address the self-selection problem. The results have been inconclusive and the authors have warned against drawing causal inferences from their research. Ethical random-assignment research designs may be required to satisfy the intense policy interest in measuring the antihunger impact of the FSP. The most promising ethical research designs would test the effects of offering eligibility to households that are currently ineligible or offering increased benefits to households that are currently eligible for small benefit amounts.

Incentives and intrinsic motivation in healthcare.

PubMed

Berdud, Mikel; Cabasés, Juan M; Nieto, Jorge

It has been established in the literature that workers within public organisations are intrinsically motivated. This paper is an empirical study of the healthcare sector using methods of qualitative analysis research, which aims to answer the following hypotheses: 1) doctors are intrinsically motivated; 2) economic incentives and control policies may undermine doctors' intrinsic motivation; and 3) well-designed incentives may encourage doctors' intrinsic motivation. We conducted semi-structured interviews à-la-Bewley with 16 doctors from Navarre's Healthcare Service (Servicio Navarro de Salud-Osasunbidea), Spain. The questions were based on current theories of intrinsic motivation and incentives to test the hypotheses. Interviewees were allowed to respond openly without time constraints. Relevant information was selected, quantified and analysed by using the qualitative concepts of saturation and codification. The results seem to confirm the hypotheses. Evidence supporting hypotheses 1 and 2 was gathered from all interviewees, as well as indications of the validity of hypothesis 3 based on interviewees' proposals of incentives. The conclusions could act as a guide to support the optimal design of incentive policies and schemes within health organisations when healthcare professionals are intrinsically motivated. Copyright © 2016 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

Modeling of policies for reduction of GHG emissions in energy sector using ANN: case study-Croatia (EU).

PubMed

Bolanča, Tomislav; Strahovnik, Tomislav; Ukić, Šime; Stankov, Mirjana Novak; Rogošić, Marko

2017-07-01

This study describes the development of tool for testing different policies for reduction of greenhouse gas (GHG) emissions in energy sector using artificial neural networks (ANNs). The case study of Croatia was elaborated. Two different energy consumption scenarios were used as a base for calculations and predictions of GHG emissions: the business as usual (BAU) scenario and sustainable scenario. Both of them are based on predicted energy consumption using different growth rates; the growth rates within the second scenario resulted from the implementation of corresponding energy efficiency measures in final energy consumption and increasing share of renewable energy sources. Both ANN architecture and training methodology were optimized to produce network that was able to successfully describe the existing data and to achieve reliable prediction of emissions in a forward time sense. The BAU scenario was found to produce continuously increasing emissions of all GHGs. The sustainable scenario was found to decrease the GHG emission levels of all gases with respect to BAU. The observed decrease was attributed to the group of measures termed the reduction of final energy consumption through energy efficiency measures.

Analysis of production-inventory decisions in a decentralized supply chain with price-dependent demand

NASA Astrophysics Data System (ADS)

Kurdhi, N. A.; Irsanianto, S. T.; Sutanto

2017-01-01

In this paper, we consider a production-inventory supply chain system with single-manufacturer and single-retailer. There are many types of contract that guarantee the supply chain. However, the administrative costs of the contract are usually neglected in real situation. The additional gain from integration may not cover the extra administrative costs may not addressed to supply chain. Therefore, a Stackelberg game and RFM policy are examined in order to investigate its performance on supply chain. The RFM policy is applied because its administrative costs are lower than othe policies. Although RFM policy is not capable of coordinating the channel, it leads to considerable improvements over the channel. The purpose of this research is to present a model of integrated policy, in which the goal is to maximize the whole system profit, and to evaluate decentralized-Stackelberg and RFM policies, in which individual firms in the supply chain have their own objectives and decisions to optimize.

EEC energy costs seen higher than U. S. [Need for policy imperative

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zahn, P.

1977-02-21

Europe's dependence on imported oil, depletion of North Sea oil after 1990, and the relation of economic growth to energy costs combine to make a comprehensive energy policy for Europe imperative. Dr. Guido Brunner, the new commissioner of energy affairs for the Common Market (EEC) sees a widening gap in energy costs compared to all major industrial competitors except Japan unless a policy is developed. Present Common Market policies, which rely on individual countries to pursue programs of conserving, storing, and converting fuels, suffer from differing political and economic interpretations of the energy problem. There is no unified policy tomore » reduce dependence on imports and achieve the goal of producing 13 percent of electricity needs by nuclear energy in 1985. Brunner's appointment was generally well received because of his liberal views and low profile, but there is little optimism that he will be able to accomplish his goal of a comprehensive EEC policy. (DCK)« less

Sex Bias in Testing: A Review with Policy Recommendations.

ERIC Educational Resources Information Center

Tittle, Carol Kehr

Educational achievement tests, career interest inventories, and aptitude tests are reviewed for examples of sex bias, and changes in policy concerning the use of these tests are suggested. These suggestions are within the authority and responsibility of local and state educational administrators, teachers, counselors, parents, and students. The…

The British Association for Sexual Health and HIV 2016 UK national audit and survey of clinic policies in relation to risk assessment, HIV testing and follow-up.

PubMed

Bhaduri, S; Curtis, H; McClean, H; Sullivan, A K

2018-01-01

This national audit of 142 clinics demonstrated that the majority of clinics surveyed had policies and agreed clinical practice for alcohol and recreational drug enquiry, as well as documentation of HIV test refusal, although this was not the case in 24% of clinics as regards alcohol usage, 21% of clinics as regards recreational drugs use and 43% of clinics as regards chemsex usage. Regarding management of HIV test refusal, there was no policy or agreed practice in 13% of clinics with respect to men having sex with men (MSM) attenders, and in 18% of clinics for heterosexual attenders. Seventy percent of clinics had HIV point of care tests (POCT) available. Recommendations include: all clinics should have a policy of routine enquiry about alcohol, recreational drugs and chemsex, all clinics should record reasons for HIV test refusal and all clinics should provide testing alternatives to improve uptake, e.g. point of care testing or home sampling.

A study of the influence of regional environmental expenditure on air quality in China: the effectiveness of environmental policy.

PubMed

He, Lingyun; Wu, Meng; Wang, Deqing; Zhong, Zhangqi

2018-03-01

Based on the panel data model, data on environmental expenditures, the air quality index, economic aggregates, industrial structures, etc., of seven seriously polluted cities in China, from the period 2007-2015, were collected, and this paper estimates the general relationship between environmental expenditures and the air quality index. Besides, the impact of the fuel tax policy on air quality as well as on the relationship between environmental expenditure and the air quality index is tested using the method of regression discontinuity. We find that there is a long-term equilibrium relationship between environmental expenditure and air quality index as well as a 0.0507% positive effect of the former on the latter. Second, for Beijing, Taiyuan, Chongqing, and Lanzhou, a 1% increase in environmental expenditure leads to 0.0773, 0.0125, 0.0965, and 0.0912% decreases in the air quality index, respectively; however, for Shijiazhuang, Ji'nan, and Urumqi, effect of environmental expenditure on air quality is insignificant. Third, both economic growth and optimization of the industrial structure can lead to an improvement of air quality. Fourth, since the implementation of the fuel tax policy in 2009, the air quality of the sample cities has improved, and the pulling effect of environmental expenditure on the air quality index has decreased from 0.0507 to 0.0048%. Our findings cannot only clarify the effect of environmental expenditures on air quality but can also objectively judge the effectiveness of environmental policies of China to a certain extent. It may benefit Chinese government to effectively govern air pollution with fiscal tools in conjunction with economic and environmental characteristics.

Increasing opportunities for the productive engagement of older adults: a response to population aging.

PubMed

Gonzales, Ernest; Matz-Costa, Christina; Morrow-Howell, Nancy

2015-04-01

"Productive aging" puts forward the fundamental view that the capacity of older adults must be better developed and utilized in activities that make economic contributions to society-working, caregiving, volunteering. It is suggested that productive engagement can lead to multiple positive ends: offsetting fiscal strains of a larger older population, contributing to the betterment of families and civil society, and maintaining the health and economic security of older adults. Advocates claim that outdated social structures and discriminatory behaviors limit participation of older adults in these important social roles as well as prevent the optimization of outcomes for older adults, families, and society. We ask two important questions: (a) How can we shape policies and programs to optimally engage the growing resources of an aging population for the sake of society and older adults themselves? and (b) How can policies pertaining to productive engagement reduce health and economic disparities? We answer these questions by first describing the current state of engagement in each of the three productive activities and summarize some current policies and programs that affect engagement. Next we highlight challenges that cross-cut productive engagement. Finally, we provide policy recommendations to address these challenges. © The Author 2015. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Economically and environmentally informed policy for road resurfacing: tradeoffs between costs and greenhouse gas emissions

NASA Astrophysics Data System (ADS)

Reger, Darren; Madanat, Samer; Horvath, Arpad

2014-10-01

As road conditions worsen, users experience an increase in fuel consumption and vehicle wear and tear. This increases the costs incurred by the drivers, and also increases the amount of greenhouse gases (GHGs) that vehicles emit. Pavement condition can be improved through rehabilitation activities (resurfacing) to reduce the effects on users, but these activities also have significant cost and GHG emission impacts. The objective of pavement management is to minimize total societal (user and agency) costs. However, the environmental impacts associated with the cost-minimizing policy are not currently accounted for. We show that there exists a range of potentially optimal decisions, known as the Pareto frontier, in which it is not possible to decrease total emissions without increasing total costs and vice versa. This research explores these tradeoffs for a system of pavement segments. For a case study, a network was created from a subset of California’s highways using available traffic data. It was shown that the current resurfacing strategy used by the state’s transportation agency, Caltrans, does not fall on the Pareto frontier, meaning that significant savings in both total costs and total emissions can be achieved by switching to one of the optimal policies. The methods presented in this paper also allow the decision maker to evaluate the impact of other policies, such as reduced vehicle kilometers traveled or better construction standards.

Coastal and river flood risk analyses for guiding economically optimal flood adaptation policies: a country-scale study for Mexico

NASA Astrophysics Data System (ADS)

Haer, Toon; Botzen, W. J. Wouter; van Roomen, Vincent; Connor, Harry; Zavala-Hidalgo, Jorge; Eilander, Dirk M.; Ward, Philip J.

2018-06-01

Many countries around the world face increasing impacts from flooding due to socio-economic development in flood-prone areas, which may be enhanced in intensity and frequency as a result of climate change. With increasing flood risk, it is becoming more important to be able to assess the costs and benefits of adaptation strategies. To guide the design of such strategies, policy makers need tools to prioritize where adaptation is needed and how much adaptation funds are required. In this country-scale study, we show how flood risk analyses can be used in cost-benefit analyses to prioritize investments in flood adaptation strategies in Mexico under future climate scenarios. Moreover, given the often limited availability of detailed local data for such analyses, we show how state-of-the-art global data and flood risk assessment models can be applied for a detailed assessment of optimal flood-protection strategies. Our results show that especially states along the Gulf of Mexico have considerable economic benefits from investments in adaptation that limit risks from both river and coastal floods, and that increased flood-protection standards are economically beneficial for many Mexican states. We discuss the sensitivity of our results to modelling uncertainties, the transferability of our modelling approach and policy implications. This article is part of the theme issue `Advances in risk assessment for climate change adaptation policy'.

An Optimization-Based Approach to Determine Requirements and Aircraft Design under Multi-domain Uncertainties

NASA Astrophysics Data System (ADS)

Govindaraju, Parithi

Determining the optimal requirements for and design variable values of new systems, which operate along with existing systems to provide a set of overarching capabilities, as a single task is challenging due to the highly interconnected effects that setting requirements on a new system's design can have on how an operator uses this newly designed system. This task of determining the requirements and the design variable values becomes even more difficult because of the presence of uncertainties in the new system design and in the operational environment. This research proposed and investigated aspects of a framework that generates optimum design requirements of new, yet-to-be-designed systems that, when operating alongside other systems, will optimize fleet-level objectives while considering the effects of various uncertainties. Specifically, this research effort addresses the issues of uncertainty in the design of the new system through reliability-based design optimization methods, and uncertainty in the operations of the fleet through descriptive sampling methods and robust optimization formulations. In this context, fleet-level performance metrics result from using the new system alongside other systems to accomplish an overarching objective or mission. This approach treats the design requirements of a new system as decision variables in an optimization problem formulation that a user in the position of making an acquisition decision could solve. This solution would indicate the best new system requirements-and an associated description of the best possible design variable variables for that new system-to optimize the fleet level performance metric(s). Using a problem motivated by recorded operations of the United States Air Force Air Mobility Command for illustration, the approach is demonstrated first for a simplified problem that only considers demand uncertainties in the service network and the proposed methodology is used to identify the optimal design requirements and optimal aircraft sizing variables of new, yet-to-be-introduced aircraft. With this new aircraft serving alongside other existing aircraft, the fleet of aircraft satisfy the desired demand for cargo transportation, while maximizing fleet productivity and minimizing fuel consumption via a multi-objective problem formulation. The approach is then extended to handle uncertainties in both the design of the new system and in the operations of the fleet. The propagation of uncertainties associated with the conceptual design of the new aircraft to the uncertainties associated with the subsequent operations of the new and existing aircraft in the fleet presents some unique challenges. A computationally tractable hybrid robust counterpart formulation efficiently handles the confluence of the two types of domain-specific uncertainties. This hybrid formulation is tested on a larger route network problem to demonstrate the scalability of the approach. Following the presentation of the results obtained, a summary discussion indicates how decision-makers might use these results to set requirements for new aircraft that meet operational needs while balancing the environmental impact of the fleet with fleet-level performance. Comparing the solutions from the uncertainty-based and deterministic formulations via a posteriori analysis demonstrates the efficacy of the robust and reliability-based optimization formulations in addressing the different domain-specific uncertainties. Results suggest that the aircraft design requirements and design description determined through the hybrid robust counterpart formulation approach differ from solutions obtained from the simplistic deterministic approach, and leads to greater fleet-level fuel savings, when subjected to real-world uncertain scenarios (more robust to uncertainty). The research, though applied to a specific air cargo application, is technically agnostic in nature and can be applied to other facets of policy and acquisition management, to explore capability trade spaces for different vehicle systems, mitigate risks, define policy and potentially generate better returns on investment. Other domains relevant to policy and acquisition decisions could utilize the problem formulation and solution approach proposed in this dissertation provided that the problem can be split into a non-linear programming problem to describe the new system sizing and the fleet operations problem can be posed as a linear/integer programming problem.