Sample records for mixed-integer dynamic optimization

  1. Solving mixed integer nonlinear programming problems using spiral dynamics optimization algorithm

    NASA Astrophysics Data System (ADS)

    Kania, Adhe; Sidarto, Kuntjoro Adji

    2016-02-01

    Many engineering and practical problem can be modeled by mixed integer nonlinear programming. This paper proposes to solve the problem with modified spiral dynamics inspired optimization method of Tamura and Yasuda. Four test cases have been examined, including problem in engineering and sport. This method succeeds in obtaining the optimal result in all test cases.

  2. Mixed integer simulation optimization for optimal hydraulic fracturing and production of shale gas fields

    NASA Astrophysics Data System (ADS)

    Li, J. C.; Gong, B.; Wang, H. G.

    2016-08-01

    Optimal development of shale gas fields involves designing a most productive fracturing network for hydraulic stimulation processes and operating wells appropriately throughout the production time. A hydraulic fracturing network design-determining well placement, number of fracturing stages, and fracture lengths-is defined by specifying a set of integer ordered blocks to drill wells and create fractures in a discrete shale gas reservoir model. The well control variables such as bottom hole pressures or production rates for well operations are real valued. Shale gas development problems, therefore, can be mathematically formulated with mixed-integer optimization models. A shale gas reservoir simulator is used to evaluate the production performance for a hydraulic fracturing and well control plan. To find the optimal fracturing design and well operation is challenging because the problem is a mixed integer optimization problem and entails computationally expensive reservoir simulation. A dynamic simplex interpolation-based alternate subspace (DSIAS) search method is applied for mixed integer optimization problems associated with shale gas development projects. The optimization performance is demonstrated with the example case of the development of the Barnett Shale field. The optimization results of DSIAS are compared with those of a pattern search algorithm.

  3. Multi-Target Tracking via Mixed Integer Optimization

    DTIC Science & Technology

    2016-05-13

    solving these two problems separately, however few algorithms attempt to solve these simultaneously and even fewer utilize optimization. In this paper we...introduce a new mixed integer optimization (MIO) model which solves the data association and trajectory estimation problems simultaneously by minimizing...Kalman filter [5], which updates the trajectory estimates before the algorithm progresses forward to the next scan. This process repeats sequentially

  4. A parallel metaheuristic for large mixed-integer dynamic optimization problems, with applications in computational biology

    PubMed Central

    Henriques, David; González, Patricia; Doallo, Ramón; Saez-Rodriguez, Julio; Banga, Julio R.

    2017-01-01

    Background We consider a general class of global optimization problems dealing with nonlinear dynamic models. Although this class is relevant to many areas of science and engineering, here we are interested in applying this framework to the reverse engineering problem in computational systems biology, which yields very large mixed-integer dynamic optimization (MIDO) problems. In particular, we consider the framework of logic-based ordinary differential equations (ODEs). Methods We present saCeSS2, a parallel method for the solution of this class of problems. This method is based on an parallel cooperative scatter search metaheuristic, with new mechanisms of self-adaptation and specific extensions to handle large mixed-integer problems. We have paid special attention to the avoidance of convergence stagnation using adaptive cooperation strategies tailored to this class of problems. Results We illustrate its performance with a set of three very challenging case studies from the domain of dynamic modelling of cell signaling. The simpler case study considers a synthetic signaling pathway and has 84 continuous and 34 binary decision variables. A second case study considers the dynamic modeling of signaling in liver cancer using high-throughput data, and has 135 continuous and 109 binaries decision variables. The third case study is an extremely difficult problem related with breast cancer, involving 690 continuous and 138 binary decision variables. We report computational results obtained in different infrastructures, including a local cluster, a large supercomputer and a public cloud platform. Interestingly, the results show how the cooperation of individual parallel searches modifies the systemic properties of the sequential algorithm, achieving superlinear speedups compared to an individual search (e.g. speedups of 15 with 10 cores), and significantly improving (above a 60%) the performance with respect to a non-cooperative parallel scheme. The scalability of the

  5. A parallel metaheuristic for large mixed-integer dynamic optimization problems, with applications in computational biology.

    PubMed

    Penas, David R; Henriques, David; González, Patricia; Doallo, Ramón; Saez-Rodriguez, Julio; Banga, Julio R

    2017-01-01

    We consider a general class of global optimization problems dealing with nonlinear dynamic models. Although this class is relevant to many areas of science and engineering, here we are interested in applying this framework to the reverse engineering problem in computational systems biology, which yields very large mixed-integer dynamic optimization (MIDO) problems. In particular, we consider the framework of logic-based ordinary differential equations (ODEs). We present saCeSS2, a parallel method for the solution of this class of problems. This method is based on an parallel cooperative scatter search metaheuristic, with new mechanisms of self-adaptation and specific extensions to handle large mixed-integer problems. We have paid special attention to the avoidance of convergence stagnation using adaptive cooperation strategies tailored to this class of problems. We illustrate its performance with a set of three very challenging case studies from the domain of dynamic modelling of cell signaling. The simpler case study considers a synthetic signaling pathway and has 84 continuous and 34 binary decision variables. A second case study considers the dynamic modeling of signaling in liver cancer using high-throughput data, and has 135 continuous and 109 binaries decision variables. The third case study is an extremely difficult problem related with breast cancer, involving 690 continuous and 138 binary decision variables. We report computational results obtained in different infrastructures, including a local cluster, a large supercomputer and a public cloud platform. Interestingly, the results show how the cooperation of individual parallel searches modifies the systemic properties of the sequential algorithm, achieving superlinear speedups compared to an individual search (e.g. speedups of 15 with 10 cores), and significantly improving (above a 60%) the performance with respect to a non-cooperative parallel scheme. The scalability of the method is also good (tests

  6. Control of wavepacket dynamics in mixed alkali metal clusters by optimally shaped fs pulses

    NASA Astrophysics Data System (ADS)

    Bartelt, A.; Minemoto, S.; Lupulescu, C.; Vajda, Š.; Wöste, L.

    We have performed adaptive feedback optimization of phase-shaped femtosecond laser pulses to control the wavepacket dynamics of small mixed alkali-metal clusters. An optimization algorithm based on Evolutionary Strategies was used to maximize the ion intensities. The optimized pulses for NaK and Na2K converged to pulse trains consisting of numerous peaks. The timing of the elements of the pulse trains corresponds to integer and half integer numbers of the vibrational periods of the molecules, reflecting the wavepacket dynamics in their excited states.

  7. Enhanced index tracking modeling in portfolio optimization with mixed-integer programming z approach

    NASA Astrophysics Data System (ADS)

    Siew, Lam Weng; Jaaman, Saiful Hafizah Hj.; Ismail, Hamizun bin

    2014-09-01

    Enhanced index tracking is a popular form of portfolio management in stock market investment. Enhanced index tracking aims to construct an optimal portfolio to generate excess return over the return achieved by the stock market index without purchasing all of the stocks that make up the index. The objective of this paper is to construct an optimal portfolio using mixed-integer programming model which adopts regression approach in order to generate higher portfolio mean return than stock market index return. In this study, the data consists of 24 component stocks in Malaysia market index which is FTSE Bursa Malaysia Kuala Lumpur Composite Index from January 2010 until December 2012. The results of this study show that the optimal portfolio of mixed-integer programming model is able to generate higher mean return than FTSE Bursa Malaysia Kuala Lumpur Composite Index return with only selecting 30% out of the total stock market index components.

  8. Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks

    NASA Technical Reports Server (NTRS)

    Cheung, Kar-Ming; Lee, Charles H.

    2012-01-01

    We developed framework and the mathematical formulation for optimizing communication network using mixed integer programming. The design yields a system that is much smaller, in search space size, when compared to the earlier approach. Our constrained network optimization takes into account the dynamics of link performance within the network along with mission and operation requirements. A unique penalty function is introduced to transform the mixed integer programming into the more manageable problem of searching in a continuous space. The constrained optimization problem was proposed to solve in two stages: first using the heuristic Particle Swarming Optimization algorithm to get a good initial starting point, and then feeding the result into the Sequential Quadratic Programming algorithm to achieve the final optimal schedule. We demonstrate the above planning and scheduling methodology with a scenario of 20 spacecraft and 3 ground stations of a Deep Space Network site. Our approach and framework have been simple and flexible so that problems with larger number of constraints and network can be easily adapted and solved.

  9. Reverse engineering of logic-based differential equation models using a mixed-integer dynamic optimization approach

    PubMed Central

    Henriques, David; Rocha, Miguel; Saez-Rodriguez, Julio; Banga, Julio R.

    2015-01-01

    Motivation: Systems biology models can be used to test new hypotheses formulated on the basis of previous knowledge or new experimental data, contradictory with a previously existing model. New hypotheses often come in the shape of a set of possible regulatory mechanisms. This search is usually not limited to finding a single regulation link, but rather a combination of links subject to great uncertainty or no information about the kinetic parameters. Results: In this work, we combine a logic-based formalism, to describe all the possible regulatory structures for a given dynamic model of a pathway, with mixed-integer dynamic optimization (MIDO). This framework aims to simultaneously identify the regulatory structure (represented by binary parameters) and the real-valued parameters that are consistent with the available experimental data, resulting in a logic-based differential equation model. The alternative to this would be to perform real-valued parameter estimation for each possible model structure, which is not tractable for models of the size presented in this work. The performance of the method presented here is illustrated with several case studies: a synthetic pathway problem of signaling regulation, a two-component signal transduction pathway in bacterial homeostasis, and a signaling network in liver cancer cells. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: julio@iim.csic.es or saezrodriguez@ebi.ac.uk PMID:26002881

  10. Reverse engineering of logic-based differential equation models using a mixed-integer dynamic optimization approach.

    PubMed

    Henriques, David; Rocha, Miguel; Saez-Rodriguez, Julio; Banga, Julio R

    2015-09-15

    Systems biology models can be used to test new hypotheses formulated on the basis of previous knowledge or new experimental data, contradictory with a previously existing model. New hypotheses often come in the shape of a set of possible regulatory mechanisms. This search is usually not limited to finding a single regulation link, but rather a combination of links subject to great uncertainty or no information about the kinetic parameters. In this work, we combine a logic-based formalism, to describe all the possible regulatory structures for a given dynamic model of a pathway, with mixed-integer dynamic optimization (MIDO). This framework aims to simultaneously identify the regulatory structure (represented by binary parameters) and the real-valued parameters that are consistent with the available experimental data, resulting in a logic-based differential equation model. The alternative to this would be to perform real-valued parameter estimation for each possible model structure, which is not tractable for models of the size presented in this work. The performance of the method presented here is illustrated with several case studies: a synthetic pathway problem of signaling regulation, a two-component signal transduction pathway in bacterial homeostasis, and a signaling network in liver cancer cells. Supplementary data are available at Bioinformatics online. julio@iim.csic.es or saezrodriguez@ebi.ac.uk. © The Author 2015. Published by Oxford University Press.

  11. Mixed Integer Programming and Heuristic Scheduling for Space Communication

    NASA Technical Reports Server (NTRS)

    Lee, Charles H.; Cheung, Kar-Ming

    2013-01-01

    Optimal planning and scheduling for a communication network was created where the nodes within the network are communicating at the highest possible rates while meeting the mission requirements and operational constraints. The planning and scheduling problem was formulated in the framework of Mixed Integer Programming (MIP) to introduce a special penalty function to convert the MIP problem into a continuous optimization problem, and to solve the constrained optimization problem using heuristic optimization. The communication network consists of space and ground assets with the link dynamics between any two assets varying with respect to time, distance, and telecom configurations. One asset could be communicating with another at very high data rates at one time, and at other times, communication is impossible, as the asset could be inaccessible from the network due to planetary occultation. Based on the network's geometric dynamics and link capabilities, the start time, end time, and link configuration of each view period are selected to maximize the communication efficiency within the network. Mathematical formulations for the constrained mixed integer optimization problem were derived, and efficient analytical and numerical techniques were developed to find the optimal solution. By setting up the problem using MIP, the search space for the optimization problem is reduced significantly, thereby speeding up the solution process. The ratio of the dimension of the traditional method over the proposed formulation is approximately an order N (single) to 2*N (arraying), where N is the number of receiving antennas of a node. By introducing a special penalty function, the MIP problem with non-differentiable cost function and nonlinear constraints can be converted into a continuous variable problem, whose solution is possible.

  12. Aerospace Applications of Integer and Combinatorial Optimization

    NASA Technical Reports Server (NTRS)

    Padula, S. L.; Kincaid, R. K.

    1995-01-01

    Research supported by NASA Langley Research Center includes many applications of aerospace design optimization and is conducted by teams of applied mathematicians and aerospace engineers. This paper investigates the benefits from this combined expertise in formulating and solving integer and combinatorial optimization problems. Applications range from the design of large space antennas to interior noise control. A typical problem, for example, seeks the optimal locations for vibration-damping devices on an orbiting platform and is expressed as a mixed/integer linear programming problem with more than 1500 design variables.

  13. Aerospace applications on integer and combinatorial optimization

    NASA Technical Reports Server (NTRS)

    Padula, S. L.; Kincaid, R. K.

    1995-01-01

    Research supported by NASA Langley Research Center includes many applications of aerospace design optimization and is conducted by teams of applied mathematicians and aerospace engineers. This paper investigates the benefits from this combined expertise in formulating and solving integer and combinatorial optimization problems. Applications range from the design of large space antennas to interior noise control. A typical problem. for example, seeks the optimal locations for vibration-damping devices on an orbiting platform and is expressed as a mixed/integer linear programming problem with more than 1500 design variables.

  14. A Mixed Integer Linear Programming Approach to Electrical Stimulation Optimization Problems.

    PubMed

    Abouelseoud, Gehan; Abouelseoud, Yasmine; Shoukry, Amin; Ismail, Nour; Mekky, Jaidaa

    2018-02-01

    Electrical stimulation optimization is a challenging problem. Even when a single region is targeted for excitation, the problem remains a constrained multi-objective optimization problem. The constrained nature of the problem results from safety concerns while its multi-objectives originate from the requirement that non-targeted regions should remain unaffected. In this paper, we propose a mixed integer linear programming formulation that can successfully address the challenges facing this problem. Moreover, the proposed framework can conclusively check the feasibility of the stimulation goals. This helps researchers to avoid wasting time trying to achieve goals that are impossible under a chosen stimulation setup. The superiority of the proposed framework over alternative methods is demonstrated through simulation examples.

  15. GAMBIT: A Parameterless Model-Based Evolutionary Algorithm for Mixed-Integer Problems.

    PubMed

    Sadowski, Krzysztof L; Thierens, Dirk; Bosman, Peter A N

    2018-01-01

    Learning and exploiting problem structure is one of the key challenges in optimization. This is especially important for black-box optimization (BBO) where prior structural knowledge of a problem is not available. Existing model-based Evolutionary Algorithms (EAs) are very efficient at learning structure in both the discrete, and in the continuous domain. In this article, discrete and continuous model-building mechanisms are integrated for the Mixed-Integer (MI) domain, comprising discrete and continuous variables. We revisit a recently introduced model-based evolutionary algorithm for the MI domain, the Genetic Algorithm for Model-Based mixed-Integer opTimization (GAMBIT). We extend GAMBIT with a parameterless scheme that allows for practical use of the algorithm without the need to explicitly specify any parameters. We furthermore contrast GAMBIT with other model-based alternatives. The ultimate goal of processing mixed dependences explicitly in GAMBIT is also addressed by introducing a new mechanism for the explicit exploitation of mixed dependences. We find that processing mixed dependences with this novel mechanism allows for more efficient optimization. We further contrast the parameterless GAMBIT with Mixed-Integer Evolution Strategies (MIES) and other state-of-the-art MI optimization algorithms from the General Algebraic Modeling System (GAMS) commercial algorithm suite on problems with and without constraints, and show that GAMBIT is capable of solving problems where variable dependences prevent many algorithms from successfully optimizing them.

  16. Modeling Road Vulnerability to Snow Using Mixed Integer Optimization

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

    Rodriguez, Tony K; Omitaomu, Olufemi A; Ostrowski, James A

    As the number and severity of snowfall events continue to grow, the need to intelligently direct road maintenance during these snowfall events will also grow. In several locations, local governments lack the resources to completely treat all roadways during snow events. Furthermore, some governments utilize only traffic data to determine which roads should be treated. As a result, many schools, businesses, and government offices must be unnecessarily closed, which directly impacts the social, educational, and economic well-being of citizens and institutions. In this work, we propose a mixed integer programming formulation to optimally allocate resources to manage snowfall on roadsmore » using meteorological, geographical, and environmental parameters. Additionally, we evaluate the impacts of an increase in budget for winter road maintenance on snow control resources.« less

  17. A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: I. Robust Linear Optimization and Robust Mixed Integer Linear Optimization

    PubMed Central

    Li, Zukui; Ding, Ran; Floudas, Christodoulos A.

    2011-01-01

    Robust counterpart optimization techniques for linear optimization and mixed integer linear optimization problems are studied in this paper. Different uncertainty sets, including those studied in literature (i.e., interval set; combined interval and ellipsoidal set; combined interval and polyhedral set) and new ones (i.e., adjustable box; pure ellipsoidal; pure polyhedral; combined interval, ellipsoidal, and polyhedral set) are studied in this work and their geometric relationship is discussed. For uncertainty in the left hand side, right hand side, and objective function of the optimization problems, robust counterpart optimization formulations induced by those different uncertainty sets are derived. Numerical studies are performed to compare the solutions of the robust counterpart optimization models and applications in refinery production planning and batch process scheduling problem are presented. PMID:21935263

  18. Incorporation of Fixed Installation Costs into Optimization of Groundwater Remediation with a New Efficient Surrogate Nonlinear Mixed Integer Optimization Algorithm

    NASA Astrophysics Data System (ADS)

    Shoemaker, Christine; Wan, Ying

    2016-04-01

    Optimization of nonlinear water resources management issues which have a mixture of fixed (e.g. construction cost for a well) and variable (e.g. cost per gallon of water pumped) costs has been not well addressed because prior algorithms for the resulting nonlinear mixed integer problems have required many groundwater simulations (with different configurations of decision variable), especially when the solution space is multimodal. In particular heuristic methods like genetic algorithms have often been used in the water resources area, but they require so many groundwater simulations that only small systems have been solved. Hence there is a need to have a method that reduces the number of expensive groundwater simulations. A recently published algorithm for nonlinear mixed integer programming using surrogates was shown in this study to greatly reduce the computational effort for obtaining accurate answers to problems involving fixed costs for well construction as well as variable costs for pumping because of a substantial reduction in the number of groundwater simulations required to obtain an accurate answer. Results are presented for a US EPA hazardous waste site. The nonlinear mixed integer surrogate algorithm is general and can be used on other problems arising in hydrology with open source codes in Matlab and python ("pySOT" in Bitbucket).

  19. Comparison of penalty functions on a penalty approach to mixed-integer optimization

    NASA Astrophysics Data System (ADS)

    Francisco, Rogério B.; Costa, M. Fernanda P.; Rocha, Ana Maria A. C.; Fernandes, Edite M. G. P.

    2016-06-01

    In this paper, we present a comparative study involving several penalty functions that can be used in a penalty approach for globally solving bound mixed-integer nonlinear programming (bMIMLP) problems. The penalty approach relies on a continuous reformulation of the bMINLP problem by adding a particular penalty term to the objective function. A penalty function based on the `erf' function is proposed. The continuous nonlinear optimization problems are sequentially solved by the population-based firefly algorithm. Preliminary numerical experiments are carried out in order to analyze the quality of the produced solutions, when compared with other penalty functions available in the literature.

  20. Aerospace applications of integer and combinatorial optimization

    NASA Technical Reports Server (NTRS)

    Padula, S. L.; Kincaid, R. K.

    1995-01-01

    Research supported by NASA Langley Research Center includes many applications of aerospace design optimization and is conducted by teams of applied mathematicians and aerospace engineers. This paper investigates the benefits from this combined expertise in solving combinatorial optimization problems. Applications range from the design of large space antennas to interior noise control. A typical problem, for example, seeks the optimal locations for vibration-damping devices on a large space structure and is expressed as a mixed/integer linear programming problem with more than 1500 design variables.

  1. Beam orientation optimization for intensity-modulated radiation therapy using mixed integer programming

    NASA Astrophysics Data System (ADS)

    Yang, Ruijie; Dai, Jianrong; Yang, Yong; Hu, Yimin

    2006-08-01

    The purpose of this study is to extend an algorithm proposed for beam orientation optimization in classical conformal radiotherapy to intensity-modulated radiation therapy (IMRT) and to evaluate the algorithm's performance in IMRT scenarios. In addition, the effect of the candidate pool of beam orientations, in terms of beam orientation resolution and starting orientation, on the optimized beam configuration, plan quality and optimization time is also explored. The algorithm is based on the technique of mixed integer linear programming in which binary and positive float variables are employed to represent candidates for beam orientation and beamlet weights in beam intensity maps. Both beam orientations and beam intensity maps are simultaneously optimized in the algorithm with a deterministic method. Several different clinical cases were used to test the algorithm and the results show that both target coverage and critical structures sparing were significantly improved for the plans with optimized beam orientations compared to those with equi-spaced beam orientations. The calculation time was less than an hour for the cases with 36 binary variables on a PC with a Pentium IV 2.66 GHz processor. It is also found that decreasing beam orientation resolution to 10° greatly reduced the size of the candidate pool of beam orientations without significant influence on the optimized beam configuration and plan quality, while selecting different starting orientations had large influence. Our study demonstrates that the algorithm can be applied to IMRT scenarios, and better beam orientation configurations can be obtained using this algorithm. Furthermore, the optimization efficiency can be greatly increased through proper selection of beam orientation resolution and starting beam orientation while guaranteeing the optimized beam configurations and plan quality.

  2. PIPS-SBB: A Parallel Distributed-Memory Branch-and-Bound Algorithm for Stochastic Mixed-Integer Programs

    DOE PAGES

    Munguia, Lluis-Miquel; Oxberry, Geoffrey; Rajan, Deepak

    2016-05-01

    Stochastic mixed-integer programs (SMIPs) deal with optimization under uncertainty at many levels of the decision-making process. When solved as extensive formulation mixed- integer programs, problem instances can exceed available memory on a single workstation. In order to overcome this limitation, we present PIPS-SBB: a distributed-memory parallel stochastic MIP solver that takes advantage of parallelism at multiple levels of the optimization process. We also show promising results on the SIPLIB benchmark by combining methods known for accelerating Branch and Bound (B&B) methods with new ideas that leverage the structure of SMIPs. Finally, we expect the performance of PIPS-SBB to improve furthermore » as more functionality is added in the future.« less

  3. A Mixed Integer Efficient Global Optimization Framework: Applied to the Simultaneous Aircraft Design, Airline Allocation and Revenue Management Problem

    NASA Astrophysics Data System (ADS)

    Roy, Satadru

    Traditional approaches to design and optimize a new system, often, use a system-centric objective and do not take into consideration how the operator will use this new system alongside of other existing systems. This "hand-off" between the design of the new system and how the new system operates alongside other systems might lead to a sub-optimal performance with respect to the operator-level objective. In other words, the system that is optimal for its system-level objective might not be best for the system-of-systems level objective of the operator. Among the few available references that describe attempts to address this hand-off, most follow an MDO-motivated subspace decomposition approach of first designing a very good system and then provide this system to the operator who decides the best way to use this new system along with the existing systems. The motivating example in this dissertation presents one such similar problem that includes aircraft design, airline operations and revenue management "subspaces". The research here develops an approach that could simultaneously solve these subspaces posed as a monolithic optimization problem. The monolithic approach makes the problem a Mixed Integer/Discrete Non-Linear Programming (MINLP/MDNLP) problem, which are extremely difficult to solve. The presence of expensive, sophisticated engineering analyses further aggravate the problem. To tackle this challenge problem, the work here presents a new optimization framework that simultaneously solves the subspaces to capture the "synergism" in the problem that the previous decomposition approaches may not have exploited, addresses mixed-integer/discrete type design variables in an efficient manner, and accounts for computationally expensive analysis tools. The framework combines concepts from efficient global optimization, Kriging partial least squares, and gradient-based optimization. This approach then demonstrates its ability to solve an 11 route airline network

  4. A mixed integer program to model spatial wildfire behavior and suppression placement decisions

    Treesearch

    Erin J. Belval; Yu Wei; Michael Bevers

    2015-01-01

    Wildfire suppression combines multiple objectives and dynamic fire behavior to form a complex problem for decision makers. This paper presents a mixed integer program designed to explore integrating spatial fire behavior and suppression placement decisions into a mathematical programming framework. Fire behavior and suppression placement decisions are modeled using...

  5. FPGA Implementation of Optimal 3D-Integer DCT Structure for Video Compression

    PubMed Central

    2015-01-01

    A novel optimal structure for implementing 3D-integer discrete cosine transform (DCT) is presented by analyzing various integer approximation methods. The integer set with reduced mean squared error (MSE) and high coding efficiency are considered for implementation in FPGA. The proposed method proves that the least resources are utilized for the integer set that has shorter bit values. Optimal 3D-integer DCT structure is determined by analyzing the MSE, power dissipation, coding efficiency, and hardware complexity of different integer sets. The experimental results reveal that direct method of computing the 3D-integer DCT using the integer set [10, 9, 6, 2, 3, 1, 1] performs better when compared to other integer sets in terms of resource utilization and power dissipation. PMID:26601120

  6. Inexact fuzzy-stochastic mixed-integer programming approach for long-term planning of waste management--Part A: methodology.

    PubMed

    Guo, P; Huang, G H

    2009-01-01

    In this study, an inexact fuzzy chance-constrained two-stage mixed-integer linear programming (IFCTIP) approach is proposed for supporting long-term planning of waste-management systems under multiple uncertainties in the City of Regina, Canada. The method improves upon the existing inexact two-stage programming and mixed-integer linear programming techniques by incorporating uncertainties expressed as multiple uncertainties of intervals and dual probability distributions within a general optimization framework. The developed method can provide an effective linkage between the predefined environmental policies and the associated economic implications. Four special characteristics of the proposed method make it unique compared with other optimization techniques that deal with uncertainties. Firstly, it provides a linkage to predefined policies that have to be respected when a modeling effort is undertaken; secondly, it is useful for tackling uncertainties presented as intervals, probabilities, fuzzy sets and their incorporation; thirdly, it facilitates dynamic analysis for decisions of facility-expansion planning and waste-flow allocation within a multi-facility, multi-period, multi-level, and multi-option context; fourthly, the penalties are exercised with recourse against any infeasibility, which permits in-depth analyses of various policy scenarios that are associated with different levels of economic consequences when the promised solid waste-generation rates are violated. In a companion paper, the developed method is applied to a real case for the long-term planning of waste management in the City of Regina, Canada.

  7. Constrained spacecraft reorientation using mixed integer convex programming

    NASA Astrophysics Data System (ADS)

    Tam, Margaret; Glenn Lightsey, E.

    2016-10-01

    A constrained attitude guidance (CAG) system is developed using convex optimization to autonomously achieve spacecraft pointing objectives while meeting the constraints imposed by on-board hardware. These constraints include bounds on the control input and slew rate, as well as pointing constraints imposed by the sensors. The pointing constraints consist of inclusion and exclusion cones that dictate permissible orientations of the spacecraft in order to keep objects in or out of the field of view of the sensors. The optimization scheme drives a body vector towards a target inertial vector along a trajectory that consists solely of permissible orientations in order to achieve the desired attitude for a given mission mode. The non-convex rotational kinematics are handled by discretization, which also ensures that the quaternion stays unity norm. In order to guarantee an admissible path, the pointing constraints are relaxed. Depending on how strict the pointing constraints are, the degree of relaxation is tuneable. The use of binary variables permits the inclusion of logical expressions in the pointing constraints in the case that a set of sensors has redundancies. The resulting mixed integer convex programming (MICP) formulation generates a steering law that can be easily integrated into an attitude determination and control (ADC) system. A sample simulation of the system is performed for the Bevo-2 satellite, including disturbance torques and actuator dynamics which are not modeled by the controller. Simulation results demonstrate the robustness of the system to disturbances while meeting the mission requirements with desirable performance characteristics.

  8. Fast scaffolding with small independent mixed integer programs

    PubMed Central

    Salmela, Leena; Mäkinen, Veli; Välimäki, Niko; Ylinen, Johannes; Ukkonen, Esko

    2011-01-01

    Motivation: Assembling genomes from short read data has become increasingly popular, but the problem remains computationally challenging especially for larger genomes. We study the scaffolding phase of sequence assembly where preassembled contigs are ordered based on mate pair data. Results: We present MIP Scaffolder that divides the scaffolding problem into smaller subproblems and solves these with mixed integer programming. The scaffolding problem can be represented as a graph and the biconnected components of this graph can be solved independently. We present a technique for restricting the size of these subproblems so that they can be solved accurately with mixed integer programming. We compare MIP Scaffolder to two state of the art methods, SOPRA and SSPACE. MIP Scaffolder is fast and produces better or as good scaffolds as its competitors on large genomes. Availability: The source code of MIP Scaffolder is freely available at http://www.cs.helsinki.fi/u/lmsalmel/mip-scaffolder/. Contact: leena.salmela@cs.helsinki.fi PMID:21998153

  9. Optimal integer resolution for attitude determination using global positioning system signals

    NASA Technical Reports Server (NTRS)

    Crassidis, John L.; Markley, F. Landis; Lightsey, E. Glenn

    1998-01-01

    In this paper, a new motion-based algorithm for GPS integer ambiguity resolution is derived. The first step of this algorithm converts the reference sightline vectors into body frame vectors. This is accomplished by an optimal vectorized transformation of the phase difference measurements. The result of this transformation leads to the conversion of the integer ambiguities to vectorized biases. This essentially converts the problem to the familiar magnetometer-bias determination problem, for which an optimal and efficient solution exists. Also, the formulation in this paper is re-derived to provide a sequential estimate, so that a suitable stopping condition can be found during the vehicle motion. The advantages of the new algorithm include: it does not require an a-priori estimate of the vehicle's attitude; it provides an inherent integrity check using a covariance-type expression; and it can sequentially estimate the ambiguities during the vehicle motion. The only disadvantage of the new algorithm is that it requires at least three non-coplanar baselines. The performance of the new algorithm is tested on a dynamic hardware simulator.

  10. Synchronic interval Gaussian mixed-integer programming for air quality management.

    PubMed

    Cheng, Guanhui; Huang, Guohe Gordon; Dong, Cong

    2015-12-15

    To reveal the synchronism of interval uncertainties, the tradeoff between system optimality and security, the discreteness of facility-expansion options, the uncertainty of pollutant dispersion processes, and the seasonality of wind features in air quality management (AQM) systems, a synchronic interval Gaussian mixed-integer programming (SIGMIP) approach is proposed in this study. A robust interval Gaussian dispersion model is developed for approaching the pollutant dispersion process under interval uncertainties and seasonal variations. The reflection of synchronic effects of interval uncertainties in the programming objective is enabled through introducing interval functions. The proposition of constraint violation degrees helps quantify the tradeoff between system optimality and constraint violation under interval uncertainties. The overall optimality of system profits of an SIGMIP model is achieved based on the definition of an integrally optimal solution. Integer variables in the SIGMIP model are resolved by the existing cutting-plane method. Combining these efforts leads to an effective algorithm for the SIGMIP model. An application to an AQM problem in a region in Shandong Province, China, reveals that the proposed SIGMIP model can facilitate identifying the desired scheme for AQM. The enhancement of the robustness of optimization exercises may be helpful for increasing the reliability of suggested schemes for AQM under these complexities. The interrelated tradeoffs among control measures, emission sources, flow processes, receptors, influencing factors, and economic and environmental goals are effectively balanced. Interests of many stakeholders are reasonably coordinated. The harmony between economic development and air quality control is enabled. Results also indicate that the constraint violation degree is effective at reflecting the compromise relationship between constraint-violation risks and system optimality under interval uncertainties. This can

  11. Item Pool Construction Using Mixed Integer Quadratic Programming (MIQP). GMAC® Research Report RR-14-01

    ERIC Educational Resources Information Center

    Han, Kyung T.; Rudner, Lawrence M.

    2014-01-01

    This study uses mixed integer quadratic programming (MIQP) to construct multiple highly equivalent item pools simultaneously, and compares the results from mixed integer programming (MIP). Three different MIP/MIQP models were implemented and evaluated using real CAT item pool data with 23 different content areas and a goal of equal information…

  12. A generalized interval fuzzy mixed integer programming model for a multimodal transportation problem under uncertainty

    NASA Astrophysics Data System (ADS)

    Tian, Wenli; Cao, Chengxuan

    2017-03-01

    A generalized interval fuzzy mixed integer programming model is proposed for the multimodal freight transportation problem under uncertainty, in which the optimal mode of transport and the optimal amount of each type of freight transported through each path need to be decided. For practical purposes, three mathematical methods, i.e. the interval ranking method, fuzzy linear programming method and linear weighted summation method, are applied to obtain equivalents of constraints and parameters, and then a fuzzy expected value model is presented. A heuristic algorithm based on a greedy criterion and the linear relaxation algorithm are designed to solve the model.

  13. Industrial waste recycling strategies optimization problem: mixed integer programming model and heuristics

    NASA Astrophysics Data System (ADS)

    Tang, Jiafu; Liu, Yang; Fung, Richard; Luo, Xinggang

    2008-12-01

    Manufacturers have a legal accountability to deal with industrial waste generated from their production processes in order to avoid pollution. Along with advances in waste recovery techniques, manufacturers may adopt various recycling strategies in dealing with industrial waste. With reuse strategies and technologies, byproducts or wastes will be returned to production processes in the iron and steel industry, and some waste can be recycled back to base material for reuse in other industries. This article focuses on a recovery strategies optimization problem for a typical class of industrial waste recycling process in order to maximize profit. There are multiple strategies for waste recycling available to generate multiple byproducts; these byproducts are then further transformed into several types of chemical products via different production patterns. A mixed integer programming model is developed to determine which recycling strategy and which production pattern should be selected with what quantity of chemical products corresponding to this strategy and pattern in order to yield maximum marginal profits. The sales profits of chemical products and the set-up costs of these strategies, patterns and operation costs of production are considered. A simulated annealing (SA) based heuristic algorithm is developed to solve the problem. Finally, an experiment is designed to verify the effectiveness and feasibility of the proposed method. By comparing a single strategy to multiple strategies in an example, it is shown that the total sales profit of chemical products can be increased by around 25% through the simultaneous use of multiple strategies. This illustrates the superiority of combinatorial multiple strategies. Furthermore, the effects of the model parameters on profit are discussed to help manufacturers organize their waste recycling network.

  14. A Composite Algorithm for Mixed Integer Constrained Nonlinear Optimization.

    DTIC Science & Technology

    1980-01-01

    de Silva [141, and Weisman and Wood [76). A particular direct search algorithm, the simplex method, has been cited for having the potential for...spaced discrete points on a line which makes the direction suitable for an efficient integer search technique based on Fibonacci numbers. Two...defined by a subset of variables. The complex algorithm is particularly well suited for this subspace search for two reasons. First, the complex method

  15. Mixed integer programming model for optimizing the layout of an ICU vehicle.

    PubMed

    Alejo, Javier Sánchez; Martín, Modoaldo Garrido; Ortega-Mier, Miguel; García-Sánchez, Alvaro

    2009-12-08

    This paper presents a Mixed Integer Programming (MIP) model for designing the layout of the Intensive Care Units' (ICUs) patient care space. In particular, this MIP model was developed for optimizing the layout for materials to be used in interventions. This work was developed within the framework of a joint project between the Madrid Technical Unverstity and the Medical Emergency Services of the Madrid Regional Government (SUMMA 112). The first task was to identify the relevant information to define the characteristics of the new vehicles and, in particular, to obtain a satisfactory interior layout to locate all the necessary materials. This information was gathered from health workers related to ICUs. With that information an optimization model was developed in order to obtain a solution. From the MIP model, a first solution was obtained, consisting of a grid to locate the different materials needed for the ICUs. The outcome from the MIP model was discussed with health workers to tune the solution, and after slightly altering that solution to meet some requirements that had not been included in the mathematical model, the eventual solution was approved by the persons responsible for specifying the characteristics of the new vehicles. According to the opinion stated by the SUMMA 112's medical group responsible for improving the ambulances (the so-called "coaching group"), the outcome was highly satisfactory. Indeed, the final design served as a basis to draw up the requirements of a public tender. As a result from solving the Optimization model, a grid was obtained to locate the different necessary materials for the ICUs. This grid had to be slightly altered to meet some requirements that had not been included in the mathematical model. The results were discussed with the persons responsible for specifying the characteristics of the new vehicles. The outcome was highly satisfactory. Indeed, the final design served as a basis to draw up the requirements of a public

  16. Mixed integer programming model for optimizing the layout of an ICU vehicle

    PubMed Central

    2009-01-01

    Background This paper presents a Mixed Integer Programming (MIP) model for designing the layout of the Intensive Care Units' (ICUs) patient care space. In particular, this MIP model was developed for optimizing the layout for materials to be used in interventions. This work was developed within the framework of a joint project between the Madrid Technical Unverstity and the Medical Emergency Services of the Madrid Regional Government (SUMMA 112). Methods The first task was to identify the relevant information to define the characteristics of the new vehicles and, in particular, to obtain a satisfactory interior layout to locate all the necessary materials. This information was gathered from health workers related to ICUs. With that information an optimization model was developed in order to obtain a solution. From the MIP model, a first solution was obtained, consisting of a grid to locate the different materials needed for the ICUs. The outcome from the MIP model was discussed with health workers to tune the solution, and after slightly altering that solution to meet some requirements that had not been included in the mathematical model, the eventual solution was approved by the persons responsible for specifying the characteristics of the new vehicles. According to the opinion stated by the SUMMA 112's medical group responsible for improving the ambulances (the so-called "coaching group"), the outcome was highly satisfactory. Indeed, the final design served as a basis to draw up the requirements of a public tender. Results As a result from solving the Optimization model, a grid was obtained to locate the different necessary materials for the ICUs. This grid had to be slightly altered to meet some requirements that had not been included in the mathematical model. The results were discussed with the persons responsible for specifying the characteristics of the new vehicles. Conclusion The outcome was highly satisfactory. Indeed, the final design served as a basis

  17. Modeling hospital infrastructure by optimizing quality, accessibility and efficiency via a mixed integer programming model.

    PubMed

    Ikkersheim, David; Tanke, Marit; van Schooten, Gwendy; de Bresser, Niels; Fleuren, Hein

    2013-06-16

    The majority of curative health care is organized in hospitals. As in most other countries, the current 94 hospital locations in the Netherlands offer almost all treatments, ranging from rather basic to very complex care. Recent studies show that concentration of care can lead to substantial quality improvements for complex conditions and that dispersion of care for chronic conditions may increase quality of care. In previous studies on allocation of hospital infrastructure, the allocation is usually only based on accessibility and/or efficiency of hospital care. In this paper, we explore the possibilities to include a quality function in the objective function, to give global directions to how the 'optimal' hospital infrastructure would be in the Dutch context. To create optimal societal value we have used a mathematical mixed integer programming (MIP) model that balances quality, efficiency and accessibility of care for 30 ICD-9 diagnosis groups. Typical aspects that are taken into account are the volume-outcome relationship, the maximum accepted travel times for diagnosis groups that may need emergency treatment and the minimum use of facilities. The optimal number of hospital locations per diagnosis group varies from 12-14 locations for diagnosis groups which have a strong volume-outcome relationship, such as neoplasms, to 150 locations for chronic diagnosis groups such as diabetes and chronic obstructive pulmonary disease (COPD). In conclusion, our study shows a new approach for allocating hospital infrastructure over a country or certain region that includes quality of care in relation to volume per provider that can be used in various countries or regions. In addition, our model shows that within the Dutch context chronic care may be too concentrated and complex and/or acute care may be too dispersed. Our approach can relatively easily be adopted towards other countries or regions and is very suitable to perform a 'what-if' analysis.

  18. Optimal Diet Planning for Eczema Patient Using Integer Programming

    NASA Astrophysics Data System (ADS)

    Zhen Sheng, Low; Sufahani, Suliadi

    2018-04-01

    Human diet planning is conducted by choosing appropriate food items that fulfill the nutritional requirements into the diet formulation. This paper discusses the application of integer programming to build the mathematical model of diet planning for eczema patients. The model developed is used to solve the diet problem of eczema patients from young age group. The integer programming is a scientific approach to select suitable food items, which seeks to minimize the costs, under conditions of meeting desired nutrient quantities, avoiding food allergens and getting certain foods into the diet that brings relief to the eczema conditions. This paper illustrates that the integer programming approach able to produce the optimal and feasible solution to deal with the diet problem of eczema patient.

  19. Distributed mixed-integer fuzzy hierarchical programming for municipal solid waste management. Part I: System identification and methodology development.

    PubMed

    Cheng, Guanhui; Huang, Guohe; Dong, Cong; Xu, Ye; Chen, Xiujuan; Chen, Jiapei

    2017-03-01

    Due to the existence of complexities of heterogeneities, hierarchy, discreteness, and interactions in municipal solid waste management (MSWM) systems such as Beijing, China, a series of socio-economic and eco-environmental problems may emerge or worsen and result in irredeemable damages in the following decades. Meanwhile, existing studies, especially ones focusing on MSWM in Beijing, could hardly reflect these complexities in system simulations and provide reliable decision support for management practices. Thus, a framework of distributed mixed-integer fuzzy hierarchical programming (DMIFHP) is developed in this study for MSWM under these complexities. Beijing is selected as a representative case. The Beijing MSWM system is comprehensively analyzed in many aspects such as socio-economic conditions, natural conditions, spatial heterogeneities, treatment facilities, and system complexities, building a solid foundation for system simulation and optimization. Correspondingly, the MSWM system in Beijing is discretized as 235 grids to reflect spatial heterogeneity. A DMIFHP model which is a nonlinear programming problem is constructed to parameterize the Beijing MSWM system. To enable scientific solving of it, a solution algorithm is proposed based on coupling of fuzzy programming and mixed-integer linear programming. Innovations and advantages of the DMIFHP framework are discussed. The optimal MSWM schemes and mechanism revelations will be discussed in another companion paper due to length limitation.

  20. BBPH: Using progressive hedging within branch and bound to solve multi-stage stochastic mixed integer programs

    DOE PAGES

    Barnett, Jason; Watson, Jean -Paul; Woodruff, David L.

    2016-11-27

    Progressive hedging, though an effective heuristic for solving stochastic mixed integer programs (SMIPs), is not guaranteed to converge in this case. Here, we describe BBPH, a branch and bound algorithm that uses PH at each node in the search tree such that, given sufficient time, it will always converge to a globally optimal solution. Additionally, to providing a theoretically convergent “wrapper” for PH applied to SMIPs, computational results demonstrate that for some difficult problem instances branch and bound can find improved solutions after exploring only a few nodes.

  1. Mixed Integer Programming Model and Incremental Optimization for Delivery and Storage Planning Using Truck Terminals

    NASA Astrophysics Data System (ADS)

    Sakakibara, Kazutoshi; Tian, Yajie; Nishikawa, Ikuko

    We discuss the planning of transportation by trucks over a multi-day period. Each truck collects loads from suppliers and delivers them to assembly plants or a truck terminal. By exploiting the truck terminal as a temporal storage, we aim to increase the load ratio of each truck and to minimize the lead time for transportation. In this paper, we show a mixed integer programming model which represents each product explicitly, and discuss the decomposition of the problem into a problem of delivery and storage, and a problem of vehicle routing. Based on this model, we propose a relax-and-fix type heuristic in which decision variables are fixed one by one by mathematical programming techniques such as branch-and-bound methods.

  2. Mixed-Integer Conic Linear Programming: Challenges and Perspectives

    DTIC Science & Technology

    2013-10-01

    The novel DCCs for MISOCO may be used in branch- and-cut algorithms when solving MISOCO problems. The experimental software CICLO was developed to...perform limited, but rigorous computational experiments. The CICLO solver utilizes continuous SOCO solvers, MOSEK, CPLES or SeDuMi, builds on the open...submitted Fall 2013. Software: 1. CICLO : Integer conic linear optimization package. Authors: J.C. Góez, T.K. Ralphs, Y. Fu, and T. Terlaky

  3. Fast Integer Ambiguity Resolution for GPS Attitude Determination

    NASA Technical Reports Server (NTRS)

    Lightsey, E. Glenn; Crassidis, John L.; Markley, F. Landis

    1999-01-01

    In this paper, a new algorithm for GPS (Global Positioning System) integer ambiguity resolution is shown. The algorithm first incorporates an instantaneous (static) integer search to significantly reduce the search space using a geometric inequality. Then a batch-type loss function is used to check the remaining integers in order to determine the optimal integer. This batch function represents the GPS sightline vectors in the body frame as the sum of two vectors, one depending on the phase measurements and the other on the unknown integers. The new algorithm has several advantages: it does not require an a-priori estimate of the vehicle's attitude; it provides an inherent integrity check using a covariance-type expression; and it can resolve the integers even when coplanar baselines exist. The performance of the new algorithm is tested on a dynamic hardware simulator.

  4. On Revenue-Optimal Dynamic Auctions for Bidders with Interdependent Values

    NASA Astrophysics Data System (ADS)

    Constantin, Florin; Parkes, David C.

    In a dynamic market, being able to update one's value based on information available to other bidders currently in the market can be critical to having profitable transactions. This is nicely captured by the model of interdependent values (IDV): a bidder's value can explicitly depend on the private information of other bidders. In this paper we present preliminary results about the revenue properties of dynamic auctions for IDV bidders. We adopt a computational approach to design single-item revenue-optimal dynamic auctions with known arrivals and departures but (private) signals that arrive online. In leveraging a characterization of truthful auctions, we present a mixed-integer programming formulation of the design problem. Although a discretization is imposed on bidder signals the solution is a mechanism applicable to continuous signals. The formulation size grows exponentially in the dependence of bidders' values on other bidders' signals. We highlight general properties of revenue-optimal dynamic auctions in a simple parametrized example and study the sensitivity of prices and revenue to model parameters.

  5. Automatic Design of Synthetic Gene Circuits through Mixed Integer Non-linear Programming

    PubMed Central

    Huynh, Linh; Kececioglu, John; Köppe, Matthias; Tagkopoulos, Ilias

    2012-01-01

    Automatic design of synthetic gene circuits poses a significant challenge to synthetic biology, primarily due to the complexity of biological systems, and the lack of rigorous optimization methods that can cope with the combinatorial explosion as the number of biological parts increases. Current optimization methods for synthetic gene design rely on heuristic algorithms that are usually not deterministic, deliver sub-optimal solutions, and provide no guaranties on convergence or error bounds. Here, we introduce an optimization framework for the problem of part selection in synthetic gene circuits that is based on mixed integer non-linear programming (MINLP), which is a deterministic method that finds the globally optimal solution and guarantees convergence in finite time. Given a synthetic gene circuit, a library of characterized parts, and user-defined constraints, our method can find the optimal selection of parts that satisfy the constraints and best approximates the objective function given by the user. We evaluated the proposed method in the design of three synthetic circuits (a toggle switch, a transcriptional cascade, and a band detector), with both experimentally constructed and synthetic promoter libraries. Scalability and robustness analysis shows that the proposed framework scales well with the library size and the solution space. The work described here is a step towards a unifying, realistic framework for the automated design of biological circuits. PMID:22536398

  6. Automatic design of synthetic gene circuits through mixed integer non-linear programming.

    PubMed

    Huynh, Linh; Kececioglu, John; Köppe, Matthias; Tagkopoulos, Ilias

    2012-01-01

    Automatic design of synthetic gene circuits poses a significant challenge to synthetic biology, primarily due to the complexity of biological systems, and the lack of rigorous optimization methods that can cope with the combinatorial explosion as the number of biological parts increases. Current optimization methods for synthetic gene design rely on heuristic algorithms that are usually not deterministic, deliver sub-optimal solutions, and provide no guaranties on convergence or error bounds. Here, we introduce an optimization framework for the problem of part selection in synthetic gene circuits that is based on mixed integer non-linear programming (MINLP), which is a deterministic method that finds the globally optimal solution and guarantees convergence in finite time. Given a synthetic gene circuit, a library of characterized parts, and user-defined constraints, our method can find the optimal selection of parts that satisfy the constraints and best approximates the objective function given by the user. We evaluated the proposed method in the design of three synthetic circuits (a toggle switch, a transcriptional cascade, and a band detector), with both experimentally constructed and synthetic promoter libraries. Scalability and robustness analysis shows that the proposed framework scales well with the library size and the solution space. The work described here is a step towards a unifying, realistic framework for the automated design of biological circuits.

  7. A Polyhedral Outer-approximation, Dynamic-discretization optimization solver, 1.x

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

    Bent, Rusell; Nagarajan, Harsha; Sundar, Kaarthik

    2017-09-25

    In this software, we implement an adaptive, multivariate partitioning algorithm for solving mixed-integer nonlinear programs (MINLP) to global optimality. The algorithm combines ideas that exploit the structure of convex relaxations to MINLPs and bound tightening procedures

  8. MDTri: robust and efficient global mixed integer search of spaces of multiple ternary alloys: A DIRECT-inspired optimization algorithm for experimentally accessible computational material design

    DOE PAGES

    Graf, Peter A.; Billups, Stephen

    2017-07-24

    Computational materials design has suffered from a lack of algorithms formulated in terms of experimentally accessible variables. Here we formulate the problem of (ternary) alloy optimization at the level of choice of atoms and their composition that is normal for synthesists. Mathematically, this is a mixed integer problem where a candidate solution consists of a choice of three elements, and how much of each of them to use. This space has the natural structure of a set of equilateral triangles. We solve this problem by introducing a novel version of the DIRECT algorithm that (1) operates on equilateral triangles insteadmore » of rectangles and (2) works across multiple triangles. We demonstrate on a test case that the algorithm is both robust and efficient. Lastly, we offer an explanation of the efficacy of DIRECT -- specifically, its balance of global and local search -- by showing that 'potentially optimal rectangles' of the original algorithm are akin to the Pareto front of the 'multi-component optimization' of global and local search.« less

  9. MDTri: robust and efficient global mixed integer search of spaces of multiple ternary alloys: A DIRECT-inspired optimization algorithm for experimentally accessible computational material design

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

    Graf, Peter A.; Billups, Stephen

    Computational materials design has suffered from a lack of algorithms formulated in terms of experimentally accessible variables. Here we formulate the problem of (ternary) alloy optimization at the level of choice of atoms and their composition that is normal for synthesists. Mathematically, this is a mixed integer problem where a candidate solution consists of a choice of three elements, and how much of each of them to use. This space has the natural structure of a set of equilateral triangles. We solve this problem by introducing a novel version of the DIRECT algorithm that (1) operates on equilateral triangles insteadmore » of rectangles and (2) works across multiple triangles. We demonstrate on a test case that the algorithm is both robust and efficient. Lastly, we offer an explanation of the efficacy of DIRECT -- specifically, its balance of global and local search -- by showing that 'potentially optimal rectangles' of the original algorithm are akin to the Pareto front of the 'multi-component optimization' of global and local search.« less

  10. An Integer Programming Model for Multi-Echelon Supply Chain Decision Problem Considering Inventories

    NASA Astrophysics Data System (ADS)

    Harahap, Amin; Mawengkang, Herman; Siswadi; Effendi, Syahril

    2018-01-01

    In this paper we address a problem that is of significance to the industry, namely the optimal decision of a multi-echelon supply chain and the associated inventory systems. By using the guaranteed service approach to model the multi-echelon inventory system, we develop a mixed integer; programming model to simultaneously optimize the transportation, inventory and network structure of a multi-echelon supply chain. To solve the model we develop a direct search approach using a strategy of releasing nonbasic variables from their bounds, combined with the “active constraint” method. This strategy is used to force the appropriate non-integer basic variables to move to their neighbourhood integer points.

  11. Software For Integer Programming

    NASA Technical Reports Server (NTRS)

    Fogle, F. R.

    1992-01-01

    Improved Exploratory Search Technique for Pure Integer Linear Programming Problems (IESIP) program optimizes objective function of variables subject to confining functions or constraints, using discrete optimization or integer programming. Enables rapid solution of problems up to 10 variables in size. Integer programming required for accuracy in modeling systems containing small number of components, distribution of goods, scheduling operations on machine tools, and scheduling production in general. Written in Borland's TURBO Pascal.

  12. Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA

    PubMed Central

    Tahriri, Farzad; Dawal, Siti Zawiah Md; Taha, Zahari

    2014-01-01

    A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model. PMID:24982962

  13. An integer programming model to optimize resource allocation for wildfire containment.

    Treesearch

    Geoffrey H. Donovan; Douglas B. Rideout

    2003-01-01

    Determining the specific mix of fire-fighting resources for a given fire is a necessary condition for identifying the minimum of the Cost Plus Net Value Change (C+NVC) function. Current wildland fire management models may not reliably do so. The problem of identifying the most efficient wildland fire organization is characterized mathematically using integer-...

  14. Final Report---Optimization Under Nonconvexity and Uncertainty: Algorithms and Software

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

    Jeff Linderoth

    2011-11-06

    the goal of this work was to develop new algorithmic techniques for solving large-scale numerical optimization problems, focusing on problems classes that have proven to be among the most challenging for practitioners: those involving uncertainty and those involving nonconvexity. This research advanced the state-of-the-art in solving mixed integer linear programs containing symmetry, mixed integer nonlinear programs, and stochastic optimization problems. The focus of the work done in the continuation was on Mixed Integer Nonlinear Programs (MINLP)s and Mixed Integer Linear Programs (MILP)s, especially those containing a great deal of symmetry.

  15. Stacking-sequence optimization for buckling of laminated plates by integer programming

    NASA Technical Reports Server (NTRS)

    Haftka, Raphael T.; Walsh, Joanne L.

    1991-01-01

    Integer-programming formulations for the design of symmetric and balanced laminated plates under biaxial compression are presented. Both maximization of buckling load for a given total thickness and the minimization of total thickness subject to a buckling constraint are formulated. The design variables that define the stacking sequence of the laminate are zero-one integers. It is shown that the formulation results in a linear optimization problem that can be solved on readily available software. This is in contrast to the continuous case, where the design variables are the thicknesses of layers with specified ply orientations, and the optimization problem is nonlinear. Constraints on the stacking sequence such as a limit on the number of contiguous plies of the same orientation and limits on in-plane stiffnesses are easily accommodated. Examples are presented for graphite-epoxy plates under uniaxial and biaxial compression using a commercial software package based on the branch-and-bound algorithm.

  16. Optimal planning of co-firing alternative fuels with coal in a power plant by grey nonlinear mixed integer programming model.

    PubMed

    Ko, Andi Setiady; Chang, Ni-Bin

    2008-07-01

    Energy supply and use is of fundamental importance to society. Although the interactions between energy and environment were originally local in character, they have now widened to cover regional and global issues, such as acid rain and the greenhouse effect. It is for this reason that there is a need for covering the direct and indirect economic and environmental impacts of energy acquisition, transport, production and use. In this paper, particular attention is directed to ways of resolving conflict between economic and environmental goals by encouraging a power plant to consider co-firing biomass and refuse-derived fuel (RDF) with coal simultaneously. It aims at reducing the emission level of sulfur dioxide (SO(2)) in an uncertain environment, using the power plant in Michigan City, Indiana as an example. To assess the uncertainty by a comparative way both deterministic and grey nonlinear mixed integer programming (MIP) models were developed to minimize the net operating cost with respect to possible fuel combinations. It aims at generating the optimal portfolio of alternative fuels while maintaining the same electricity generation simultaneously. To ease the solution procedure stepwise relaxation algorithm was developed for solving the grey nonlinear MIP model. Breakeven alternative fuel value can be identified in the post-optimization stage for decision-making. Research findings show that the inclusion of RDF does not exhibit comparative advantage in terms of the net cost, albeit relatively lower air pollution impact. Yet it can be sustained by a charge system, subsidy program, or emission credit as the price of coal increases over time.

  17. An n -material thresholding method for improving integerness of solutions in topology optimization

    DOE PAGES

    Watts, Seth; Tortorelli, Daniel A.

    2016-04-10

    It is common in solving topology optimization problems to replace an integer-valued characteristic function design field with the material volume fraction field, a real-valued approximation of the design field that permits "fictitious" mixtures of materials during intermediate iterations in the optimization process. This is reasonable so long as one can interpolate properties for such materials and so long as the final design is integer valued. For this purpose, we present a method for smoothly thresholding the volume fractions of an arbitrary number of material phases which specify the design. This method is trivial for two-material design problems, for example, themore » canonical topology design problem of specifying the presence or absence of a single material within a domain, but it becomes more complex when three or more materials are used, as often occurs in material design problems. We take advantage of the similarity in properties between the volume fractions and the barycentric coordinates on a simplex to derive a thresholding, method which is applicable to an arbitrary number of materials. As we show in a sensitivity analysis, this method has smooth derivatives, allowing it to be used in gradient-based optimization algorithms. Finally, we present results, which show synergistic effects when used with Solid Isotropic Material with Penalty and Rational Approximation of Material Properties material interpolation functions, popular methods of ensuring integerness of solutions.« less

  18. A two-stage mixed-integer fuzzy programming with interval-valued membership functions approach for flood-diversion planning.

    PubMed

    Wang, S; Huang, G H

    2013-03-15

    Flood disasters have been extremely severe in recent decades, and they account for about one third of all natural catastrophes throughout the world. In this study, a two-stage mixed-integer fuzzy programming with interval-valued membership functions (TMFP-IMF) approach is developed for flood-diversion planning under uncertainty. TMFP-IMF integrates the fuzzy flexible programming, two-stage stochastic programming, and integer programming within a general framework. A concept of interval-valued fuzzy membership function is introduced to address complexities of system uncertainties. TMFP-IMF can not only deal with uncertainties expressed as fuzzy sets and probability distributions, but also incorporate pre-regulated water-diversion policies directly into its optimization process. TMFP-IMF is applied to a hypothetical case study of flood-diversion planning for demonstrating its applicability. Results indicate that reasonable solutions can be generated for binary and continuous variables. A variety of flood-diversion and capacity-expansion schemes can be obtained under four scenarios, which enable decision makers (DMs) to identify the most desired one based on their perceptions and attitudes towards the objective-function value and constraints. Copyright © 2013 Elsevier Ltd. All rights reserved.

  19. Optimal one-way and roundtrip journeys design by mixed-integer programming

    NASA Astrophysics Data System (ADS)

    Ribeiro, Isabel M.; Vale, Cecília

    2017-12-01

    The introduction of multimodal/intermodal networks in transportation problems, especially when considering roundtrips, adds complexity to the models. This article presents two models for the optimization of intermodal trips as a contribution to the integration of transport modes in networks. The first model is devoted to one-way trips while the second one is dedicated to roundtrips. The original contribution of this research to transportation is mainly the consideration of roundtrips in the optimization process of intermodal transport, especially because the transport mode between two nodes on the return trip should be the same as the one on the outward trip if both nodes are visited on the return trip, which is a valuable aspect for transport companies. The mathematical formulations of both models leads to mixed binary linear programs, which is not a common approach for this type of problem. In this article, as well as the model description, computational experience is included to highlight the importance and efficiency of the proposed models, which may provide a valuable tool for transport managers.

  20. A Mixed Integer Linear Program for Solving a Multiple Route Taxi Scheduling Problem

    NASA Technical Reports Server (NTRS)

    Montoya, Justin Vincent; Wood, Zachary Paul; Rathinam, Sivakumar; Malik, Waqar Ahmad

    2010-01-01

    Aircraft movements on taxiways at busy airports often create bottlenecks. This paper introduces a mixed integer linear program to solve a Multiple Route Aircraft Taxi Scheduling Problem. The outputs of the model are in the form of optimal taxi schedules, which include routing decisions for taxiing aircraft. The model extends an existing single route formulation to include routing decisions. An efficient comparison framework compares the multi-route formulation and the single route formulation. The multi-route model is exercised for east side airport surface traffic at Dallas/Fort Worth International Airport to determine if any arrival taxi time savings can be achieved by allowing arrivals to have two taxi routes: a route that crosses an active departure runway and a perimeter route that avoids the crossing. Results indicate that the multi-route formulation yields reduced arrival taxi times over the single route formulation only when a perimeter taxiway is used. In conditions where the departure aircraft are given an optimal and fixed takeoff sequence, accumulative arrival taxi time savings in the multi-route formulation can be as high as 3.6 hours more than the single route formulation. If the departure sequence is not optimal, the multi-route formulation results in less taxi time savings made over the single route formulation, but the average arrival taxi time is significantly decreased.

  1. A Two-Stage Stochastic Mixed-Integer Programming Approach to the Smart House Scheduling Problem

    NASA Astrophysics Data System (ADS)

    Ozoe, Shunsuke; Tanaka, Yoichi; Fukushima, Masao

    A “Smart House” is a highly energy-optimized house equipped with photovoltaic systems (PV systems), electric battery systems, fuel cell cogeneration systems (FC systems), electric vehicles (EVs) and so on. Smart houses are attracting much attention recently thanks to their enhanced ability to save energy by making full use of renewable energy and by achieving power grid stability despite an increased power draw for installed PV systems. Yet running a smart house's power system, with its multiple power sources and power storages, is no simple task. In this paper, we consider the problem of power scheduling for a smart house with a PV system, an FC system and an EV. We formulate the problem as a mixed integer programming problem, and then extend it to a stochastic programming problem involving recourse costs to cope with uncertain electricity demand, heat demand and PV power generation. Using our method, we seek to achieve the optimal power schedule running at the minimum expected operation cost. We present some results of numerical experiments with data on real-life demands and PV power generation to show the effectiveness of our method.

  2. Integer programming model for optimizing bus timetable using genetic algorithm

    NASA Astrophysics Data System (ADS)

    Wihartiko, F. D.; Buono, A.; Silalahi, B. P.

    2017-01-01

    Bus timetable gave an information for passengers to ensure the availability of bus services. Timetable optimal condition happened when bus trips frequency could adapt and suit with passenger demand. In the peak time, the number of bus trips would be larger than the off-peak time. If the number of bus trips were more frequent than the optimal condition, it would make a high operating cost for bus operator. Conversely, if the number of trip was less than optimal condition, it would make a bad quality service for passengers. In this paper, the bus timetabling problem would be solved by integer programming model with modified genetic algorithm. Modification was placed in the chromosomes design, initial population recovery technique, chromosomes reconstruction and chromosomes extermination on specific generation. The result of this model gave the optimal solution with accuracy 99.1%.

  3. TRACKING SIMULATIONS NEAR HALF-INTEGER RESONANCE AT PEP-II

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

    Nosochkov, Yuri

    2003-05-13

    Beam-beam simulations predict that PEP-II luminosity can be increased by operating the horizontal betatron tune near and above a half-integer resonance. However, effects of the resonance and its synchrotron sidebands significantly enhance betatron and chromatic perturbations which tend to reduce dynamic aperture. In the study, chromatic variation of horizontal tune near the resonance was minimized by optimizing local sextupoles in the Interaction Region. Dynamic aperture was calculated using tracking simulations in LEGO code. Dependence of dynamic aperture on the residual orbit, dispersion and {beta} distortion after correction was investigated.

  4. Robust automated mass spectra interpretation and chemical formula calculation using mixed integer linear programming.

    PubMed

    Baran, Richard; Northen, Trent R

    2013-10-15

    Untargeted metabolite profiling using liquid chromatography and mass spectrometry coupled via electrospray ionization is a powerful tool for the discovery of novel natural products, metabolic capabilities, and biomarkers. However, the elucidation of the identities of uncharacterized metabolites from spectral features remains challenging. A critical step in the metabolite identification workflow is the assignment of redundant spectral features (adducts, fragments, multimers) and calculation of the underlying chemical formula. Inspection of the data by experts using computational tools solving partial problems (e.g., chemical formula calculation for individual ions) can be performed to disambiguate alternative solutions and provide reliable results. However, manual curation is tedious and not readily scalable or standardized. Here we describe an automated procedure for the robust automated mass spectra interpretation and chemical formula calculation using mixed integer linear programming optimization (RAMSI). Chemical rules among related ions are expressed as linear constraints and both the spectra interpretation and chemical formula calculation are performed in a single optimization step. This approach is unbiased in that it does not require predefined sets of neutral losses and positive and negative polarity spectra can be combined in a single optimization. The procedure was evaluated with 30 experimental mass spectra and was found to effectively identify the protonated or deprotonated molecule ([M + H](+) or [M - H](-)) while being robust to the presence of background ions. RAMSI provides a much-needed standardized tool for interpreting ions for subsequent identification in untargeted metabolomics workflows.

  5. Advances in mixed-integer programming methods for chemical production scheduling.

    PubMed

    Velez, Sara; Maravelias, Christos T

    2014-01-01

    The goal of this paper is to critically review advances in the area of chemical production scheduling over the past three decades and then present two recently proposed solution methods that have led to dramatic computational enhancements. First, we present a general framework and problem classification and discuss modeling and solution methods with an emphasis on mixed-integer programming (MIP) techniques. Second, we present two solution methods: (a) a constraint propagation algorithm that allows us to compute parameters that are then used to tighten MIP scheduling models and (b) a reformulation that introduces new variables, thus leading to effective branching. We also present computational results and an example illustrating how these methods are implemented, as well as the resulting enhancements. We close with a discussion of open research challenges and future research directions.

  6. Developing optimal nurses work schedule using integer programming

    NASA Astrophysics Data System (ADS)

    Shahidin, Ainon Mardhiyah; Said, Mohd Syazwan Md; Said, Noor Hizwan Mohamad; Sazali, Noor Izatie Amaliena

    2017-08-01

    Time management is the art of arranging, organizing and scheduling one's time for the purpose of generating more effective work and productivity. Scheduling is the process of deciding how to commit resources between varieties of possible tasks. Thus, it is crucial for every organization to have a good work schedule for their staffs. The job of Ward nurses at hospitals runs for 24 hours every day. Therefore, nurses will be working using shift scheduling. This study is aimed to solve the nurse scheduling problem at an emergency ward of a private hospital. A 7-day work schedule for 7 consecutive weeks satisfying all the constraints set by the hospital will be developed using Integer Programming. The work schedule for the nurses obtained gives an optimal solution where all the constraints are being satisfied successfully.

  7. Dynamic analysis for solid waste management systems: an inexact multistage integer programming approach.

    PubMed

    Li, Yongping; Huang, Guohe

    2009-03-01

    In this study, a dynamic analysis approach based on an inexact multistage integer programming (IMIP) model is developed for supporting municipal solid waste (MSW) management under uncertainty. Techniques of interval-parameter programming and multistage stochastic programming are incorporated within an integer-programming framework. The developed IMIP can deal with uncertainties expressed as probability distributions and interval numbers, and can reflect the dynamics in terms of decisions for waste-flow allocation and facility-capacity expansion over a multistage context. Moreover, the IMIP can be used for analyzing various policy scenarios that are associated with different levels of economic consequences. The developed method is applied to a case study of long-term waste-management planning. The results indicate that reasonable solutions have been generated for binary and continuous variables. They can help generate desired decisions of system-capacity expansion and waste-flow allocation with a minimized system cost and maximized system reliability.

  8. The Capability Portfolio Analysis Tool (CPAT): A Mixed Integer Linear Programming Formulation for Fleet Modernization Analysis (Version 2.0.2).

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

    Waddell, Lucas; Muldoon, Frank; Henry, Stephen Michael

    In order to effectively plan the management and modernization of their large and diverse fleets of vehicles, Program Executive Office Ground Combat Systems (PEO GCS) and Program Executive Office Combat Support and Combat Service Support (PEO CS&CSS) commis- sioned the development of a large-scale portfolio planning optimization tool. This software, the Capability Portfolio Analysis Tool (CPAT), creates a detailed schedule that optimally prioritizes the modernization or replacement of vehicles within the fleet - respecting numerous business rules associated with fleet structure, budgets, industrial base, research and testing, etc., while maximizing overall fleet performance through time. This paper contains a thor-more » ough documentation of the terminology, parameters, variables, and constraints that comprise the fleet management mixed integer linear programming (MILP) mathematical formulation. This paper, which is an update to the original CPAT formulation document published in 2015 (SAND2015-3487), covers the formulation of important new CPAT features.« less

  9. Dynamically sculpturing plasmonic vortices: from integer to fractional orbital angular momentum

    PubMed Central

    Wang, Yu; Zhao, Peng; Feng, Xue; Xu, Yuntao; Liu, Fang; Cui, Kaiyu; Zhang, Wei; Huang, Yidong

    2016-01-01

    As a fundamental tool for light-matter interactions, plasmonic vortex (PV) is extremely useful due to the unique near field property. However, it is a pity that, up to now, the orbital angular momentum (OAM) carried by PVs could not be dynamically and continuously tuned in practice as well as the properties of fractional PVs are still not well investigated. By comparing with two previously reported methods, it is suggested that our proposal of utilizing the propagation induced radial phase gradient of incident Laguerre-Gaussian (LG) beam is a promising candidate to sculpture PVs from integer to fractional OAM dynamically. Consequently, the preset OAM of PVs could have four composing parts: the incident spin and orbital angular momentum, the geometric contribution of chiral plasmonic structure, and the radial phase gradient dependent contribution. Moreover, an analytical expression for the fractional PV is derived as a linear superposition of infinite numbers of integer PVs described by Bessel function of the first kind. It is also shown that the actual mean OAM of a fractional PV would deviate from the preset value, which is similar with previous results for spatial fractional optical vortices. PMID:27811986

  10. Modelling with Integer Variables.

    DTIC Science & Technology

    1984-01-01

    Computational Comparison of * ’Equivalent’ Mixed Integer Formulations," Naval Research Logistics Quarterly 28 (1981), pp. 115- 131 . 39. R. R, Meyer and...jE(i) 3 K ".- .e I " Z A . .,.. x jCI (i) IJ ~s ;:. ... i=I 1 1X. integer A- k . . . . . . . . . . . ... . ... . . . . . . . . . o...be such that Z X.. = 1 andIfxCi’e k jcI (i) 11 13 kx m). *x + E okv . Then by putting Xil and X.=O for j* i, j£I(i) kE (2.3.4) holds. Hence S’ Pi" As

  11. Performance comparison of genetic algorithms and particle swarm optimization for model integer programming bus timetabling problem

    NASA Astrophysics Data System (ADS)

    Wihartiko, F. D.; Wijayanti, H.; Virgantari, F.

    2018-03-01

    Genetic Algorithm (GA) is a common algorithm used to solve optimization problems with artificial intelligence approach. Similarly, the Particle Swarm Optimization (PSO) algorithm. Both algorithms have different advantages and disadvantages when applied to the case of optimization of the Model Integer Programming for Bus Timetabling Problem (MIPBTP), where in the case of MIPBTP will be found the optimal number of trips confronted with various constraints. The comparison results show that the PSO algorithm is superior in terms of complexity, accuracy, iteration and program simplicity in finding the optimal solution.

  12. Modeling optimal treatment strategies in a heterogeneous mixing model.

    PubMed

    Choe, Seoyun; Lee, Sunmi

    2015-11-25

    Many mathematical models assume random or homogeneous mixing for various infectious diseases. Homogeneous mixing can be generalized to mathematical models with multi-patches or age structure by incorporating contact matrices to capture the dynamics of the heterogeneously mixing populations. Contact or mixing patterns are difficult to measure in many infectious diseases including influenza. Mixing patterns are considered to be one of the critical factors for infectious disease modeling. A two-group influenza model is considered to evaluate the impact of heterogeneous mixing on the influenza transmission dynamics. Heterogeneous mixing between two groups with two different activity levels includes proportionate mixing, preferred mixing and like-with-like mixing. Furthermore, the optimal control problem is formulated in this two-group influenza model to identify the group-specific optimal treatment strategies at a minimal cost. We investigate group-specific optimal treatment strategies under various mixing scenarios. The characteristics of the two-group influenza dynamics have been investigated in terms of the basic reproduction number and the final epidemic size under various mixing scenarios. As the mixing patterns become proportionate mixing, the basic reproduction number becomes smaller; however, the final epidemic size becomes larger. This is due to the fact that the number of infected people increases only slightly in the higher activity level group, while the number of infected people increases more significantly in the lower activity level group. Our results indicate that more intensive treatment of both groups at the early stage is the most effective treatment regardless of the mixing scenario. However, proportionate mixing requires more treated cases for all combinations of different group activity levels and group population sizes. Mixing patterns can play a critical role in the effectiveness of optimal treatments. As the mixing becomes more like

  13. Obtaining lower bounds from the progressive hedging algorithm for stochastic mixed-integer programs

    DOE PAGES

    Gade, Dinakar; Hackebeil, Gabriel; Ryan, Sarah M.; ...

    2016-04-02

    We present a method for computing lower bounds in the progressive hedging algorithm (PHA) for two-stage and multi-stage stochastic mixed-integer programs. Computing lower bounds in the PHA allows one to assess the quality of the solutions generated by the algorithm contemporaneously. The lower bounds can be computed in any iteration of the algorithm by using dual prices that are calculated during execution of the standard PHA. In conclusion, we report computational results on stochastic unit commitment and stochastic server location problem instances, and explore the relationship between key PHA parameters and the quality of the resulting lower bounds.

  14. Mixed Integer Linear Programming model for Crude Palm Oil Supply Chain Planning

    NASA Astrophysics Data System (ADS)

    Sembiring, Pasukat; Mawengkang, Herman; Sadyadharma, Hendaru; Bu'ulolo, F.; Fajriana

    2018-01-01

    The production process of crude palm oil (CPO) can be defined as the milling process of raw materials, called fresh fruit bunch (FFB) into end products palm oil. The process usually through a series of steps producing and consuming intermediate products. The CPO milling industry considered in this paper does not have oil palm plantation, therefore the FFB are supplied by several public oil palm plantations. Due to the limited availability of FFB, then it is necessary to choose from which plantations would be appropriate. This paper proposes a mixed integer linear programming model the supply chain integrated problem, which include waste processing. The mathematical programming model is solved using neighborhood search approach.

  15. The use of integer programming to select bulls across breeding companies with volume price discounts.

    PubMed

    McConnel, M B; Galligan, D T

    2004-10-01

    Optimization programs are currently used to aid in the selection of bulls to be used in herd breeding programs. While these programs offer a systematic approach to the problem of semen selection, they ignore the impact of volume discounts. Volume discounts are discounts that vary depending on the number of straws purchased. The dynamic nature of volume discounts means that, in order to be adequately accounted for, they must be considered in the optimization routine. Failing to do this creates a missed economic opportunity because the potential benefits of optimally selecting and combining breeding company discount opportunities are not captured. To address these issues, an integer program was created which used binary decision variables to incorporate the effects of quantity discounts into the optimization program. A consistent set of trait criteria was used to select a group of bulls from 3 sample breeding companies. Three different selection programs were used to select the bulls, 2 traditional methods and the integer method. After the discounts were applied using each method, the integer program resulted in the lowest cost portfolio of bulls. A sensitivity analysis showed that the integer program also resulted in a low cost portfolio when the genetic trait goals were changed to be more or less stringent. In the sample application, a net benefit of the new approach over the traditional approaches was a 12.3 to 20.0% savings in semen cost.

  16. Interval-parameter semi-infinite fuzzy-stochastic mixed-integer programming approach for environmental management under multiple uncertainties.

    PubMed

    Guo, P; Huang, G H

    2010-03-01

    In this study, an interval-parameter semi-infinite fuzzy-chance-constrained mixed-integer linear programming (ISIFCIP) approach is developed for supporting long-term planning of waste-management systems under multiple uncertainties in the City of Regina, Canada. The method improves upon the existing interval-parameter semi-infinite programming (ISIP) and fuzzy-chance-constrained programming (FCCP) by incorporating uncertainties expressed as dual uncertainties of functional intervals and multiple uncertainties of distributions with fuzzy-interval admissible probability of violating constraint within a general optimization framework. The binary-variable solutions represent the decisions of waste-management-facility expansion, and the continuous ones are related to decisions of waste-flow allocation. The interval solutions can help decision-makers to obtain multiple decision alternatives, as well as provide bases for further analyses of tradeoffs between waste-management cost and system-failure risk. In the application to the City of Regina, Canada, two scenarios are considered. In Scenario 1, the City's waste-management practices would be based on the existing policy over the next 25 years. The total diversion rate for the residential waste would be approximately 14%. Scenario 2 is associated with a policy for waste minimization and diversion, where 35% diversion of residential waste should be achieved within 15 years, and 50% diversion over 25 years. In this scenario, not only landfill would be expanded, but also CF and MRF would be expanded. Through the scenario analyses, useful decision support for the City's solid-waste managers and decision-makers has been generated. Three special characteristics of the proposed method make it unique compared with other optimization techniques that deal with uncertainties. Firstly, it is useful for tackling multiple uncertainties expressed as intervals, functional intervals, probability distributions, fuzzy sets, and their

  17. Determining on-fault earthquake magnitude distributions from integer programming

    NASA Astrophysics Data System (ADS)

    Geist, Eric L.; Parsons, Tom

    2018-02-01

    Earthquake magnitude distributions among faults within a fault system are determined from regional seismicity and fault slip rates using binary integer programming. A synthetic earthquake catalog (i.e., list of randomly sampled magnitudes) that spans millennia is first formed, assuming that regional seismicity follows a Gutenberg-Richter relation. Each earthquake in the synthetic catalog can occur on any fault and at any location. The objective is to minimize misfits in the target slip rate for each fault, where slip for each earthquake is scaled from its magnitude. The decision vector consists of binary variables indicating which locations are optimal among all possibilities. Uncertainty estimates in fault slip rates provide explicit upper and lower bounding constraints to the problem. An implicit constraint is that an earthquake can only be located on a fault if it is long enough to contain that earthquake. A general mixed-integer programming solver, consisting of a number of different algorithms, is used to determine the optimal decision vector. A case study is presented for the State of California, where a 4 kyr synthetic earthquake catalog is created and faults with slip ≥3 mm/yr are considered, resulting in >106 variables. The optimal magnitude distributions for each of the faults in the system span a rich diversity of shapes, ranging from characteristic to power-law distributions.

  18. Determining on-fault earthquake magnitude distributions from integer programming

    USGS Publications Warehouse

    Geist, Eric L.; Parsons, Thomas E.

    2018-01-01

    Earthquake magnitude distributions among faults within a fault system are determined from regional seismicity and fault slip rates using binary integer programming. A synthetic earthquake catalog (i.e., list of randomly sampled magnitudes) that spans millennia is first formed, assuming that regional seismicity follows a Gutenberg-Richter relation. Each earthquake in the synthetic catalog can occur on any fault and at any location. The objective is to minimize misfits in the target slip rate for each fault, where slip for each earthquake is scaled from its magnitude. The decision vector consists of binary variables indicating which locations are optimal among all possibilities. Uncertainty estimates in fault slip rates provide explicit upper and lower bounding constraints to the problem. An implicit constraint is that an earthquake can only be located on a fault if it is long enough to contain that earthquake. A general mixed-integer programming solver, consisting of a number of different algorithms, is used to determine the optimal decision vector. A case study is presented for the State of California, where a 4 kyr synthetic earthquake catalog is created and faults with slip ≥3 mm/yr are considered, resulting in >106  variables. The optimal magnitude distributions for each of the faults in the system span a rich diversity of shapes, ranging from characteristic to power-law distributions. 

  19. Mixed integer nonlinear programming model of wireless pricing scheme with QoS attribute of bandwidth and end-to-end delay

    NASA Astrophysics Data System (ADS)

    Irmeilyana, Puspita, Fitri Maya; Indrawati

    2016-02-01

    The pricing for wireless networks is developed by considering linearity factors, elasticity price and price factors. Mixed Integer Nonlinear Programming of wireless pricing model is proposed as the nonlinear programming problem that can be solved optimally using LINGO 13.0. The solutions are expected to give some information about the connections between the acceptance factor and the price. Previous model worked on the model that focuses on bandwidth as the QoS attribute. The models attempt to maximize the total price for a connection based on QoS parameter. The QoS attributes used will be the bandwidth and the end to end delay that affect the traffic. The maximum goal to maximum price is achieved when the provider determine the requirement for the increment or decrement of price change due to QoS change and amount of QoS value.

  20. Integer programming for improving radiotherapy treatment efficiency.

    PubMed

    Lv, Ming; Li, Yi; Kou, Bo; Zhou, Zhili

    2017-01-01

    Patients received by radiotherapy departments are diverse and may be diagnosed with different cancers. Therefore, they need different radiotherapy treatment plans and thus have different needs for medical resources. This research aims to explore the best method of scheduling the admission of patients receiving radiotherapy so as to reduce patient loss and maximize the usage efficiency of service resources. A mix integer programming (MIP) model integrated with special features of radiotherapy is constructed. The data used here is based on the historical data collected and we propose an exact method to solve the MIP model. Compared with the traditional First Come First Served (FCFS) method, the new method has boosted patient admission as well as the usage of linear accelerators (LINAC) and beds. The integer programming model can be used to describe the complex problem of scheduling radio-receiving patients, to identify the bottleneck resources that hinder patient admission, and to obtain the optimal LINAC-bed radio under the current data conditions. Different management strategies can be implemented by adjusting the settings of the MIP model. The computational results can serve as a reference for the policy-makers in decision making.

  1. Obstacle avoidance handling and mixed integer predictive control for space robots

    NASA Astrophysics Data System (ADS)

    Zong, Lijun; Luo, Jianjun; Wang, Mingming; Yuan, Jianping

    2018-04-01

    This paper presents a novel obstacle avoidance constraint and a mixed integer predictive control (MIPC) method for space robots avoiding obstacles and satisfying physical limits during performing tasks. Firstly, a novel kind of obstacle avoidance constraint of space robots, which needs the assumption that the manipulator links and the obstacles can be represented by convex bodies, is proposed by limiting the relative velocity between two closest points which are on the manipulator and the obstacle, respectively. Furthermore, the logical variables are introduced into the obstacle avoidance constraint, which have realized the constraint form is automatically changed to satisfy different obstacle avoidance requirements in different distance intervals between the space robot and the obstacle. Afterwards, the obstacle avoidance constraint and other system physical limits, such as joint angle ranges, the amplitude boundaries of joint velocities and joint torques, are described as inequality constraints of a quadratic programming (QP) problem by using the model predictive control (MPC) method. To guarantee the feasibility of the obtained multi-constraint QP problem, the constraints are treated as soft constraints and assigned levels of priority based on the propositional logic theory, which can realize that the constraints with lower priorities are always firstly violated to recover the feasibility of the QP problem. Since the logical variables have been introduced, the optimization problem including obstacle avoidance and system physical limits as prioritized inequality constraints is termed as MIPC method of space robots, and its computational complexity as well as possible strategies for reducing calculation amount are analyzed. Simulations of the space robot unfolding its manipulator and tracking the end-effector's desired trajectories with the existence of obstacles and physical limits are presented to demonstrate the effectiveness of the proposed obstacle avoidance

  2. A Bell-Curved Based Algorithm for Mixed Continuous and Discrete Structural Optimization

    NASA Technical Reports Server (NTRS)

    Kincaid, Rex K.; Weber, Michael; Sobieszczanski-Sobieski, Jaroslaw

    2001-01-01

    An evolutionary based strategy utilizing two normal distributions to generate children is developed to solve mixed integer nonlinear programming problems. This Bell-Curve Based (BCB) evolutionary algorithm is similar in spirit to (mu + mu) evolutionary strategies and evolutionary programs but with fewer parameters to adjust and no mechanism for self adaptation. First, a new version of BCB to solve purely discrete optimization problems is described and its performance tested against a tabu search code for an actuator placement problem. Next, the performance of a combined version of discrete and continuous BCB is tested on 2-dimensional shape problems and on a minimum weight hub design problem. In the latter case the discrete portion is the choice of the underlying beam shape (I, triangular, circular, rectangular, or U).

  3. IESIP - AN IMPROVED EXPLORATORY SEARCH TECHNIQUE FOR PURE INTEGER LINEAR PROGRAMMING PROBLEMS

    NASA Technical Reports Server (NTRS)

    Fogle, F. R.

    1994-01-01

    IESIP, an Improved Exploratory Search Technique for Pure Integer Linear Programming Problems, addresses the problem of optimizing an objective function of one or more variables subject to a set of confining functions or constraints by a method called discrete optimization or integer programming. Integer programming is based on a specific form of the general linear programming problem in which all variables in the objective function and all variables in the constraints are integers. While more difficult, integer programming is required for accuracy when modeling systems with small numbers of components such as the distribution of goods, machine scheduling, and production scheduling. IESIP establishes a new methodology for solving pure integer programming problems by utilizing a modified version of the univariate exploratory move developed by Robert Hooke and T.A. Jeeves. IESIP also takes some of its technique from the greedy procedure and the idea of unit neighborhoods. A rounding scheme uses the continuous solution found by traditional methods (simplex or other suitable technique) and creates a feasible integer starting point. The Hook and Jeeves exploratory search is modified to accommodate integers and constraints and is then employed to determine an optimal integer solution from the feasible starting solution. The user-friendly IESIP allows for rapid solution of problems up to 10 variables in size (limited by DOS allocation). Sample problems compare IESIP solutions with the traditional branch-and-bound approach. IESIP is written in Borland's TURBO Pascal for IBM PC series computers and compatibles running DOS. Source code and an executable are provided. The main memory requirement for execution is 25K. This program is available on a 5.25 inch 360K MS DOS format diskette. IESIP was developed in 1990. IBM is a trademark of International Business Machines. TURBO Pascal is registered by Borland International.

  4. Learning oncogenetic networks by reducing to mixed integer linear programming.

    PubMed

    Shahrabi Farahani, Hossein; Lagergren, Jens

    2013-01-01

    Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog.

  5. RSM 1.0 - A RESUPPLY SCHEDULER USING INTEGER OPTIMIZATION

    NASA Technical Reports Server (NTRS)

    Viterna, L. A.

    1994-01-01

    RSM, Resupply Scheduling Modeler, is a fully menu-driven program that uses integer programming techniques to determine an optimum schedule for replacing components on or before the end of a fixed replacement period. Although written to analyze the electrical power system on the Space Station Freedom, RSM is quite general and can be used to model the resupply of almost any system subject to user-defined resource constraints. RSM is based on a specific form of the general linear programming problem in which all variables in the objective function and all variables in the constraints are integers. While more computationally intensive, integer programming was required for accuracy when modeling systems with small quantities of components. Input values for component life cane be real numbers, RSM converts them to integers by dividing the lifetime by the period duration, then reducing the result to the next lowest integer. For each component, there is a set of constraints that insure that it is replaced before its lifetime expires. RSM includes user-defined constraints such as transportation mass and volume limits, as well as component life, available repair crew time and assembly sequences. A weighting factor allows the program to minimize factors such as cost. The program then performs an iterative analysis, which is displayed during the processing. A message gives the first period in which resources are being exceeded on each iteration. If the scheduling problem is unfeasible, the final message will also indicate the first period in which resources were exceeded. RSM is written in APL2 for IBM PC series computers and compatibles. A stand-alone executable version of RSM is provided; however, this is a "packed" version of RSM which can only utilize the memory within the 640K DOS limit. This executable requires at least 640K of memory and DOS 3.1 or higher. Source code for an APL2/PC workspace version is also provided. This version of RSM can make full use of any

  6. Optimization of rotor blades for combined structural, dynamic, and aerodynamic properties

    NASA Technical Reports Server (NTRS)

    He, Cheng-Jian; Peters, David A.

    1990-01-01

    Optimal helicopter blade design with computer-based mathematical programming has received more and more attention in recent years. Most of the research has focused on optimum dynamic characteristics of rotor blades to reduce vehicle vibration. There is also work on optimization of aerodynamic performance and on composite structural design. This research has greatly increased our understanding of helicopter optimum design in each of these aspects. Helicopter design is an inherently multidisciplinary process involving strong interactions among various disciplines which can appropriately include aerodynamics; dynamics, both flight dynamics and structural dynamics; aeroelasticity: vibrations and stability; and even acoustics. Therefore, the helicopter design process must satisfy manifold requirements related to the aforementioned diverse disciplines. In our present work, we attempt to combine several of these important effects in a unified manner. First, we design a blade with optimum aerodynamic performance by proper layout of blade planform and spanwise twist. Second, the blade is designed to have natural frequencies that are placed away from integer multiples of the rotor speed for a good dynamic characteristics. Third, the structure is made as light as possible with sufficient rotational inertia to allow for autorotational landing, with safe stress margins and flight fatigue life at each cross-section, and with aeroelastical stability and low vibrations. Finally, a unified optimization refines the solution.

  7. Optimized Waterspace Management and Scheduling Using Mixed-Integer Linear Programming

    DTIC Science & Technology

    2016-01-01

    Complete [30]. Proposition 4.1 satisfies the first criterion. For the second criterion, we will use the Traveling Salesman Problem (TSP), which has been...A branch and cut algorithm for the symmetric generalized traveling salesman problem , Operations Research 45 (1997) 378–394. [33] J. Silberholz, B...Golden, The generalized traveling salesman problem : A new genetic algorithm ap- proach, Extended Horizons: Advances in Computing, Optimization, and

  8. Comparison of two non-convex mixed-integer nonlinear programming algorithms applied to autoregressive moving average model structure and parameter estimation

    NASA Astrophysics Data System (ADS)

    Uilhoorn, F. E.

    2016-10-01

    In this article, the stochastic modelling approach proposed by Box and Jenkins is treated as a mixed-integer nonlinear programming (MINLP) problem solved with a mesh adaptive direct search and a real-coded genetic class of algorithms. The aim is to estimate the real-valued parameters and non-negative integer, correlated structure of stationary autoregressive moving average (ARMA) processes. The maximum likelihood function of the stationary ARMA process is embedded in Akaike's information criterion and the Bayesian information criterion, whereas the estimation procedure is based on Kalman filter recursions. The constraints imposed on the objective function enforce stability and invertibility. The best ARMA model is regarded as the global minimum of the non-convex MINLP problem. The robustness and computational performance of the MINLP solvers are compared with brute-force enumeration. Numerical experiments are done for existing time series and one new data set.

  9. Integers as Transformations.

    ERIC Educational Resources Information Center

    Thompson, Patrick W.; Dreyfus, Tommy

    1988-01-01

    Investigates whether elementary school students can construct operations of thought for integers and integer addition crucial for understanding elementary algebra. Two sixth graders were taught using a computer. Results included both students being able to construct mental operations for negating arbitrary integers and determining sign and…

  10. Logic integer programming models for signaling networks.

    PubMed

    Haus, Utz-Uwe; Niermann, Kathrin; Truemper, Klaus; Weismantel, Robert

    2009-05-01

    We propose a static and a dynamic approach to model biological signaling networks, and show how each can be used to answer relevant biological questions. For this, we use the two different mathematical tools of Propositional Logic and Integer Programming. The power of discrete mathematics for handling qualitative as well as quantitative data has so far not been exploited in molecular biology, which is mostly driven by experimental research, relying on first-order or statistical models. The arising logic statements and integer programs are analyzed and can be solved with standard software. For a restricted class of problems the logic models reduce to a polynomial-time solvable satisfiability algorithm. Additionally, a more dynamic model enables enumeration of possible time resolutions in poly-logarithmic time. Computational experiments are included.

  11. Dynamic Optimization

    NASA Technical Reports Server (NTRS)

    Laird, Philip

    1992-01-01

    We distinguish static and dynamic optimization of programs: whereas static optimization modifies a program before runtime and is based only on its syntactical structure, dynamic optimization is based on the statistical properties of the input source and examples of program execution. Explanation-based generalization is a commonly used dynamic optimization method, but its effectiveness as a speedup-learning method is limited, in part because it fails to separate the learning process from the program transformation process. This paper describes a dynamic optimization technique called a learn-optimize cycle that first uses a learning element to uncover predictable patterns in the program execution and then uses an optimization algorithm to map these patterns into beneficial transformations. The technique has been used successfully for dynamic optimization of pure Prolog.

  12. Large-scale bi-level strain design approaches and mixed-integer programming solution techniques.

    PubMed

    Kim, Joonhoon; Reed, Jennifer L; Maravelias, Christos T

    2011-01-01

    The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ∼10 days to ∼5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution

  13. Online with Integers

    ERIC Educational Resources Information Center

    Siegel, Jonathan W.; Siegel, P. B.

    2011-01-01

    Integers are sometimes used in physics problems to simplify the mathematics so the arithmetic does not distract students from the physics concepts. This is particularly important in exams where students should not have to spend a lot of time using their calculators. Common uses of integers in physics problems include integer solutions to…

  14. A two-level approach to large mixed-integer programs with application to cogeneration in energy-efficient buildings

    DOE PAGES

    Lin, Fu; Leyffer, Sven; Munson, Todd

    2016-04-12

    We study a two-stage mixed-integer linear program (MILP) with more than 1 million binary variables in the second stage. We develop a two-level approach by constructing a semi-coarse model that coarsens with respect to variables and a coarse model that coarsens with respect to both variables and constraints. We coarsen binary variables by selecting a small number of prespecified on/off profiles. We aggregate constraints by partitioning them into groups and taking convex combination over each group. With an appropriate choice of coarsened profiles, the semi-coarse model is guaranteed to find a feasible solution of the original problem and hence providesmore » an upper bound on the optimal solution. We show that solving a sequence of coarse models converges to the same upper bound with proven finite steps. This is achieved by adding violated constraints to coarse models until all constraints in the semi-coarse model are satisfied. We demonstrate the effectiveness of our approach in cogeneration for buildings. Here, the coarsened models allow us to obtain good approximate solutions at a fraction of the time required by solving the original problem. Extensive numerical experiments show that the two-level approach scales to large problems that are beyond the capacity of state-of-the-art commercial MILP solvers.« less

  15. A two-level approach to large mixed-integer programs with application to cogeneration in energy-efficient buildings

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

    Lin, Fu; Leyffer, Sven; Munson, Todd

    We study a two-stage mixed-integer linear program (MILP) with more than 1 million binary variables in the second stage. We develop a two-level approach by constructing a semi-coarse model that coarsens with respect to variables and a coarse model that coarsens with respect to both variables and constraints. We coarsen binary variables by selecting a small number of prespecified on/off profiles. We aggregate constraints by partitioning them into groups and taking convex combination over each group. With an appropriate choice of coarsened profiles, the semi-coarse model is guaranteed to find a feasible solution of the original problem and hence providesmore » an upper bound on the optimal solution. We show that solving a sequence of coarse models converges to the same upper bound with proven finite steps. This is achieved by adding violated constraints to coarse models until all constraints in the semi-coarse model are satisfied. We demonstrate the effectiveness of our approach in cogeneration for buildings. Here, the coarsened models allow us to obtain good approximate solutions at a fraction of the time required by solving the original problem. Extensive numerical experiments show that the two-level approach scales to large problems that are beyond the capacity of state-of-the-art commercial MILP solvers.« less

  16. Automated Design Framework for Synthetic Biology Exploiting Pareto Optimality.

    PubMed

    Otero-Muras, Irene; Banga, Julio R

    2017-07-21

    In this work we consider Pareto optimality for automated design in synthetic biology. We present a generalized framework based on a mixed-integer dynamic optimization formulation that, given design specifications, allows the computation of Pareto optimal sets of designs, that is, the set of best trade-offs for the metrics of interest. We show how this framework can be used for (i) forward design, that is, finding the Pareto optimal set of synthetic designs for implementation, and (ii) reverse design, that is, analyzing and inferring motifs and/or design principles of gene regulatory networks from the Pareto set of optimal circuits. Finally, we illustrate the capabilities and performance of this framework considering four case studies. In the first problem we consider the forward design of an oscillator. In the remaining problems, we illustrate how to apply the reverse design approach to find motifs for stripe formation, rapid adaption, and fold-change detection, respectively.

  17. Edge mixing dynamics in graphene p–n junctions in the quantum Hall regime

    PubMed Central

    Matsuo, Sadashige; Takeshita, Shunpei; Tanaka, Takahiro; Nakaharai, Shu; Tsukagoshi, Kazuhito; Moriyama, Takahiro; Ono, Teruo; Kobayashi, Kensuke

    2015-01-01

    Massless Dirac electron systems such as graphene exhibit a distinct half-integer quantum Hall effect, and in the bipolar transport regime co-propagating edge states along the p–n junction are realized. Additionally, these edge states are uniformly mixed at the junction, which makes it a unique structure to partition electrons in these edge states. Although many experimental works have addressed this issue, the microscopic dynamics of electron partition in this peculiar structure remains unclear. Here we performed shot-noise measurements on the junction in the quantum Hall regime as well as at zero magnetic field. We found that, in sharp contrast with the zero-field case, the shot noise in the quantum Hall regime is finite in the bipolar regime, but is strongly suppressed in the unipolar regime. Our observation is consistent with the theoretical prediction and gives microscopic evidence that the edge states are uniquely mixed along the p–n junction. PMID:26337445

  18. Mixed-Strategy Chance Constrained Optimal Control

    NASA Technical Reports Server (NTRS)

    Ono, Masahiro; Kuwata, Yoshiaki; Balaram, J.

    2013-01-01

    This paper presents a novel chance constrained optimal control (CCOC) algorithm that chooses a control action probabilistically. A CCOC problem is to find a control input that minimizes the expected cost while guaranteeing that the probability of violating a set of constraints is below a user-specified threshold. We show that a probabilistic control approach, which we refer to as a mixed control strategy, enables us to obtain a cost that is better than what deterministic control strategies can achieve when the CCOC problem is nonconvex. The resulting mixed-strategy CCOC problem turns out to be a convexification of the original nonconvex CCOC problem. Furthermore, we also show that a mixed control strategy only needs to "mix" up to two deterministic control actions in order to achieve optimality. Building upon an iterative dual optimization, the proposed algorithm quickly converges to the optimal mixed control strategy with a user-specified tolerance.

  19. An Improved Search Approach for Solving Non-Convex Mixed-Integer Non Linear Programming Problems

    NASA Astrophysics Data System (ADS)

    Sitopu, Joni Wilson; Mawengkang, Herman; Syafitri Lubis, Riri

    2018-01-01

    The nonlinear mathematical programming problem addressed in this paper has a structure characterized by a subset of variables restricted to assume discrete values, which are linear and separable from the continuous variables. The strategy of releasing nonbasic variables from their bounds, combined with the “active constraint” method, has been developed. This strategy is used to force the appropriate non-integer basic variables to move to their neighbourhood integer points. Successful implementation of these algorithms was achieved on various test problems.

  20. New numerical methods for open-loop and feedback solutions to dynamic optimization problems

    NASA Astrophysics Data System (ADS)

    Ghosh, Pradipto

    The topic of the first part of this research is trajectory optimization of dynamical systems via computational swarm intelligence. Particle swarm optimization is a nature-inspired heuristic search method that relies on a group of potential solutions to explore the fitness landscape. Conceptually, each particle in the swarm uses its own memory as well as the knowledge accumulated by the entire swarm to iteratively converge on an optimal or near-optimal solution. It is relatively straightforward to implement and unlike gradient-based solvers, does not require an initial guess or continuity in the problem definition. Although particle swarm optimization has been successfully employed in solving static optimization problems, its application in dynamic optimization, as posed in optimal control theory, is still relatively new. In the first half of this thesis particle swarm optimization is used to generate near-optimal solutions to several nontrivial trajectory optimization problems including thrust programming for minimum fuel, multi-burn spacecraft orbit transfer, and computing minimum-time rest-to-rest trajectories for a robotic manipulator. A distinct feature of the particle swarm optimization implementation in this work is the runtime selection of the optimal solution structure. Optimal trajectories are generated by solving instances of constrained nonlinear mixed-integer programming problems with the swarming technique. For each solved optimal programming problem, the particle swarm optimization result is compared with a nearly exact solution found via a direct method using nonlinear programming. Numerical experiments indicate that swarm search can locate solutions to very great accuracy. The second half of this research develops a new extremal-field approach for synthesizing nearly optimal feedback controllers for optimal control and two-player pursuit-evasion games described by general nonlinear differential equations. A notable revelation from this development

  1. Designing optimal cell factories: integer programming couples elementary mode analysis with regulation

    PubMed Central

    2012-01-01

    Background Elementary mode (EM) analysis is ideally suited for metabolic engineering as it allows for an unbiased decomposition of metabolic networks in biologically meaningful pathways. Recently, constrained minimal cut sets (cMCS) have been introduced to derive optimal design strategies for strain improvement by using the full potential of EM analysis. However, this approach does not allow for the inclusion of regulatory information. Results Here we present an alternative, novel and simple method for the prediction of cMCS, which allows to account for boolean transcriptional regulation. We use binary linear programming and show that the design of a regulated, optimal metabolic network of minimal functionality can be formulated as a standard optimization problem, where EM and regulation show up as constraints. We validated our tool by optimizing ethanol production in E. coli. Our study showed that up to 70% of the predicted cMCS contained non-enzymatic, non-annotated reactions, which are difficult to engineer. These cMCS are automatically excluded by our approach utilizing simple weight functions. Finally, due to efficient preprocessing, the binary program remains computationally feasible. Conclusions We used integer programming to predict efficient deletion strategies to metabolically engineer a production organism. Our formulation utilizes the full potential of cMCS but adds additional flexibility to the design process. In particular our method allows to integrate regulatory information into the metabolic design process and explicitly favors experimentally feasible deletions. Our method remains manageable even if millions or potentially billions of EM enter the analysis. We demonstrated that our approach is able to correctly predict the most efficient designs for ethanol production in E. coli. PMID:22898474

  2. Smart-Grid Backbone Network Real-Time Delay Reduction via Integer Programming.

    PubMed

    Pagadrai, Sasikanth; Yilmaz, Muhittin; Valluri, Pratyush

    2016-08-01

    This research investigates an optimal delay-based virtual topology design using integer linear programming (ILP), which is applied to the current backbone networks such as smart-grid real-time communication systems. A network traffic matrix is applied and the corresponding virtual topology problem is solved using the ILP formulations that include a network delay-dependent objective function and lightpath routing, wavelength assignment, wavelength continuity, flow routing, and traffic loss constraints. The proposed optimization approach provides an efficient deterministic integration of intelligent sensing and decision making, and network learning features for superior smart grid operations by adaptively responding the time-varying network traffic data as well as operational constraints to maintain optimal virtual topologies. A representative optical backbone network has been utilized to demonstrate the proposed optimization framework whose simulation results indicate that superior smart-grid network performance can be achieved using commercial networks and integer programming.

  3. Integer-ambiguity resolution in astronomy and geodesy

    NASA Astrophysics Data System (ADS)

    Lannes, A.; Prieur, J.-L.

    2014-02-01

    Recent theoretical developments in astronomical aperture synthesis have revealed the existence of integer-ambiguity problems. Those problems, which appear in the self-calibration procedures of radio imaging, have been shown to be similar to the nearest-lattice point (NLP) problems encountered in high-precision geodetic positioning and in global navigation satellite systems. In this paper we analyse the theoretical aspects of the matter and propose new methods for solving those NLP~problems. The related optimization aspects concern both the preconditioning stage, and the discrete-search stage in which the integer ambiguities are finally fixed. Our algorithms, which are described in an explicit manner, can easily be implemented. They lead to substantial gains in the processing time of both stages. Their efficiency was shown via intensive numerical tests.

  4. Large-Scale Bi-Level Strain Design Approaches and Mixed-Integer Programming Solution Techniques

    PubMed Central

    Kim, Joonhoon; Reed, Jennifer L.; Maravelias, Christos T.

    2011-01-01

    The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ∼10 days to ∼5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution

  5. Optimal Control of Evolution Mixed Variational Inclusions

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

    Alduncin, Gonzalo, E-mail: alduncin@geofisica.unam.mx

    2013-12-15

    Optimal control problems of primal and dual evolution mixed variational inclusions, in reflexive Banach spaces, are studied. The solvability analysis of the mixed state systems is established via duality principles. The optimality analysis is performed in terms of perturbation conjugate duality methods, and proximation penalty-duality algorithms to mixed optimality conditions are further presented. Applications to nonlinear diffusion constrained problems as well as quasistatic elastoviscoplastic bilateral contact problems exemplify the theory.

  6. A dynamic scheduling method of Earth-observing satellites by employing rolling horizon strategy.

    PubMed

    Dishan, Qiu; Chuan, He; Jin, Liu; Manhao, Ma

    2013-01-01

    Focused on the dynamic scheduling problem for earth-observing satellites (EOS), an integer programming model is constructed after analyzing the main constraints. The rolling horizon (RH) strategy is proposed according to the independent arriving time and deadline of the imaging tasks. This strategy is designed with a mixed triggering mode composed of periodical triggering and event triggering, and the scheduling horizon is decomposed into a series of static scheduling intervals. By optimizing the scheduling schemes in each interval, the dynamic scheduling of EOS is realized. We also propose three dynamic scheduling algorithms by the combination of the RH strategy and various heuristic algorithms. Finally, the scheduling results of different algorithms are compared and the presented methods in this paper are demonstrated to be efficient by extensive experiments.

  7. Integer Linear Programming in Computational Biology

    NASA Astrophysics Data System (ADS)

    Althaus, Ernst; Klau, Gunnar W.; Kohlbacher, Oliver; Lenhof, Hans-Peter; Reinert, Knut

    Computational molecular biology (bioinformatics) is a young research field that is rich in NP-hard optimization problems. The problem instances encountered are often huge and comprise thousands of variables. Since their introduction into the field of bioinformatics in 1997, integer linear programming (ILP) techniques have been successfully applied to many optimization problems. These approaches have added much momentum to development and progress in related areas. In particular, ILP-based approaches have become a standard optimization technique in bioinformatics. In this review, we present applications of ILP-based techniques developed by members and former members of Kurt Mehlhorn’s group. These techniques were introduced to bioinformatics in a series of papers and popularized by demonstration of their effectiveness and potential.

  8. Distributed mixed-integer fuzzy hierarchical programming for municipal solid waste management. Part II: scheme analysis and mechanism revelation.

    PubMed

    Cheng, Guanhui; Huang, Guohe; Dong, Cong; Xu, Ye; Chen, Jiapei; Chen, Xiujuan; Li, Kailong

    2017-03-01

    As presented in the first companion paper, distributed mixed-integer fuzzy hierarchical programming (DMIFHP) was developed for municipal solid waste management (MSWM) under complexities of heterogeneities, hierarchy, discreteness, and interactions. Beijing was selected as a representative case. This paper focuses on presenting the obtained schemes and the revealed mechanisms of the Beijing MSWM system. The optimal MSWM schemes for Beijing under various solid waste treatment policies and their differences are deliberated. The impacts of facility expansion, hierarchy, and spatial heterogeneities and potential extensions of DMIFHP are also discussed. A few of findings are revealed from the results and a series of comparisons and analyses. For instance, DMIFHP is capable of robustly reflecting these complexities in MSWM systems, especially for Beijing. The optimal MSWM schemes are of fragmented patterns due to the dominant role of the proximity principle in allocating solid waste treatment resources, and they are closely related to regulated ratios of landfilling, incineration, and composting. Communities without significant differences among distances to different types of treatment facilities are more sensitive to these ratios than others. The complexities of hierarchy and heterogeneities pose significant impacts on MSWM practices. Spatial dislocation of MSW generation rates and facility capacities caused by unreasonable planning in the past may result in insufficient utilization of treatment capacities under substantial influences of transportation costs. The problems of unreasonable MSWM planning, e.g., severe imbalance among different technologies and complete vacancy of ten facilities, should be gained deliberation of the public and the municipal or local governments in Beijing. These findings are helpful for gaining insights into MSWM systems under these complexities, mitigating key challenges in the planning of these systems, improving the related management

  9. Optimising the selection of food items for FFQs using Mixed Integer Linear Programming.

    PubMed

    Gerdessen, Johanna C; Souverein, Olga W; van 't Veer, Pieter; de Vries, Jeanne Hm

    2015-01-01

    To support the selection of food items for FFQs in such a way that the amount of information on all relevant nutrients is maximised while the food list is as short as possible. Selection of the most informative food items to be included in FFQs was modelled as a Mixed Integer Linear Programming (MILP) model. The methodology was demonstrated for an FFQ with interest in energy, total protein, total fat, saturated fat, monounsaturated fat, polyunsaturated fat, total carbohydrates, mono- and disaccharides, dietary fibre and potassium. The food lists generated by the MILP model have good performance in terms of length, coverage and R 2 (explained variance) of all nutrients. MILP-generated food lists were 32-40 % shorter than a benchmark food list, whereas their quality in terms of R 2 was similar to that of the benchmark. The results suggest that the MILP model makes the selection process faster, more standardised and transparent, and is especially helpful in coping with multiple nutrients. The complexity of the method does not increase with increasing number of nutrients. The generated food lists appear either shorter or provide more information than a food list generated without the MILP model.

  10. A logic-based method for integer programming

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

    Hooker, J.; Natraj, N.R.

    1994-12-31

    We propose a logic-based approach to integer programming that replaces traditional branch-and-cut techniques with logical analogs. Integer variables are regarded as atomic propositions. The constraints give rise to logical formulas that are analogous to separating cuts. No continuous relaxation is used. Rather, the cuts are selected so that they can be easily solved as a discrete relaxation. (In fact, defining a relaxation and generating cuts are best seen as the same problem.) We experiment with relaxations that have a k-tree structure and can be solved by nonserial dynamic programming. We also present logic-based analogs of facet-defining cuts, Chv{acute a}tal rank,more » etc. We conclude with some preliminary computational results.« less

  11. Microgrid Optimal Scheduling With Chance-Constrained Islanding Capability

    DOE PAGES

    Liu, Guodong; Starke, Michael R.; Xiao, B.; ...

    2017-01-13

    To facilitate the integration of variable renewable generation and improve the resilience of electricity sup-ply in a microgrid, this paper proposes an optimal scheduling strategy for microgrid operation considering constraints of islanding capability. A new concept, probability of successful islanding (PSI), indicating the probability that a microgrid maintains enough spinning reserve (both up and down) to meet local demand and accommodate local renewable generation after instantaneously islanding from the main grid, is developed. The PSI is formulated as mixed-integer linear program using multi-interval approximation taking into account the probability distributions of forecast errors of wind, PV and load. With themore » goal of minimizing the total operating cost while preserving user specified PSI, a chance-constrained optimization problem is formulated for the optimal scheduling of mirogrids and solved by mixed integer linear programming (MILP). Numerical simulations on a microgrid consisting of a wind turbine, a PV panel, a fuel cell, a micro-turbine, a diesel generator and a battery demonstrate the effectiveness of the proposed scheduling strategy. Lastly, we verify the relationship between PSI and various factors.« less

  12. Community Microgrid Scheduling Considering Network Operational Constraints and Building Thermal Dynamics

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

    Liu, Guodong; Ollis, Thomas B.; Xiao, Bailu

    Here, this paper proposes a Mixed Integer Conic Programming (MICP) model for community microgrids considering the network operational constraints and building thermal dynamics. The proposed optimization model optimizes not only the operating cost, including fuel cost, purchasing cost, battery degradation cost, voluntary load shedding cost and the cost associated with customer discomfort due to room temperature deviation from the set point, but also several performance indices, including voltage deviation, network power loss and power factor at the Point of Common Coupling (PCC). In particular, the detailed thermal dynamic model of buildings is integrated into the distribution optimal power flow (D-OPF)more » model for the optimal operation of community microgrids. The heating, ventilation and air-conditioning (HVAC) systems can be scheduled intelligently to reduce the electricity cost while maintaining the indoor temperature in the comfort range set by customers. Numerical simulation results show the effectiveness of the proposed model and significant saving in electricity cost could be achieved with network operational constraints satisfied.« less

  13. Community Microgrid Scheduling Considering Network Operational Constraints and Building Thermal Dynamics

    DOE PAGES

    Liu, Guodong; Ollis, Thomas B.; Xiao, Bailu; ...

    2017-10-10

    Here, this paper proposes a Mixed Integer Conic Programming (MICP) model for community microgrids considering the network operational constraints and building thermal dynamics. The proposed optimization model optimizes not only the operating cost, including fuel cost, purchasing cost, battery degradation cost, voluntary load shedding cost and the cost associated with customer discomfort due to room temperature deviation from the set point, but also several performance indices, including voltage deviation, network power loss and power factor at the Point of Common Coupling (PCC). In particular, the detailed thermal dynamic model of buildings is integrated into the distribution optimal power flow (D-OPF)more » model for the optimal operation of community microgrids. The heating, ventilation and air-conditioning (HVAC) systems can be scheduled intelligently to reduce the electricity cost while maintaining the indoor temperature in the comfort range set by customers. Numerical simulation results show the effectiveness of the proposed model and significant saving in electricity cost could be achieved with network operational constraints satisfied.« less

  14. Selective Optimization

    DTIC Science & Technology

    2015-07-06

    NUMBER 5b. GRANT NUMBER AFOSR FA9550-12-1-0154 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Shabbir Ahmed and Santanu S. Dey 5d. PROJECT NUMBER 5e. TASK...standard mixed-integer programming (MIP) formulations of selective optimization problems. While such formulations can be attacked by commercial...F33615-86-C-5169. 5b. GRANT NUMBER. Enter all grant numbers as they appear in the report, e.g. AFOSR-82-1234. 5c. PROGRAM ELEMENT NUMBER. Enter

  15. A Mixed-Integer Linear Programming Problem which is Efficiently Solvable.

    DTIC Science & Technology

    1987-10-01

    INTEGER LINEAR PROGRAMMING PROBLEM WHICH IS EFFICIENTLY SOLVABLE 12. PERSONAL AUTHOR(S) Leiserson, Charles, and Saxe, James B. 13a. TYPE OF REPORT j13b TIME...ger prongramn rg versions or the problem is not ac’hievable in genieral for sparse inistancves of’ P rolem(r Mi. Th le remrai nder or thris paper is...rClazes c:oIh edge (i,I*) by comlpli urg +- rnirr(z 3, ,x + a,j). A sirnI) le analysis (11 vto Nei [131 indicates why whe Iellinan-Ford algorithm works

  16. Stochastic Optimization for Unit Commitment-A Review

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

    Zheng, Qipeng P.; Wang, Jianhui; Liu, Andrew L.

    2015-07-01

    Optimization models have been widely used in the power industry to aid the decision-making process of scheduling and dispatching electric power generation resources, a process known as unit commitment (UC). Since UC's birth, there have been two major waves of revolution on UC research and real life practice. The first wave has made mixed integer programming stand out from the early solution and modeling approaches for deterministic UC, such as priority list, dynamic programming, and Lagrangian relaxation. With the high penetration of renewable energy, increasing deregulation of the electricity industry, and growing demands on system reliability, the next wave ismore » focused on transitioning from traditional deterministic approaches to stochastic optimization for unit commitment. Since the literature has grown rapidly in the past several years, this paper is to review the works that have contributed to the modeling and computational aspects of stochastic optimization (SO) based UC. Relevant lines of future research are also discussed to help transform research advances into real-world applications.« less

  17. A solution procedure for mixed-integer nonlinear programming formulation of supply chain planning with quantity discounts under demand uncertainty

    NASA Astrophysics Data System (ADS)

    Yin, Sisi; Nishi, Tatsushi

    2014-11-01

    Quantity discount policy is decision-making for trade-off prices between suppliers and manufacturers while production is changeable due to demand fluctuations in a real market. In this paper, quantity discount models which consider selection of contract suppliers, production quantity and inventory simultaneously are addressed. The supply chain planning problem with quantity discounts under demand uncertainty is formulated as a mixed-integer nonlinear programming problem (MINLP) with integral terms. We apply an outer-approximation method to solve MINLP problems. In order to improve the efficiency of the proposed method, the problem is reformulated as a stochastic model replacing the integral terms by using a normalisation technique. We present numerical examples to demonstrate the efficiency of the proposed method.

  18. A mixed integer linear programming model for operational planning of a biodiesel supply chain network from used cooking oil

    NASA Astrophysics Data System (ADS)

    Jonrinaldi, Hadiguna, Rika Ampuh; Salastino, Rades

    2017-11-01

    Environmental consciousness has paid many attention nowadays. It is not only about how to recycle, remanufacture or reuse used end products but it is also how to optimize the operations of the reverse system. A previous research has proposed a design of reverse supply chain of biodiesel network from used cooking oil. However, the research focused on the design of the supply chain strategy not the operations of the supply chain. It only decided how to design the structure of the supply chain in the next few years, and the process of each stage will be conducted in the supply chain system in general. The supply chain system has not considered operational policies to be conducted by the companies in the supply chain. Companies need a policy for each stage of the supply chain operations to be conducted so as to produce the optimal supply chain system, including how to use all the resources that have been designed in order to achieve the objectives of the supply chain system. Therefore, this paper proposes a model to optimize the operational planning of a biodiesel supply chain network from used cooking oil. A mixed integer linear programming is developed to model the operational planning of biodiesel supply chain in order to minimize the total operational cost of the supply chain. Based on the implementation of the model developed, the total operational cost of the biodiesel supply chain incurred by the system is less than the total operational cost of supply chain based on the previous research during seven days of operational planning about amount of 2,743,470.00 or 0.186%. Production costs contributed to 74.6 % of total operational cost and the cost of purchasing the used cooking oil contributed to 24.1 % of total operational cost. So, the system should pay more attention to these two aspects as changes in the value of these aspects will cause significant effects to the change in the total operational cost of the supply chain.

  19. Rapid microfluidic mixing and liquid jets for studying biomolecular chemical dynamics

    NASA Astrophysics Data System (ADS)

    Langley, Daniel; Abbey, Brian

    2018-01-01

    X-ray Free-Electron Lasers (XFELs) offer a unique opportunity to study the structural dynamics of proteins on a femtosecond time-scale. To realize the full potential of XFEL sources for studying time-resolved biomolecular processes however, requires the optimization and development of devices that can both act as a trigger and a delivery mechanism for the system of interest. Here we present numerical simulations and actual devices exploring the conditions required for the development of successful mixing and injection devices for tracking the molecular dynamics of proteins in solution on micro to nanosecond timescales using XFELs. The mechanism for combining reagents employs a threefold combination of pico-liter volumes, lamination and serpentine mixing. Focusing and delivering the sample in solution is achieved using the Gas Dynamic Virtual Nozzle (GDVN), which was specifically developed to produce a micrometer diameter, in-vacuum liquid jet. We explore the influence of parameters such as flow rate and gas pressure on the mixing time and jet stability, and explore the formation of rapid homogeneously mixed jets for `mix-and-inject' liquid scattering experiments at Synchrotron and XFEL facilities.

  20. A Dynamic Scheduling Method of Earth-Observing Satellites by Employing Rolling Horizon Strategy

    PubMed Central

    Dishan, Qiu; Chuan, He; Jin, Liu; Manhao, Ma

    2013-01-01

    Focused on the dynamic scheduling problem for earth-observing satellites (EOS), an integer programming model is constructed after analyzing the main constraints. The rolling horizon (RH) strategy is proposed according to the independent arriving time and deadline of the imaging tasks. This strategy is designed with a mixed triggering mode composed of periodical triggering and event triggering, and the scheduling horizon is decomposed into a series of static scheduling intervals. By optimizing the scheduling schemes in each interval, the dynamic scheduling of EOS is realized. We also propose three dynamic scheduling algorithms by the combination of the RH strategy and various heuristic algorithms. Finally, the scheduling results of different algorithms are compared and the presented methods in this paper are demonstrated to be efficient by extensive experiments. PMID:23690742

  1. Turbulence and mixing from optimal perturbations to a stratified shear layer

    NASA Astrophysics Data System (ADS)

    Kaminski, Alexis; Caulfield, C. P.; Taylor, John

    2014-11-01

    The stability and mixing of stratified shear layers is a canonical problem in fluid dynamics with relevance to flows in the ocean and atmosphere. The Miles-Howard theorem states that a necessary condition for normal-mode instability in parallel, inviscid, steady stratified shear flows is that the gradient Richardson number, Rig is less than 1/4 somewhere in the flow. However, substantial transient growth of non-normal modes may be possible at finite times even when Rig > 1 / 4 everywhere in the flow. We have calculated the ``optimal perturbations'' associated with maximum perturbation energy gain for a stably-stratified shear layer. These optimal perturbations are then used to initialize direct numerical simulations. For small but finite perturbation amplitudes, the optimal perturbations grow at the predicted linear rate initially, but then experience sufficient transient growth to become nonlinear and susceptible to secondary instabilities, which then break down into turbulence. Remarkably, this occurs even in flows for which Rig > 1 / 4 everywhere. We will describe the nonlinear evolution of the optimal perturbations and characterize the resulting turbulence and mixing.

  2. Wind Farm Turbine Type and Placement Optimization

    NASA Astrophysics Data System (ADS)

    Graf, Peter; Dykes, Katherine; Scott, George; Fields, Jason; Lunacek, Monte; Quick, Julian; Rethore, Pierre-Elouan

    2016-09-01

    The layout of turbines in a wind farm is already a challenging nonlinear, nonconvex, nonlinearly constrained continuous global optimization problem. Here we begin to address the next generation of wind farm optimization problems by adding the complexity that there is more than one turbine type to choose from. The optimization becomes a nonlinear constrained mixed integer problem, which is a very difficult class of problems to solve. This document briefly summarizes the algorithm and code we have developed, the code validation steps we have performed, and the initial results for multi-turbine type and placement optimization (TTP_OPT) we have run.

  3. Wind farm turbine type and placement optimization

    DOE PAGES

    Graf, Peter; Dykes, Katherine; Scott, George; ...

    2016-10-03

    The layout of turbines in a wind farm is already a challenging nonlinear, nonconvex, nonlinearly constrained continuous global optimization problem. Here we begin to address the next generation of wind farm optimization problems by adding the complexity that there is more than one turbine type to choose from. The optimization becomes a nonlinear constrained mixed integer problem, which is a very difficult class of problems to solve. Furthermore, this document briefly summarizes the algorithm and code we have developed, the code validation steps we have performed, and the initial results for multi-turbine type and placement optimization (TTP_OPT) we have run.

  4. Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming

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

    Song, Hyun-Seob; Goldberg, Noam; Mahajan, Ashutosh

    Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. Tomore » alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Results: Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs.« less

  5. Sums of Consecutive Integers

    ERIC Educational Resources Information Center

    Pong, Wai Yan

    2007-01-01

    We begin by answering the question, "Which natural numbers are sums of consecutive integers?" We then go on to explore the set of lengths (numbers of summands) in the decompositions of an integer as such sums.

  6. Managing time-substitutable electricity usage using dynamic controls

    DOEpatents

    Ghosh, Soumyadip; Hosking, Jonathan R.; Natarajan, Ramesh; Subramaniam, Shivaram; Zhang, Xiaoxuan

    2017-02-07

    A predictive-control approach allows an electricity provider to monitor and proactively manage peak and off-peak residential intra-day electricity usage in an emerging smart energy grid using time-dependent dynamic pricing incentives. The daily load is modeled as time-shifted, but cost-differentiated and substitutable, copies of the continuously-consumed electricity resource, and a consumer-choice prediction model is constructed to forecast the corresponding intra-day shares of total daily load according to this model. This is embedded within an optimization framework for managing the daily electricity usage. A series of transformations are employed, including the reformulation-linearization technique (RLT) to obtain a Mixed-Integer Programming (MIP) model representation of the resulting nonlinear optimization problem. In addition, various regulatory and pricing constraints are incorporated in conjunction with the specified profit and capacity utilization objectives.

  7. Managing time-substitutable electricity usage using dynamic controls

    DOEpatents

    Ghosh, Soumyadip; Hosking, Jonathan R.; Natarajan, Ramesh; Subramaniam, Shivaram; Zhang, Xiaoxuan

    2017-02-21

    A predictive-control approach allows an electricity provider to monitor and proactively manage peak and off-peak residential intra-day electricity usage in an emerging smart energy grid using time-dependent dynamic pricing incentives. The daily load is modeled as time-shifted, but cost-differentiated and substitutable, copies of the continuously-consumed electricity resource, and a consumer-choice prediction model is constructed to forecast the corresponding intra-day shares of total daily load according to this model. This is embedded within an optimization framework for managing the daily electricity usage. A series of transformations are employed, including the reformulation-linearization technique (RLT) to obtain a Mixed-Integer Programming (MIP) model representation of the resulting nonlinear optimization problem. In addition, various regulatory and pricing constraints are incorporated in conjunction with the specified profit and capacity utilization objectives.

  8. Non-integer viscoelastic constitutive law to model soft biological tissues to in-vivo indentation.

    PubMed

    Demirci, Nagehan; Tönük, Ergin

    2014-01-01

    During the last decades, derivatives and integrals of non-integer orders are being more commonly used for the description of constitutive behavior of various viscoelastic materials including soft biological tissues. Compared to integer order constitutive relations, non-integer order viscoelastic material models of soft biological tissues are capable of capturing a wider range of viscoelastic behavior obtained from experiments. Although integer order models may yield comparably accurate results, non-integer order material models have less number of parameters to be identified in addition to description of an intermediate material that can monotonically and continuously be adjusted in between an ideal elastic solid and an ideal viscous fluid. In this work, starting with some preliminaries on non-integer (fractional) calculus, the "spring-pot", (intermediate mechanical element between a solid and a fluid), non-integer order three element (Zener) solid model, finally a user-defined large strain non-integer order viscoelastic constitutive model was constructed to be used in finite element simulations. Using the constitutive equation developed, by utilizing inverse finite element method and in vivo indentation experiments, soft tissue material identification was performed. The results indicate that material coefficients obtained from relaxation experiments, when optimized with creep experimental data could simulate relaxation, creep and cyclic loading and unloading experiments accurately. Non-integer calculus viscoelastic constitutive models, having physical interpretation and modeling experimental data accurately is a good alternative to classical phenomenological viscoelastic constitutive equations.

  9. Mixed Integer Linear Programming based machine learning approach identifies regulators of telomerase in yeast.

    PubMed

    Poos, Alexandra M; Maicher, André; Dieckmann, Anna K; Oswald, Marcus; Eils, Roland; Kupiec, Martin; Luke, Brian; König, Rainer

    2016-06-02

    Understanding telomere length maintenance mechanisms is central in cancer biology as their dysregulation is one of the hallmarks for immortalization of cancer cells. Important for this well-balanced control is the transcriptional regulation of the telomerase genes. We integrated Mixed Integer Linear Programming models into a comparative machine learning based approach to identify regulatory interactions that best explain the discrepancy of telomerase transcript levels in yeast mutants with deleted regulators showing aberrant telomere length, when compared to mutants with normal telomere length. We uncover novel regulators of telomerase expression, several of which affect histone levels or modifications. In particular, our results point to the transcription factors Sum1, Hst1 and Srb2 as being important for the regulation of EST1 transcription, and we validated the effect of Sum1 experimentally. We compiled our machine learning method leading to a user friendly package for R which can straightforwardly be applied to similar problems integrating gene regulator binding information and expression profiles of samples of e.g. different phenotypes, diseases or treatments. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  10. Graphical models for optimal power flow

    DOE PAGES

    Dvijotham, Krishnamurthy; Chertkov, Michael; Van Hentenryck, Pascal; ...

    2016-09-13

    Optimal power flow (OPF) is the central optimization problem in electric power grids. Although solved routinely in the course of power grid operations, it is known to be strongly NP-hard in general, and weakly NP-hard over tree networks. In this paper, we formulate the optimal power flow problem over tree networks as an inference problem over a tree-structured graphical model where the nodal variables are low-dimensional vectors. We adapt the standard dynamic programming algorithm for inference over a tree-structured graphical model to the OPF problem. Combining this with an interval discretization of the nodal variables, we develop an approximation algorithmmore » for the OPF problem. Further, we use techniques from constraint programming (CP) to perform interval computations and adaptive bound propagation to obtain practically efficient algorithms. Compared to previous algorithms that solve OPF with optimality guarantees using convex relaxations, our approach is able to work for arbitrary tree-structured distribution networks and handle mixed-integer optimization problems. Further, it can be implemented in a distributed message-passing fashion that is scalable and is suitable for “smart grid” applications like control of distributed energy resources. In conclusion, numerical evaluations on several benchmark networks show that practical OPF problems can be solved effectively using this approach.« less

  11. COMSAT: Residue contact prediction of transmembrane proteins based on support vector machines and mixed integer linear programming.

    PubMed

    Zhang, Huiling; Huang, Qingsheng; Bei, Zhendong; Wei, Yanjie; Floudas, Christodoulos A

    2016-03-01

    In this article, we present COMSAT, a hybrid framework for residue contact prediction of transmembrane (TM) proteins, integrating a support vector machine (SVM) method and a mixed integer linear programming (MILP) method. COMSAT consists of two modules: COMSAT_SVM which is trained mainly on position-specific scoring matrix features, and COMSAT_MILP which is an ab initio method based on optimization models. Contacts predicted by the SVM model are ranked by SVM confidence scores, and a threshold is trained to improve the reliability of the predicted contacts. For TM proteins with no contacts above the threshold, COMSAT_MILP is used. The proposed hybrid contact prediction scheme was tested on two independent TM protein sets based on the contact definition of 14 Å between Cα-Cα atoms. First, using a rigorous leave-one-protein-out cross validation on the training set of 90 TM proteins, an accuracy of 66.8%, a coverage of 12.3%, a specificity of 99.3% and a Matthews' correlation coefficient (MCC) of 0.184 were obtained for residue pairs that are at least six amino acids apart. Second, when tested on a test set of 87 TM proteins, the proposed method showed a prediction accuracy of 64.5%, a coverage of 5.3%, a specificity of 99.4% and a MCC of 0.106. COMSAT shows satisfactory results when compared with 12 other state-of-the-art predictors, and is more robust in terms of prediction accuracy as the length and complexity of TM protein increase. COMSAT is freely accessible at http://hpcc.siat.ac.cn/COMSAT/. © 2016 Wiley Periodicals, Inc.

  12. TH-EF-BRB-04: 4π Dynamic Conformal Arc Therapy Dynamic Conformal Arc Therapy (DCAT) for SBRT

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

    Chiu, T; Long, T; Tian, Z.

    2016-06-15

    Purpose: To develop an efficient and effective trajectory optimization methodology for 4π dynamic conformal arc treatment (4π DCAT) with synchronized gantry and couch motion; and to investigate potential clinical benefits for stereotactic body radiation therapy (SBRT) to breast, lung, liver and spine tumors. Methods: The entire optimization framework for 4π DCAT inverse planning consists of two parts: 1) integer programming algorithm and 2) particle swarm optimization (PSO) algorithm. The integer programming is designed to find an optimal solution for arc delivery trajectory with both couch and gantry rotation, while PSO minimize a non-convex objective function based on the selected trajectorymore » and dose-volume constraints. In this study, control point interaction is explicitly taken into account. Beam trajectory was modeled as a series of control points connected together to form a deliverable path. With linear treatment planning objectives, a mixed-integer program (MIP) was formulated. Under mild assumptions, the MIP is tractable. Assigning monitor units to control points along the path can be integrated into the model and done by PSO. The developed 4π DCAT inverse planning strategy is evaluated on SBRT cases and compared to clinically treated plans. Results: The resultant dose distribution of this technique was evaluated between 3D conformal treatment plan generated by Pinnacle treatment planning system and 4π DCAT on a lung SBRT patient case. Both plans share the same scale of MU, 3038 and 2822 correspondingly to 3D conformal plan and 4π DCAT. The mean doses for most of OARs were greatly reduced at 32% (cord), 70% (esophagus), 2.8% (lung) and 42.4% (stomach). Conclusion: Initial results in this study show the proposed 4π DCAT treatment technique can achieve better OAR sparing and lower MUs, which indicates that the developed technique is promising for high dose SBRT to reduce the risk of secondary cancer.« less

  13. An Integer Programming Approach to School District Financial Management.

    ERIC Educational Resources Information Center

    Dembowski, Frederick L.

    Because of the nature of school district cash flows, there are opportunities for investing surplus cash and the necessity to borrow cash in deficit periods. The term structure of interest rates makes the manual determination of the optimal financial package impossible. In this research, an integer programming model of this cash management process…

  14. Galaxy Redshifts from Discrete Optimization of Correlation Functions

    NASA Astrophysics Data System (ADS)

    Lee, Benjamin C. G.; Budavári, Tamás; Basu, Amitabh; Rahman, Mubdi

    2016-12-01

    We propose a new method of constraining the redshifts of individual extragalactic sources based on celestial coordinates and their ensemble statistics. Techniques from integer linear programming (ILP) are utilized to optimize simultaneously for the angular two-point cross- and autocorrelation functions. Our novel formalism introduced here not only transforms the otherwise hopelessly expensive, brute-force combinatorial search into a linear system with integer constraints but also is readily implementable in off-the-shelf solvers. We adopt Gurobi, a commercial optimization solver, and use Python to build the cost function dynamically. The preliminary results on simulated data show potential for future applications to sky surveys by complementing and enhancing photometric redshift estimators. Our approach is the first application of ILP to astronomical analysis.

  15. Optimized Mixing in Microchannels with Integrated Microactuators

    NASA Astrophysics Data System (ADS)

    Folk, Christopher Richard

    Microscale valves and pumps have been designed and fabricated for integration into a microfluidic circuit. Furthermore, a micromixer for this circuit has been designed and optimized. N-isopropylacrylamide (NIPA) gels have been fabricated and actuated directly with heat and indirectly by laser. A new method for photopatterning these gels based on photoinitiation has been used to fabricate hydrogel valves down to 50 mum in diameter. Hydrogel valves have been fabricated in situ in a microfluidic network. The valves open in 27 seconds and close via diffusion of water into the gel in 128 seconds, which is faster than other optically-driven polymers used for large displacements. In this research, azobis-isobutyronitrile (AIBN) is incorporated into a variety of polydimethylsiloxane (PDMS) pump chambers. The AIBN is heated via integrated resistive heaters and decomposes to release nitrogen gas. The nitrogen gas provides impulse power to a PDMS diaphragm to displace the fluid. The pump devices have been built and characterized. Lastly, in this work, we describe the use of combined fluid dynamic and diffusion modeling to simulate a micromixer based on the elements above. The micromixer is optimized via Design of Experiments to produce an optimized geometry for mixing. The optimization is validated via comparison to previous work through the Strouhal number.

  16. Split diversity in constrained conservation prioritization using integer linear programming.

    PubMed

    Chernomor, Olga; Minh, Bui Quang; Forest, Félix; Klaere, Steffen; Ingram, Travis; Henzinger, Monika; von Haeseler, Arndt

    2015-01-01

    Phylogenetic diversity (PD) is a measure of biodiversity based on the evolutionary history of species. Here, we discuss several optimization problems related to the use of PD, and the more general measure split diversity (SD), in conservation prioritization.Depending on the conservation goal and the information available about species, one can construct optimization routines that incorporate various conservation constraints. We demonstrate how this information can be used to select sets of species for conservation action. Specifically, we discuss the use of species' geographic distributions, the choice of candidates under economic pressure, and the use of predator-prey interactions between the species in a community to define viability constraints.Despite such optimization problems falling into the area of NP hard problems, it is possible to solve them in a reasonable amount of time using integer programming. We apply integer linear programming to a variety of models for conservation prioritization that incorporate the SD measure.We exemplarily show the results for two data sets: the Cape region of South Africa and a Caribbean coral reef community. Finally, we provide user-friendly software at http://www.cibiv.at/software/pda.

  17. Fish Processed Production Planning Using Integer Stochastic Programming Model

    NASA Astrophysics Data System (ADS)

    Firmansyah, Mawengkang, Herman

    2011-06-01

    Fish and its processed products are the most affordable source of animal protein in the diet of most people in Indonesia. The goal in production planning is to meet customer demand over a fixed time horizon divided into planning periods by optimizing the trade-off between economic objectives such as production cost and customer satisfaction level. The major decisions are production and inventory levels for each product and the number of workforce in each planning period. In this paper we consider the management of small scale traditional business at North Sumatera Province which performs processing fish into several local seafood products. The inherent uncertainty of data (e.g. demand, fish availability), together with the sequential evolution of data over time leads the production planning problem to a nonlinear mixed-integer stochastic programming model. We use scenario generation based approach and feasible neighborhood search for solving the model. The results which show the amount of each fish processed product and the number of workforce needed in each horizon planning are presented.

  18. Optimal design of supply chain network under uncertainty environment using hybrid analytical and simulation modeling approach

    NASA Astrophysics Data System (ADS)

    Chiadamrong, N.; Piyathanavong, V.

    2017-12-01

    Models that aim to optimize the design of supply chain networks have gained more interest in the supply chain literature. Mixed-integer linear programming and discrete-event simulation are widely used for such an optimization problem. We present a hybrid approach to support decisions for supply chain network design using a combination of analytical and discrete-event simulation models. The proposed approach is based on iterative procedures until the difference between subsequent solutions satisfies the pre-determined termination criteria. The effectiveness of proposed approach is illustrated by an example, which shows closer to optimal results with much faster solving time than the results obtained from the conventional simulation-based optimization model. The efficacy of this proposed hybrid approach is promising and can be applied as a powerful tool in designing a real supply chain network. It also provides the possibility to model and solve more realistic problems, which incorporate dynamism and uncertainty.

  19. Learning Integer Addition: Is Later Better?

    ERIC Educational Resources Information Center

    Aqazade, Mahtob; Bofferding, Laura; Farmer, Sherri

    2017-01-01

    We investigate thirty-three second and fifth-grade students' solution strategies on integer addition problems before and after analyzing contrasting cases with integer addition and participating in a lesson on integers. The students took a pretest, participated in two small group sessions and a short lesson, and took a posttest. Even though the…

  20. Trace of totally positive algebraic integers and integer transfinite diameter

    NASA Astrophysics Data System (ADS)

    Flammang, V.

    2009-06-01

    Explicit auxiliary functions can be used in the ``Schur-Siegel- Smyth trace problem''. In the previous works, these functions were constructed only with polynomials having all their roots positive. Here, we use several polynomials with complex roots, which are found with Wu's algorithm, and we improve the known lower bounds for the absolute trace of totally positive algebraic integers. This improvement has a consequence for the search of Salem numbers that have a negative trace. The same method also gives a small improvement of the upper bound for the integer transfinite diameter of [0,1].

  1. Slip and Slide Method of Factoring Trinomials with Integer Coefficients over the Integers

    ERIC Educational Resources Information Center

    Donnell, William A.

    2012-01-01

    In intermediate and college algebra courses there are a number of methods for factoring quadratic trinomials with integer coefficients over the integers. Some of these methods have been given names, such as trial and error, reversing FOIL, AC method, middle term splitting method and slip and slide method. The purpose of this article is to discuss…

  2. Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem

    NASA Astrophysics Data System (ADS)

    Chen, Wei

    2015-07-01

    In this paper, we discuss the portfolio optimization problem with real-world constraints under the assumption that the returns of risky assets are fuzzy numbers. A new possibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered. Due to such constraints the proposed model becomes a mixed integer nonlinear programming problem and traditional optimization methods fail to find the optimal solution efficiently. Thus, a modified artificial bee colony (MABC) algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm.

  3. Study of a mixed dispersal population dynamics model

    DOE PAGES

    Chugunova, Marina; Jadamba, Baasansuren; Kao, Chiu -Yen; ...

    2016-08-27

    In this study, we consider a mixed dispersal model with periodic and Dirichlet boundary conditions and its corresponding linear eigenvalue problem. This model describes the time evolution of a population which disperses both locally and non-locally. We investigate how long time dynamics depend on the parameter values. Furthermore, we study the minimization of the principal eigenvalue under the constraints that the resource function is bounded from above and below, and with a fixed total integral. Biologically, this minimization problem is motivated by the question of determining the optimal spatial arrangement of favorable and unfavorable regions for the species to diemore » out more slowly or survive more easily. Our numerical simulations indicate that the optimal favorable region tends to be a simply-connected domain. Numerous results are shown to demonstrate various scenarios of optimal favorable regions for periodic and Dirichlet boundary conditions.« less

  4. Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming.

    PubMed

    Song, Hyun-Seob; Goldberg, Noam; Mahajan, Ashutosh; Ramkrishna, Doraiswami

    2017-08-01

    Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs. The software is implemented in Matlab, and is provided as supplementary information . hyunseob.song@pnnl.gov. Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2017. This work is written by US Government employees and are in the public domain in the US.

  5. Integers Made Easy: Just Walk It Off

    ERIC Educational Resources Information Center

    Nurnberger-Haag, Julie

    2007-01-01

    This article describes a multisensory method for teaching students how to multiply and divide as well as add and subtract integers. The author uses sidewalk chalk and the underlying concept of integers to physically and mentally engage students in understanding the concepts of integers, making connections, and developing computational fluency.…

  6. A statistical mechanical approach to restricted integer partition functions

    NASA Astrophysics Data System (ADS)

    Zhou, Chi-Chun; Dai, Wu-Sheng

    2018-05-01

    The main aim of this paper is twofold: (1) suggesting a statistical mechanical approach to the calculation of the generating function of restricted integer partition functions which count the number of partitions—a way of writing an integer as a sum of other integers under certain restrictions. In this approach, the generating function of restricted integer partition functions is constructed from the canonical partition functions of various quantum gases. (2) Introducing a new type of restricted integer partition functions corresponding to general statistics which is a generalization of Gentile statistics in statistical mechanics; many kinds of restricted integer partition functions are special cases of this restricted integer partition function. Moreover, with statistical mechanics as a bridge, we reveal a mathematical fact: the generating function of restricted integer partition function is just the symmetric function which is a class of functions being invariant under the action of permutation groups. Using this approach, we provide some expressions of restricted integer partition functions as examples.

  7. Hybrid switched time-optimal control of underactuated spacecraft

    NASA Astrophysics Data System (ADS)

    Olivares, Alberto; Staffetti, Ernesto

    2018-04-01

    This paper studies the time-optimal control problem for an underactuated rigid spacecraft equipped with both reaction wheels and gas jet thrusters that generate control torques about two of the principal axes of the spacecraft. Since a spacecraft equipped with two reaction wheels is not controllable, whereas a spacecraft equipped with two gas jet thrusters is controllable, this mixed actuation ensures controllability in the case in which one of the control axes is unactuated. A novel control logic is proposed for this hybrid actuation in which the reaction wheels are the main actuators and the gas jet thrusters act only after saturation or anticipating future saturation of the reaction wheels. The presence of both reaction wheels and gas jet thrusters gives rise to two operating modes for each actuated axis and therefore the spacecraft can be regarded as a switched dynamical system. The time-optimal control problem for this system is reformulated using the so-called embedding technique and the resulting problem is a classical optimal control problem. The main advantages of this technique are that integer or binary variables do not have to be introduced to model switching decisions between modes and that assumptions about the number of switches are not necessary. It is shown in this paper that this general method for the solution of optimal control problems for switched dynamical systems can efficiently deal with time-optimal control of an underactuated rigid spacecraft in which bound constraints on the torque of the actuators and on the angular momentum of the reaction wheels are taken into account.

  8. Greenhouse gas emissions control in integrated municipal solid waste management through mixed integer bilevel decision-making.

    PubMed

    He, Li; Huang, G H; Lu, Hongwei

    2011-10-15

    Recent studies indicated that municipal solid waste (MSW) is a major contributor to global warming due to extensive emissions of greenhouse gases (GHGs). However, most of them focused on investigating impacts of MSW on GHG emission amounts. This study presents two mixed integer bilevel decision-making models for integrated municipal solid waste management and GHG emissions control: MGU-MCL and MCU-MGL. The MGU-MCL model represents a top-down decision process, with the environmental sectors at the national level dominating the upper-level objective and the waste management sectors at the municipal level providing the lower-level objective. The MCU-MGL model implies a bottom-up decision process where municipality plays a leading role. Results from the models indicate that: the top-down decisions would reduce metric tonne carbon emissions (MTCEs) by about 59% yet increase about 8% of the total management cost; the bottom-up decisions would reduce MTCE emissions by about 13% but increase the total management cost very slightly; on-site monitoring and downscaled laboratory experiments are still required for reducing uncertainty in GHG emission rate from the landfill facility. Copyright © 2011 Elsevier B.V. All rights reserved.

  9. Cost Optimization Model for Business Applications in Virtualized Grid Environments

    NASA Astrophysics Data System (ADS)

    Strebel, Jörg

    The advent of Grid computing gives enterprises an ever increasing choice of computing options, yet research has so far hardly addressed the problem of mixing the different computing options in a cost-minimal fashion. The following paper presents a comprehensive cost model and a mixed integer optimization model which can be used to minimize the IT expenditures of an enterprise and help in decision-making when to outsource certain business software applications. A sample scenario is analyzed and promising cost savings are demonstrated. Possible applications of the model to future research questions are outlined.

  10. Optimization Research of Generation Investment Based on Linear Programming Model

    NASA Astrophysics Data System (ADS)

    Wu, Juan; Ge, Xueqian

    Linear programming is an important branch of operational research and it is a mathematical method to assist the people to carry out scientific management. GAMS is an advanced simulation and optimization modeling language and it will combine a large number of complex mathematical programming, such as linear programming LP, nonlinear programming NLP, MIP and other mixed-integer programming with the system simulation. In this paper, based on the linear programming model, the optimized investment decision-making of generation is simulated and analyzed. At last, the optimal installed capacity of power plants and the final total cost are got, which provides the rational decision-making basis for optimized investments.

  11. Mathematical Optimization Techniques

    NASA Technical Reports Server (NTRS)

    Bellman, R. (Editor)

    1963-01-01

    The papers collected in this volume were presented at the Symposium on Mathematical Optimization Techniques held in the Santa Monica Civic Auditorium, Santa Monica, California, on October 18-20, 1960. The objective of the symposium was to bring together, for the purpose of mutual education, mathematicians, scientists, and engineers interested in modern optimization techniques. Some 250 persons attended. The techniques discussed included recent developments in linear, integer, convex, and dynamic programming as well as the variational processes surrounding optimal guidance, flight trajectories, statistical decisions, structural configurations, and adaptive control systems. The symposium was sponsored jointly by the University of California, with assistance from the National Science Foundation, the Office of Naval Research, the National Aeronautics and Space Administration, and The RAND Corporation, through Air Force Project RAND.

  12. On unified modeling, theory, and method for solving multi-scale global optimization problems

    NASA Astrophysics Data System (ADS)

    Gao, David Yang

    2016-10-01

    A unified model is proposed for general optimization problems in multi-scale complex systems. Based on this model and necessary assumptions in physics, the canonical duality theory is presented in a precise way to include traditional duality theories and popular methods as special applications. Two conjectures on NP-hardness are proposed, which should play important roles for correctly understanding and efficiently solving challenging real-world problems. Applications are illustrated for both nonconvex continuous optimization and mixed integer nonlinear programming.

  13. Generating subtour elimination constraints for the TSP from pure integer solutions.

    PubMed

    Pferschy, Ulrich; Staněk, Rostislav

    2017-01-01

    The traveling salesman problem ( TSP ) is one of the most prominent combinatorial optimization problems. Given a complete graph [Formula: see text] and non-negative distances d for every edge, the TSP asks for a shortest tour through all vertices with respect to the distances d. The method of choice for solving the TSP to optimality is a branch and cut approach . Usually the integrality constraints are relaxed first and all separation processes to identify violated inequalities are done on fractional solutions . In our approach we try to exploit the impressive performance of current ILP-solvers and work only with integer solutions without ever interfering with fractional solutions. We stick to a very simple ILP-model and relax the subtour elimination constraints only. The resulting problem is solved to integer optimality, violated constraints (which are trivial to find) are added and the process is repeated until a feasible solution is found. In order to speed up the algorithm we pursue several attempts to find as many relevant subtours as possible. These attempts are based on the clustering of vertices with additional insights gained from empirical observations and random graph theory. Computational results are performed on test instances taken from the TSPLIB95 and on random Euclidean graphs .

  14. Modeling an integrated hospital management planning problem using integer optimization approach

    NASA Astrophysics Data System (ADS)

    Sitepu, Suryati; Mawengkang, Herman; Irvan

    2017-09-01

    Hospital is a very important institution to provide health care for people. It is not surprising that nowadays the people’s demands for hospital is increasing. However, due to the rising cost of healthcare services, hospitals need to consider efficiencies in order to overcome these two problems. This paper deals with an integrated strategy of staff capacity management and bed allocation planning to tackle these problems. Mathematically, the strategy can be modeled as an integer linear programming problem. We solve the model using a direct neighborhood search approach, based on the notion of superbasic variables.

  15. Mixed state dynamical quantum phase transitions

    NASA Astrophysics Data System (ADS)

    Bhattacharya, Utso; Bandyopadhyay, Souvik; Dutta, Amit

    2017-11-01

    Preparing an integrable system in a mixed state described by a thermal density matrix, we subject it to a sudden quench and explore the subsequent unitary dynamics. To address the question of whether the nonanalyticities, namely, the dynamical quantum phase transitions (DQPTs), persist when the initial state is mixed, we consider two versions of the generalized Loschmidt overlap amplitude (GLOA). Our study shows that the GLOA constructed using the Uhlmann approach does not show any signature of DQPTs at any nonzero initial temperature. On the other hand, a GLOA defined in the interferometric phase approach through the purifications of the time-evolved density matrix, indeed shows that nonanalyiticies in the corresponding "dynamical free-energy density" persist, thereby establishing the existence of mixed state dynamical quantum phase transitions (MSDQPTs). Our work provides a framework that perfectly reproduces both the nonanalyticities and also the emergent topological structure in the pure state limit. These claims are corroborated by analyzing the nonequilibrium dynamics of a transverse Ising chain initially prepared in a thermal state and subjected to a sudden quench of the transverse field.

  16. Currency arbitrage detection using a binary integer programming model

    NASA Astrophysics Data System (ADS)

    Soon, Wanmei; Ye, Heng-Qing

    2011-04-01

    In this article, we examine the use of a new binary integer programming (BIP) model to detect arbitrage opportunities in currency exchanges. This model showcases an excellent application of mathematics to the real world. The concepts involved are easily accessible to undergraduate students with basic knowledge in Operations Research. Through this work, students can learn to link several types of basic optimization models, namely linear programming, integer programming and network models, and apply the well-known sensitivity analysis procedure to accommodate realistic changes in the exchange rates. Beginning with a BIP model, we discuss how it can be reduced to an equivalent but considerably simpler model, where an efficient algorithm can be applied to find the arbitrages and incorporate the sensitivity analysis procedure. A simple comparison is then made with a different arbitrage detection model. This exercise helps students learn to apply basic Operations Research concepts to a practical real-life example, and provides insights into the processes involved in Operations Research model formulations.

  17. A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks.

    PubMed

    Röhl, Annika; Bockmayr, Alexander

    2017-01-03

    Constraint-based analysis has become a widely used method to study metabolic networks. While some of the associated algorithms can be applied to genome-scale network reconstructions with several thousands of reactions, others are limited to small or medium-sized models. In 2015, Erdrich et al. introduced a method called NetworkReducer, which reduces large metabolic networks to smaller subnetworks, while preserving a set of biological requirements that can be specified by the user. Already in 2001, Burgard et al. developed a mixed-integer linear programming (MILP) approach for computing minimal reaction sets under a given growth requirement. Here we present an MILP approach for computing minimum subnetworks with the given properties. The minimality (with respect to the number of active reactions) is not guaranteed by NetworkReducer, while the method by Burgard et al. does not allow specifying the different biological requirements. Our procedure is about 5-10 times faster than NetworkReducer and can enumerate all minimum subnetworks in case there exist several ones. This allows identifying common reactions that are present in all subnetworks, and reactions appearing in alternative pathways. Applying complex analysis methods to genome-scale metabolic networks is often not possible in practice. Thus it may become necessary to reduce the size of the network while keeping important functionalities. We propose a MILP solution to this problem. Compared to previous work, our approach is more efficient and allows computing not only one, but even all minimum subnetworks satisfying the required properties.

  18. A Mixed Integer Linear Program for Airport Departure Scheduling

    NASA Technical Reports Server (NTRS)

    Gupta, Gautam; Jung, Yoon Chul

    2009-01-01

    Aircraft departing from an airport are subject to numerous constraints while scheduling departure times. These constraints include wake-separation constraints for successive departures, miles-in-trail separation for aircraft bound for the same departure fixes, and time-window or prioritization constraints for individual flights. Besides these, emissions as well as increased fuel consumption due to inefficient scheduling need to be included. Addressing all the above constraints in a single framework while allowing for resequencing of the aircraft using runway queues is critical to the implementation of the Next Generation Air Transport System (NextGen) concepts. Prior work on airport departure scheduling has addressed some of the above. However, existing methods use pre-determined runway queues, and schedule aircraft from these departure queues. The source of such pre-determined queues is not explicit, and could potentially be a subjective controller input. Determining runway queues and scheduling within the same framework would potentially result in better scheduling. This paper presents a mixed integer linear program (MILP) for the departure-scheduling problem. The program takes as input the incoming sequence of aircraft for departure from a runway, along with their earliest departure times and an optional prioritization scheme based on time-window of departure for each aircraft. The program then assigns these aircraft to the available departure queues and schedules departure times, explicitly considering wake separation and departure fix restrictions to minimize total delay for all aircraft. The approach is generalized and can be used in a variety of situations, and allows for aircraft prioritization based on operational as well as environmental considerations. We present the MILP in the paper, along with benefits over the first-come-first-serve (FCFS) scheme for numerous randomized problems based on real-world settings. The MILP results in substantially reduced

  19. Analysis misconception of integers in microteaching activities

    NASA Astrophysics Data System (ADS)

    Setyawati, R. D.; Indiati, I.

    2018-05-01

    This study view to analyse student misconceptions on integers in microteaching activities. This research used qualitative research design. An integers test contained questions from eight main areas of integers. The Integers material test includes (a) converting the image into fractions, (b) examples of positive numbers including rational numbers, (c) operations in fractions, (d) sorting fractions from the largest to the smallest, and vice versa; e) equate denominator, (f) concept of ratio mark, (g) definition of fraction, and (h) difference between fractions and parts. The results indicated an integers concepts: (1) the students have not been able to define concepts well based on the classification of facts in organized part; (2) The correlational concept: students have not been able to combine interrelated events in the form of general principles; and (3) theoretical concepts: students have not been able to use concepts that facilitate in learning the facts or events in an organized system.

  20. Superstructure-based Design and Optimization of Batch Biodiesel Production Using Heterogeneous Catalysts

    NASA Astrophysics Data System (ADS)

    Nuh, M. Z.; Nasir, N. F.

    2017-08-01

    Biodiesel as a fuel comprised of mono alkyl esters of long chain fatty acids derived from renewable lipid feedstock, such as vegetable oil and animal fat. Biodiesel production is complex process which need systematic design and optimization. However, no case study using the process system engineering (PSE) elements which are superstructure optimization of batch process, it involves complex problems and uses mixed-integer nonlinear programming (MINLP). The PSE offers a solution to complex engineering system by enabling the use of viable tools and techniques to better manage and comprehend the complexity of the system. This study is aimed to apply the PSE tools for the simulation of biodiesel process and optimization and to develop mathematical models for component of the plant for case A, B, C by using published kinetic data. Secondly, to determine economic analysis for biodiesel production, focusing on heterogeneous catalyst. Finally, the objective of this study is to develop the superstructure for biodiesel production by using heterogeneous catalyst. The mathematical models are developed by the superstructure and solving the resulting mixed integer non-linear model and estimation economic analysis by using MATLAB software. The results of the optimization process with the objective function of minimizing the annual production cost by batch process from case C is 23.2587 million USD. Overall, the implementation a study of process system engineering (PSE) has optimized the process of modelling, design and cost estimation. By optimizing the process, it results in solving the complex production and processing of biodiesel by batch.

  1. A divide-and-conquer approach to determine the Pareto frontier for optimization of protein engineering experiments.

    PubMed

    He, Lu; Friedman, Alan M; Bailey-Kellogg, Chris

    2012-03-01

    In developing improved protein variants by site-directed mutagenesis or recombination, there are often competing objectives that must be considered in designing an experiment (selecting mutations or breakpoints): stability versus novelty, affinity versus specificity, activity versus immunogenicity, and so forth. Pareto optimal experimental designs make the best trade-offs between competing objectives. Such designs are not "dominated"; that is, no other design is better than a Pareto optimal design for one objective without being worse for another objective. Our goal is to produce all the Pareto optimal designs (the Pareto frontier), to characterize the trade-offs and suggest designs most worth considering, but to avoid explicitly considering the large number of dominated designs. To do so, we develop a divide-and-conquer algorithm, Protein Engineering Pareto FRontier (PEPFR), that hierarchically subdivides the objective space, using appropriate dynamic programming or integer programming methods to optimize designs in different regions. This divide-and-conquer approach is efficient in that the number of divisions (and thus calls to the optimizer) is directly proportional to the number of Pareto optimal designs. We demonstrate PEPFR with three protein engineering case studies: site-directed recombination for stability and diversity via dynamic programming, site-directed mutagenesis of interacting proteins for affinity and specificity via integer programming, and site-directed mutagenesis of a therapeutic protein for activity and immunogenicity via integer programming. We show that PEPFR is able to effectively produce all the Pareto optimal designs, discovering many more designs than previous methods. The characterization of the Pareto frontier provides additional insights into the local stability of design choices as well as global trends leading to trade-offs between competing criteria. Copyright © 2011 Wiley Periodicals, Inc.

  2. Estimating Most Productive Scale Size in Data Envelopment Analysis with Integer Value Data

    NASA Astrophysics Data System (ADS)

    Dwi Sari, Yunita; Angria S, Layla; Efendi, Syahril; Zarlis, Muhammad

    2018-01-01

    The most productive scale size (MPSS) is a measurement that states how resources should be organized and utilized to achieve optimal results. The most productive scale size (MPSS) can be used as a benchmark for the success of an industry or company in producing goods or services. To estimate the most productive scale size (MPSS), each decision making unit (DMU) should pay attention the level of input-output efficiency, by data envelopment analysis (DEA) method decision making unit (DMU) can identify units used as references that can help to find the cause and solution from inefficiencies can optimize productivity that main advantage in managerial applications. Therefore, data envelopment analysis (DEA) is chosen to estimating most productive scale size (MPSS) that will focus on the input of integer value data with the CCR model and the BCC model. The purpose of this research is to find the best solution for estimating most productive scale size (MPSS) with input of integer value data in data envelopment analysis (DEA) method.

  3. Optimal Design and Operation of Permanent Irrigation Systems

    NASA Astrophysics Data System (ADS)

    Oron, Gideon; Walker, Wynn R.

    1981-01-01

    Solid-set pressurized irrigation system design and operation are studied with optimization techniques to determine the minimum cost distribution system. The principle of the analysis is to divide the irrigation system into subunits in such a manner that the trade-offs among energy, piping, and equipment costs are selected at the minimum cost point. The optimization procedure involves a nonlinear, mixed integer approach capable of achieving a variety of optimal solutions leading to significant conclusions with regard to the design and operation of the system. Factors investigated include field geometry, the effect of the pressure head, consumptive use rates, a smaller flow rate in the pipe system, and outlet (sprinkler or emitter) discharge.

  4. 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.

  5. Designing area optimized application-specific network-on-chip architectures while providing hard QoS guarantees.

    PubMed

    Khawaja, Sajid Gul; Mushtaq, Mian Hamza; Khan, Shoab A; Akram, M Usman; Jamal, Habib Ullah

    2015-01-01

    With the increase of transistors' density, popularity of System on Chip (SoC) has increased exponentially. As a communication module for SoC, Network on Chip (NoC) framework has been adapted as its backbone. In this paper, we propose a methodology for designing area-optimized application specific NoC while providing hard Quality of Service (QoS) guarantees for real time flows. The novelty of the proposed system lies in derivation of a Mixed Integer Linear Programming model which is then used to generate a resource optimal Network on Chip (NoC) topology and architecture while considering traffic and QoS requirements. We also present the micro-architectural design features used for enabling traffic and latency guarantees and discuss how the solution adapts for dynamic variations in the application traffic. The paper highlights the effectiveness of proposed method by generating resource efficient NoC solutions for both industrial and benchmark applications. The area-optimized results are generated in few seconds by proposed technique, without resorting to heuristics, even for an application with 48 traffic flows.

  6. Designing Area Optimized Application-Specific Network-On-Chip Architectures while Providing Hard QoS Guarantees

    PubMed Central

    Khawaja, Sajid Gul; Mushtaq, Mian Hamza; Khan, Shoab A.; Akram, M. Usman; Jamal, Habib ullah

    2015-01-01

    With the increase of transistors' density, popularity of System on Chip (SoC) has increased exponentially. As a communication module for SoC, Network on Chip (NoC) framework has been adapted as its backbone. In this paper, we propose a methodology for designing area-optimized application specific NoC while providing hard Quality of Service (QoS) guarantees for real time flows. The novelty of the proposed system lies in derivation of a Mixed Integer Linear Programming model which is then used to generate a resource optimal Network on Chip (NoC) topology and architecture while considering traffic and QoS requirements. We also present the micro-architectural design features used for enabling traffic and latency guarantees and discuss how the solution adapts for dynamic variations in the application traffic. The paper highlights the effectiveness of proposed method by generating resource efficient NoC solutions for both industrial and benchmark applications. The area-optimized results are generated in few seconds by proposed technique, without resorting to heuristics, even for an application with 48 traffic flows. PMID:25898016

  7. Stochastic search in structural optimization - Genetic algorithms and simulated annealing

    NASA Technical Reports Server (NTRS)

    Hajela, Prabhat

    1993-01-01

    An account is given of illustrative applications of genetic algorithms and simulated annealing methods in structural optimization. The advantages of such stochastic search methods over traditional mathematical programming strategies are emphasized; it is noted that these methods offer a significantly higher probability of locating the global optimum in a multimodal design space. Both genetic-search and simulated annealing can be effectively used in problems with a mix of continuous, discrete, and integer design variables.

  8. Stochastic Dynamic Mixed-Integer Programming (SD-MIP)

    DTIC Science & Technology

    2015-05-05

    stochastic linear programming ( SLP ) problems. By using a combination of ideas from cutting plane theory of deterministic MIP (especially disjunctive...developed to date. b) As part of this project, we have also developed tools for very large scale Stochastic Linear Programming ( SLP ). There are...several reasons for this. First, SLP models continue to challenge many of the fastest computers to date, and many applications within the DoD (e.g

  9. An improved exploratory search technique for pure integer linear programming problems

    NASA Technical Reports Server (NTRS)

    Fogle, F. R.

    1990-01-01

    The development is documented of a heuristic method for the solution of pure integer linear programming problems. The procedure draws its methodology from the ideas of Hooke and Jeeves type 1 and 2 exploratory searches, greedy procedures, and neighborhood searches. It uses an efficient rounding method to obtain its first feasible integer point from the optimal continuous solution obtained via the simplex method. Since this method is based entirely on simple addition or subtraction of one to each variable of a point in n-space and the subsequent comparison of candidate solutions to a given set of constraints, it facilitates significant complexity improvements over existing techniques. It also obtains the same optimal solution found by the branch-and-bound technique in 44 of 45 small to moderate size test problems. Two example problems are worked in detail to show the inner workings of the method. Furthermore, using an established weighted scheme for comparing computational effort involved in an algorithm, a comparison of this algorithm is made to the more established and rigorous branch-and-bound method. A computer implementation of the procedure, in PC compatible Pascal, is also presented and discussed.

  10. Integer cosine transform for image compression

    NASA Technical Reports Server (NTRS)

    Cheung, K.-M.; Pollara, F.; Shahshahani, M.

    1991-01-01

    This article describes a recently introduced transform algorithm called the integer cosine transform (ICT), which is used in transform-based data compression schemes. The ICT algorithm requires only integer operations on small integers and at the same time gives a rate-distortion performance comparable to that offered by the floating-point discrete cosine transform (DCT). The article addresses the issue of implementation complexity, which is of prime concern for source coding applications of interest in deep-space communications. Complexity reduction in the transform stage of the compression scheme is particularly relevant, since this stage accounts for most (typically over 80 percent) of the computational load.

  11. Hybrid Optimization Parallel Search PACKage

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

    2009-11-10

    HOPSPACK is open source software for solving optimization problems without derivatives. Application problems may have a fully nonlinear objective function, bound constraints, and linear and nonlinear constraints. Problem variables may be continuous, integer-valued, or a mixture of both. The software provides a framework that supports any derivative-free type of solver algorithm. Through the framework, solvers request parallel function evaluation, which may use MPI (multiple machines) or multithreading (multiple processors/cores on one machine). The framework provides a Cache and Pending Cache of saved evaluations that reduces execution time and facilitates restarts. Solvers can dynamically create other algorithms to solve subproblems, amore » useful technique for handling multiple start points and integer-valued variables. HOPSPACK ships with the Generating Set Search (GSS) algorithm, developed at Sandia as part of the APPSPACK open source software project.« less

  12. Quantum Hall effect in ac driven graphene: From the half-integer to the integer case

    NASA Astrophysics Data System (ADS)

    Ding, Kai-He; Lim, Lih-King; Su, Gang; Weng, Zheng-Yu

    2018-01-01

    We theoretically study the quantum Hall effect (QHE) in graphene with an ac electric field. Based on the tight-binding model, the structure of the half-integer Hall plateaus at σxy=±(n +1 /2 ) 4 e2/h (n is an integer) gets qualitatively changed with the addition of new integer Hall plateaus at σxy=±n (4 e2/h ) starting from the edges of the band center regime towards the band center with an increasing ac field. Beyond a critical field strength, a Hall plateau with σxy=0 can be realized at the band center, hence fully restoring a conventional integer QHE with particle-hole symmetry. Within a low-energy Hamiltonian for Dirac cones merging, we show a very good agreement with the tight-binding calculations for the Hall plateau transitions. We also obtain the band structure for driven graphene ribbons to provide a further understanding on the appearance of the new Hall plateaus, showing a trivial insulator behavior for the σxy=0 state. In the presence of disorder, we numerically study the disorder-induced destruction of the quantum Hall states in a finite driven sample and find that qualitative features known in the undriven disordered case are maintained.

  13. Optimal GENCO bidding strategy

    NASA Astrophysics Data System (ADS)

    Gao, Feng

    Electricity industries worldwide are undergoing a period of profound upheaval. The conventional vertically integrated mechanism is being replaced by a competitive market environment. Generation companies have incentives to apply novel technologies to lower production costs, for example: Combined Cycle units. Economic dispatch with Combined Cycle units becomes a non-convex optimization problem, which is difficult if not impossible to solve by conventional methods. Several techniques are proposed here: Mixed Integer Linear Programming, a hybrid method, as well as Evolutionary Algorithms. Evolutionary Algorithms share a common mechanism, stochastic searching per generation. The stochastic property makes evolutionary algorithms robust and adaptive enough to solve a non-convex optimization problem. This research implements GA, EP, and PS algorithms for economic dispatch with Combined Cycle units, and makes a comparison with classical Mixed Integer Linear Programming. The electricity market equilibrium model not only helps Independent System Operator/Regulator analyze market performance and market power, but also provides Market Participants the ability to build optimal bidding strategies based on Microeconomics analysis. Supply Function Equilibrium (SFE) is attractive compared to traditional models. This research identifies a proper SFE model, which can be applied to a multiple period situation. The equilibrium condition using discrete time optimal control is then developed for fuel resource constraints. Finally, the research discusses the issues of multiple equilibria and mixed strategies, which are caused by the transmission network. Additionally, an advantage of the proposed model for merchant transmission planning is discussed. A market simulator is a valuable training and evaluation tool to assist sellers, buyers, and regulators to understand market performance and make better decisions. A traditional optimization model may not be enough to consider the distributed

  14. Solving Integer Programs from Dependence and Synchronization Problems

    DTIC Science & Technology

    1993-03-01

    DEFF.NSNE Solving Integer Programs from Dependence and Synchronization Problems Jaspal Subhlok March 1993 CMU-CS-93-130 School of Computer ScienceT IC...method Is an exact and efficient way of solving integer programming problems arising in dependence and synchronization analysis of parallel programs...7/;- p Keywords: Exact dependence tesing, integer programming. parallelilzng compilers, parallel program analysis, synchronization analysis Solving

  15. Order and Value: Transitioning to Integers

    ERIC Educational Resources Information Center

    Bofferding, Laura

    2014-01-01

    As students progress from working with whole numbers to working with integers, they must wrestle with the big ideas of number values and order. Using objects to show positive quantities is easy, but no physical negative quantities exist. Therefore, when talking about integers, the author refers to number values instead of number quantities. The…

  16. Multi-objective shape optimization of plate structure under stress criteria based on sub-structured mixed FEM and genetic algorithms

    NASA Astrophysics Data System (ADS)

    Garambois, Pierre; Besset, Sebastien; Jézéquel, Louis

    2015-07-01

    This paper presents a methodology for the multi-objective (MO) shape optimization of plate structure under stress criteria, based on a mixed Finite Element Model (FEM) enhanced with a sub-structuring method. The optimization is performed with a classical Genetic Algorithm (GA) method based on Pareto-optimal solutions and considers thickness distributions parameters and antagonist objectives among them stress criteria. We implement a displacement-stress Dynamic Mixed FEM (DM-FEM) for plate structure vibrations analysis. Such a model gives a privileged access to the stress within the plate structure compared to primal classical FEM, and features a linear dependence to the thickness parameters. A sub-structuring reduction method is also computed in order to reduce the size of the mixed FEM and split the given structure into smaller ones with their own thickness parameters. Those methods combined enable a fast and stress-wise efficient structure analysis, and improve the performance of the repetitive GA. A few cases of minimizing the mass and the maximum Von Mises stress within a plate structure under a dynamic load put forward the relevance of our method with promising results. It is able to satisfy multiple damage criteria with different thickness distributions, and use a smaller FEM.

  17. Integer-Linear-Programing Optimization in Scalable Video Multicast with Adaptive Modulation and Coding in Wireless Networks

    PubMed Central

    Lee, Chaewoo

    2014-01-01

    The advancement in wideband wireless network supports real time services such as IPTV and live video streaming. However, because of the sharing nature of the wireless medium, efficient resource allocation has been studied to achieve a high level of acceptability and proliferation of wireless multimedia. Scalable video coding (SVC) with adaptive modulation and coding (AMC) provides an excellent solution for wireless video streaming. By assigning different modulation and coding schemes (MCSs) to video layers, SVC can provide good video quality to users in good channel conditions and also basic video quality to users in bad channel conditions. For optimal resource allocation, a key issue in applying SVC in the wireless multicast service is how to assign MCSs and the time resources to each SVC layer in the heterogeneous channel condition. We formulate this problem with integer linear programming (ILP) and provide numerical results to show the performance under 802.16 m environment. The result shows that our methodology enhances the overall system throughput compared to an existing algorithm. PMID:25276862

  18. A mixed integer bi-level DEA model for bank branch performance evaluation by Stackelberg approach

    NASA Astrophysics Data System (ADS)

    Shafiee, Morteza; Lotfi, Farhad Hosseinzadeh; Saleh, Hilda; Ghaderi, Mehdi

    2016-03-01

    One of the most complicated decision making problems for managers is the evaluation of bank performance, which involves various criteria. There are many studies about bank efficiency evaluation by network DEA in the literature review. These studies do not focus on multi-level network. Wu (Eur J Oper Res 207:856-864, 2010) proposed a bi-level structure for cost efficiency at the first time. In this model, multi-level programming and cost efficiency were used. He used a nonlinear programming to solve the model. In this paper, we have focused on multi-level structure and proposed a bi-level DEA model. We then used a liner programming to solve our model. In other hand, we significantly improved the way to achieve the optimum solution in comparison with the work by Wu (2010) by converting the NP-hard nonlinear programing into a mixed integer linear programming. This study uses a bi-level programming data envelopment analysis model that embodies internal structure with Stackelberg-game relationships to evaluate the performance of banking chain. The perspective of decentralized decisions is taken in this paper to cope with complex interactions in banking chain. The results derived from bi-level programming DEA can provide valuable insights and detailed information for managers to help them evaluate the performance of the banking chain as a whole using Stackelberg-game relationships. Finally, this model was applied in the Iranian bank to evaluate cost efficiency.

  19. A farm-level precision land management framework based on integer programming

    PubMed Central

    Li, Qi; Hu, Guiping; Jubery, Talukder Zaki; Ganapathysubramanian, Baskar

    2017-01-01

    Farmland management involves several planning and decision making tasks including seed selection and irrigation management. A farm-level precision farmland management model based on mixed integer linear programming is proposed in this study. Optimal decisions are designed for pre-season planning of crops and irrigation water allocation. The model captures the effect of size and shape of decision scale as well as special irrigation patterns. The authors illustrate the model with a case study on a farm in the state of California in the U.S. and show the model can capture the impact of precision farm management on profitability. The results show that threefold increase of annual net profit for farmers could be achieved by carefully choosing irrigation and seed selection. Although farmers could increase profits by applying precision management to seed or irrigation alone, profit increase is more significant if farmers apply precision management on seed and irrigation simultaneously. The proposed model can also serve as a risk analysis tool for farmers facing seasonal irrigation water limits as well as a quantitative tool to explore the impact of precision agriculture. PMID:28346499

  20. Optimized open-flow mixing: insights from microbubble streaming

    NASA Astrophysics Data System (ADS)

    Rallabandi, Bhargav; Wang, Cheng; Guo, Lin; Hilgenfeldt, Sascha

    2015-11-01

    Microbubble streaming has been developed into a robust and powerful flow actuation technique in microfluidics. Here, we study it as a paradigmatic system for microfluidic mixing under a continuous throughput of fluid (open-flow mixing), providing a systematic optimization of the device parameters in this practically important situation. Focusing on two-dimensional advective stirring (neglecting diffusion), we show through numerical simulation and analytical theory that mixing in steady streaming vortices becomes ineffective beyond a characteristic time scale, necessitating the introduction of unsteadiness. By duty cycling the streaming, such unsteadiness is introduced in a controlled fashion, leading to exponential refinement of the advection structures. The rate of refinement is then optimized for particular parameters of the time modulation, i.e. a particular combination of times for which the streaming is turned ``on'' and ``off''. The optimized protocol can be understood theoretically using the properties of the streaming vortices and the throughput Poiseuille flow. We can thus infer simple design principles for practical open flow micromixing applications, consistent with experiments. Current Address: Mechanical and Aerospace Engineering, Princeton University.

  1. Fractal electrodynamics via non-integer dimensional space approach

    NASA Astrophysics Data System (ADS)

    Tarasov, Vasily E.

    2015-09-01

    Using the recently suggested vector calculus for non-integer dimensional space, we consider electrodynamics problems in isotropic case. This calculus allows us to describe fractal media in the framework of continuum models with non-integer dimensional space. We consider electric and magnetic fields of fractal media with charges and currents in the framework of continuum models with non-integer dimensional spaces. An application of the fractal Gauss's law, the fractal Ampere's circuital law, the fractal Poisson equation for electric potential, and equation for fractal stream of charges are suggested. Lorentz invariance and speed of light in fractal electrodynamics are discussed. An expression for effective refractive index of non-integer dimensional space is suggested.

  2. REopt: A Platform for Energy System Integration and Optimization: Preprint

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

    Simpkins, T.; Cutler, D.; Anderson, K.

    2014-08-01

    REopt is NREL's energy planning platform offering concurrent, multi-technology integration and optimization capabilities to help clients meet their cost savings and energy performance goals. The REopt platform provides techno-economic decision-support analysis throughout the energy planning process, from agency-level screening and macro planning to project development to energy asset operation. REopt employs an integrated approach to optimizing a site?s energy costs by considering electricity and thermal consumption, resource availability, complex tariff structures including time-of-use, demand and sell-back rates, incentives, net-metering, and interconnection limits. Formulated as a mixed integer linear program, REopt recommends an optimally-sized mix of conventional and renewable energy, andmore » energy storage technologies; estimates the net present value associated with implementing those technologies; and provides the cost-optimal dispatch strategy for operating them at maximum economic efficiency. The REopt platform can be customized to address a variety of energy optimization scenarios including policy, microgrid, and operational energy applications. This paper presents the REopt techno-economic model along with two examples of recently completed analysis projects.« less

  3. Integration of progressive hedging and dual decomposition in stochastic integer programs

    DOE PAGES

    Watson, Jean -Paul; Guo, Ge; Hackebeil, Gabriel; ...

    2015-04-07

    We present a method for integrating the Progressive Hedging (PH) algorithm and the Dual Decomposition (DD) algorithm of Carøe and Schultz for stochastic mixed-integer programs. Based on the correspondence between lower bounds obtained with PH and DD, a method to transform weights from PH to Lagrange multipliers in DD is found. Fast progress in early iterations of PH speeds up convergence of DD to an exact solution. As a result, we report computational results on server location and unit commitment instances.

  4. Core barrier formation near integer q surfaces in DIII-D

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

    Austin, M. E.; Gentle, K. W.; Burrell, K. H.

    2006-08-15

    Recent DIII-D experiments have significantly improved the understanding of internal transport barriers (ITBs) that are triggered close to the time when an integer value of the minimum in q is crossed. While this phenomenon has been observed on many tokamaks, the extensive transport and fluctuation diagnostics on DIII-D have permitted a detailed study of the generation mechanisms of q-triggered ITBs as pertaining to turbulence suppression dynamics, shear flows, and energetic particle modes. In these discharges, the evolution of the q profile is measured using motional Stark effect polarimetry and the integer q{sub min} crossings are further pinpointed in time bymore » the observation of Alfven cascades. High time resolution measurements of the ion and electron temperatures and the toroidal rotation show that the start of improved confinement is simultaneous in all three channels, and that this event precedes the traversal of integer q{sub min} by 5-20 ms. There is no significant low-frequency magnetohydrodynamic activity prior to or just after the crossing of the integer q{sub min} and hence magnetic reconnection is determined not to be the precipitant of the confinement change. Instead, results from the GYRO code point to the effects of zonal flows near low order rational q values as playing a role in ITB triggering. A reduction in local turbulent fluctuations is observed at the start of the temperature rise and, concurrently, an increase in turbulence poloidal flow velocity and flow shear is measured with the beam emission spectroscopy diagnostic. For the case of a transition to an enduring internal barrier the fluctuation level remains at a reduced amplitude. The timing and nature of the temperature, rotation, and fluctuation changes leading to internal barriers suggests transport improvement due to increased shear flow arising from the zonal flow structures.« less

  5. Dynamic Allocation of SPM Based on Time-Slotted Cache Conflict Graph for System Optimization

    NASA Astrophysics Data System (ADS)

    Wu, Jianping; Ling, Ming; Zhang, Yang; Mei, Chen; Wang, Huan

    This paper proposes a novel dynamic Scratch-pad Memory allocation strategy to optimize the energy consumption of the memory sub-system. Firstly, the whole program execution process is sliced into several time slots according to the temporal dimension; thereafter, a Time-Slotted Cache Conflict Graph (TSCCG) is introduced to model the behavior of Data Cache (D-Cache) conflicts within each time slot. Then, Integer Nonlinear Programming (INP) is implemented, which can avoid time-consuming linearization process, to select the most profitable data pages. Virtual Memory System (VMS) is adopted to remap those data pages, which will cause severe Cache conflicts within a time slot, to SPM. In order to minimize the swapping overhead of dynamic SPM allocation, a novel SPM controller with a tightly coupled DMA is introduced to issue the swapping operations without CPU's intervention. Last but not the least, this paper discusses the fluctuation of system energy profit based on different MMU page size as well as the Time Slot duration quantitatively. According to our design space exploration, the proposed method can optimize all of the data segments, including global data, heap and stack data in general, and reduce the total energy consumption by 27.28% on average, up to 55.22% with a marginal performance promotion. And comparing to the conventional static CCG (Cache Conflicts Graph), our approach can obtain 24.7% energy profit on average, up to 30.5% with a sight boost in performance.

  6. Fluid dynamic analysis of a continuous stirred tank reactor for technical optimization of wastewater digestion.

    PubMed

    Hurtado, F J; Kaiser, A S; Zamora, B

    2015-03-15

    Continuous stirred tank reactors (CSTR) are widely used in wastewater treatment plants to reduce the organic matter and microorganism present in sludge by anaerobic digestion. The present study carries out a numerical analysis of the fluid dynamic behaviour of a CSTR in order to optimize the process energetically. The characterization of the sludge flow inside the digester tank, the residence time distribution and the active volume of the reactor under different criteria are determined. The effects of design and power of the mixing system on the active volume of the CSTR are analyzed. The numerical model is solved under non-steady conditions by examining the evolution of the flow during the stop and restart of the mixing system. An intermittent regime of the mixing system, which kept the active volume between 94% and 99%, is achieved. The results obtained can lead to the eventual energy optimization of the mixing system of the CSTR. Copyright © 2014 Elsevier Ltd. All rights reserved.

  7. Optimal perturbations for nonlinear systems using graph-based optimal transport

    NASA Astrophysics Data System (ADS)

    Grover, Piyush; Elamvazhuthi, Karthik

    2018-06-01

    We formulate and solve a class of finite-time transport and mixing problems in the set-oriented framework. The aim is to obtain optimal discrete-time perturbations in nonlinear dynamical systems to transport a specified initial measure on the phase space to a final measure in finite time. The measure is propagated under system dynamics in between the perturbations via the associated transfer operator. Each perturbation is described by a deterministic map in the measure space that implements a version of Monge-Kantorovich optimal transport with quadratic cost. Hence, the optimal solution minimizes a sum of quadratic costs on phase space transport due to the perturbations applied at specified times. The action of the transport map is approximated by a continuous pseudo-time flow on a graph, resulting in a tractable convex optimization problem. This problem is solved via state-of-the-art solvers to global optimality. We apply this algorithm to a problem of transport between measures supported on two disjoint almost-invariant sets in a chaotic fluid system, and to a finite-time optimal mixing problem by choosing the final measure to be uniform. In both cases, the optimal perturbations are found to exploit the phase space structures, such as lobe dynamics, leading to efficient global transport. As the time-horizon of the problem is increased, the optimal perturbations become increasingly localized. Hence, by combining the transfer operator approach with ideas from the theory of optimal mass transportation, we obtain a discrete-time graph-based algorithm for optimal transport and mixing in nonlinear systems.

  8. Optimal minimal measurements of mixed states

    NASA Astrophysics Data System (ADS)

    Vidal, G.; Latorre, J. I.; Pascual, P.; Tarrach, R.

    1999-07-01

    The optimal and minimal measuring strategy is obtained for a two-state system prepared in a mixed state with a probability given by any isotropic a priori distribution. We explicitly construct the specific optimal and minimal generalized measurements, which turn out to be independent of the a priori probability distribution, obtaining the best guesses for the unknown state as well as a closed expression for the maximal mean-average fidelity. We do this for up to three copies of the unknown state in a way that leads to the generalization to any number of copies, which we then present and prove.

  9. Integer aperture ambiguity resolution based on difference test

    NASA Astrophysics Data System (ADS)

    Zhang, Jingyu; Wu, Meiping; Li, Tao; Zhang, Kaidong

    2015-07-01

    Carrier-phase integer ambiguity resolution (IAR) is the key to highly precise, fast positioning and attitude determination with Global Navigation Satellite System (GNSS). It can be seen as the process of estimating the unknown cycle ambiguities of the carrier-phase observations as integers. Once the ambiguities are fixed, carrier phase data will act as the very precise range data. Integer aperture (IA) ambiguity resolution is the combination of acceptance testing and integer ambiguity resolution, which can realize better quality control of IAR. Difference test (DT) is one of the most popular acceptance tests. This contribution will give a detailed analysis about the following properties of IA ambiguity resolution based on DT: 1. The sharpest and loose upper bounds of DT are derived from the perspective of geometry. These bounds are very simple and easy to be computed, which give the range for the critical values of DT.

  10. Dynamic Infinite Mixed-Membership Stochastic Blockmodel.

    PubMed

    Fan, Xuhui; Cao, Longbing; Xu, Richard Yi Da

    2015-09-01

    Directional and pairwise measurements are often used to model interactions in a social network setting. The mixed-membership stochastic blockmodel (MMSB) was a seminal work in this area, and its ability has been extended. However, models such as MMSB face particular challenges in modeling dynamic networks, for example, with the unknown number of communities. Accordingly, this paper proposes a dynamic infinite mixed-membership stochastic blockmodel, a generalized framework that extends the existing work to potentially infinite communities inside a network in dynamic settings (i.e., networks are observed over time). Additional model parameters are introduced to reflect the degree of persistence among one's memberships at consecutive time stamps. Under this framework, two specific models, namely mixture time variant and mixture time invariant models, are proposed to depict two different time correlation structures. Two effective posterior sampling strategies and their results are presented, respectively, using synthetic and real-world data.

  11. A novel comprehensive learning artificial bee colony optimizer for dynamic optimization biological problems.

    PubMed

    Su, Weixing; Chen, Hanning; Liu, Fang; Lin, Na; Jing, Shikai; Liang, Xiaodan; Liu, Wei

    2017-03-01

    There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC) for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell's pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm.

  12. Generating "Random" Integers

    ERIC Educational Resources Information Center

    Griffiths, Martin

    2011-01-01

    One of the author's undergraduate students recently asked him whether it was possible to generate a random positive integer. After some thought, the author realised that there were plenty of interesting mathematical ideas inherent in her question. So much so in fact, that the author decided to organise a workshop, open both to undergraduates and…

  13. INDDGO: Integrated Network Decomposition & Dynamic programming for Graph Optimization

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

    Groer, Christopher S; Sullivan, Blair D; Weerapurage, Dinesh P

    2012-10-01

    It is well-known that dynamic programming algorithms can utilize tree decompositions to provide a way to solve some \\emph{NP}-hard problems on graphs where the complexity is polynomial in the number of nodes and edges in the graph, but exponential in the width of the underlying tree decomposition. However, there has been relatively little computational work done to determine the practical utility of such dynamic programming algorithms. We have developed software to construct tree decompositions using various heuristics and have created a fast, memory-efficient dynamic programming implementation for solving maximum weighted independent set. We describe our software and the algorithms wemore » have implemented, focusing on memory saving techniques for the dynamic programming. We compare the running time and memory usage of our implementation with other techniques for solving maximum weighted independent set, including a commercial integer programming solver and a semi-definite programming solver. Our results indicate that it is possible to solve some instances where the underlying decomposition has width much larger than suggested by the literature. For certain types of problems, our dynamic programming code runs several times faster than these other methods.« less

  14. Bi-objective integer programming for RNA secondary structure prediction with pseudoknots.

    PubMed

    Legendre, Audrey; Angel, Eric; Tahi, Fariza

    2018-01-15

    RNA structure prediction is an important field in bioinformatics, and numerous methods and tools have been proposed. Pseudoknots are specific motifs of RNA secondary structures that are difficult to predict. Almost all existing methods are based on a single model and return one solution, often missing the real structure. An alternative approach would be to combine different models and return a (small) set of solutions, maximizing its quality and diversity in order to increase the probability that it contains the real structure. We propose here an original method for predicting RNA secondary structures with pseudoknots, based on integer programming. We developed a generic bi-objective integer programming algorithm allowing to return optimal and sub-optimal solutions optimizing simultaneously two models. This algorithm was then applied to the combination of two known models of RNA secondary structure prediction, namely MEA and MFE. The resulting tool, called BiokoP, is compared with the other methods in the literature. The results show that the best solution (structure with the highest F 1 -score) is, in most cases, given by BiokoP. Moreover, the results of BiokoP are homogeneous, regardless of the pseudoknot type or the presence or not of pseudoknots. Indeed, the F 1 -scores are always higher than 70% for any number of solutions returned. The results obtained by BiokoP show that combining the MEA and the MFE models, as well as returning several optimal and several sub-optimal solutions, allow to improve the prediction of secondary structures. One perspective of our work is to combine better mono-criterion models, in particular to combine a model based on the comparative approach with the MEA and the MFE models. This leads to develop in the future a new multi-objective algorithm to combine more than two models. BiokoP is available on the EvryRNA platform: https://EvryRNA.ibisc.univ-evry.fr .

  15. Stochastic dynamics and combinatorial optimization

    NASA Astrophysics Data System (ADS)

    Ovchinnikov, Igor V.; Wang, Kang L.

    2017-11-01

    Natural dynamics is often dominated by sudden nonlinear processes such as neuroavalanches, gamma-ray bursts, solar flares, etc., that exhibit scale-free statistics much in the spirit of the logarithmic Ritcher scale for earthquake magnitudes. On phase diagrams, stochastic dynamical systems (DSs) exhibiting this type of dynamics belong to the finite-width phase (N-phase for brevity) that precedes ordinary chaotic behavior and that is known under such names as noise-induced chaos, self-organized criticality, dynamical complexity, etc. Within the recently proposed supersymmetric theory of stochastic dynamics, the N-phase can be roughly interpreted as the noise-induced “overlap” between integrable and chaotic deterministic dynamics. As a result, the N-phase dynamics inherits the properties of the both. Here, we analyze this unique set of properties and conclude that the N-phase DSs must naturally be the most efficient optimizers: on one hand, N-phase DSs have integrable flows with well-defined attractors that can be associated with candidate solutions and, on the other hand, the noise-induced attractor-to-attractor dynamics in the N-phase is effectively chaotic or aperiodic so that a DS must avoid revisiting solutions/attractors thus accelerating the search for the best solution. Based on this understanding, we propose a method for stochastic dynamical optimization using the N-phase DSs. This method can be viewed as a hybrid of the simulated and chaotic annealing methods. Our proposition can result in a new generation of hardware devices for efficient solution of various search and/or combinatorial optimization problems.

  16. Pattern-based integer sample motion search strategies in the context of HEVC

    NASA Astrophysics Data System (ADS)

    Maier, Georg; Bross, Benjamin; Grois, Dan; Marpe, Detlev; Schwarz, Heiko; Veltkamp, Remco C.; Wiegand, Thomas

    2015-09-01

    The H.265/MPEG-H High Efficiency Video Coding (HEVC) standard provides a significant increase in coding efficiency compared to its predecessor, the H.264/MPEG-4 Advanced Video Coding (AVC) standard, which however comes at the cost of a high computational burden for a compliant encoder. Motion estimation (ME), which is a part of the inter-picture prediction process, typically consumes a high amount of computational resources, while significantly increasing the coding efficiency. In spite of the fact that both H.265/MPEG-H HEVC and H.264/MPEG-4 AVC standards allow processing motion information on a fractional sample level, the motion search algorithms based on the integer sample level remain to be an integral part of ME. In this paper, a flexible integer sample ME framework is proposed, thereby allowing to trade off significant reduction of ME computation time versus coding efficiency penalty in terms of bit rate overhead. As a result, through extensive experimentation, an integer sample ME algorithm that provides a good trade-off is derived, incorporating a combination and optimization of known predictive, pattern-based and early termination techniques. The proposed ME framework is implemented on a basis of the HEVC Test Model (HM) reference software, further being compared to the state-of-the-art fast search algorithm, which is a native part of HM. It is observed that for high resolution sequences, the integer sample ME process can be speed-up by factors varying from 3.2 to 7.6, resulting in the bit-rate overhead of 1.5% and 0.6% for Random Access (RA) and Low Delay P (LDP) configurations, respectively. In addition, the similar speed-up is observed for sequences with mainly Computer-Generated Imagery (CGI) content while trading off the bit rate overhead of up to 5.2%.

  17. A Polynomial Time, Numerically Stable Integer Relation Algorithm

    NASA Technical Reports Server (NTRS)

    Ferguson, Helaman R. P.; Bailey, Daivd H.; Kutler, Paul (Technical Monitor)

    1998-01-01

    Let x = (x1, x2...,xn be a vector of real numbers. X is said to possess an integer relation if there exist integers a(sub i) not all zero such that a1x1 + a2x2 + ... a(sub n)Xn = 0. Beginning in 1977 several algorithms (with proofs) have been discovered to recover the a(sub i) given x. The most efficient of these existing integer relation algorithms (in terms of run time and the precision required of the input) has the drawback of being very unstable numerically. It often requires a numeric precision level in the thousands of digits to reliably recover relations in modest-sized test problems. We present here a new algorithm for finding integer relations, which we have named the "PSLQ" algorithm. It is proved in this paper that the PSLQ algorithm terminates with a relation in a number of iterations that is bounded by a polynomial in it. Because this algorithm employs a numerically stable matrix reduction procedure, it is free from the numerical difficulties, that plague other integer relation algorithms. Furthermore, its stability admits an efficient implementation with lower run times oil average than other algorithms currently in Use. Finally, this stability can be used to prove that relation bounds obtained from computer runs using this algorithm are numerically accurate.

  18. LEO cooperative multi-spacecraft refueling mission optimization considering J2 perturbation and target's surplus propellant constraint

    NASA Astrophysics Data System (ADS)

    Zhao, Zhao; Zhang, Jin; Li, Hai-yang; Zhou, Jian-yong

    2017-01-01

    The optimization of an LEO cooperative multi-spacecraft refueling mission considering the J2 perturbation and target's surplus propellant constraint is studied in the paper. First, a mission scenario is introduced. One service spacecraft and several target spacecraft run on an LEO near-circular orbit, the service spacecraft rendezvouses with some service positions one by one, and target spacecraft transfer to corresponding service positions respectively. Each target spacecraft returns to its original position after obtaining required propellant and the service spacecraft returns to its original position after refueling all target spacecraft. Next, an optimization model of this mission is built. The service sequence, orbital transfer time, and service position are used as deign variables, whereas the propellant cost is used as the design objective. The J2 perturbation, time constraint and the target spacecraft's surplus propellant capability constraint are taken into account. Then, a hybrid two-level optimization approach is presented to solve the formulated mixed integer nonlinear programming (MINLP) problem. A hybrid-encoding genetic algorithm is adopted to seek the near optimal solution in the up-level optimization, while a linear relative dynamic equation considering the J2 perturbation is used to obtain the impulses of orbital transfer in the low-level optimization. Finally, the effectiveness of the proposed model and method is validated by numerical examples.

  19. Anisotropic fractal media by vector calculus in non-integer dimensional space

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

    Tarasov, Vasily E., E-mail: tarasov@theory.sinp.msu.ru

    2014-08-15

    A review of different approaches to describe anisotropic fractal media is proposed. In this paper, differentiation and integration non-integer dimensional and multi-fractional spaces are considered as tools to describe anisotropic fractal materials and media. We suggest a generalization of vector calculus for non-integer dimensional space by using a product measure method. The product of fractional and non-integer dimensional spaces allows us to take into account the anisotropy of the fractal media in the framework of continuum models. The integration over non-integer-dimensional spaces is considered. In this paper differential operators of first and second orders for fractional space and non-integer dimensionalmore » space are suggested. The differential operators are defined as inverse operations to integration in spaces with non-integer dimensions. Non-integer dimensional space that is product of spaces with different dimensions allows us to give continuum models for anisotropic type of the media. The Poisson's equation for fractal medium, the Euler-Bernoulli fractal beam, and the Timoshenko beam equations for fractal material are considered as examples of application of suggested generalization of vector calculus for anisotropic fractal materials and media.« less

  20. Anisotropic fractal media by vector calculus in non-integer dimensional space

    NASA Astrophysics Data System (ADS)

    Tarasov, Vasily E.

    2014-08-01

    A review of different approaches to describe anisotropic fractal media is proposed. In this paper, differentiation and integration non-integer dimensional and multi-fractional spaces are considered as tools to describe anisotropic fractal materials and media. We suggest a generalization of vector calculus for non-integer dimensional space by using a product measure method. The product of fractional and non-integer dimensional spaces allows us to take into account the anisotropy of the fractal media in the framework of continuum models. The integration over non-integer-dimensional spaces is considered. In this paper differential operators of first and second orders for fractional space and non-integer dimensional space are suggested. The differential operators are defined as inverse operations to integration in spaces with non-integer dimensions. Non-integer dimensional space that is product of spaces with different dimensions allows us to give continuum models for anisotropic type of the media. The Poisson's equation for fractal medium, the Euler-Bernoulli fractal beam, and the Timoshenko beam equations for fractal material are considered as examples of application of suggested generalization of vector calculus for anisotropic fractal materials and media.

  1. Optimization of Energy Efficiency and Conservation in Green Building Design Using Duelist, Killer-Whale and Rain-Water Algorithms

    NASA Astrophysics Data System (ADS)

    Biyanto, T. R.; Matradji; Syamsi, M. N.; Fibrianto, H. Y.; Afdanny, N.; Rahman, A. H.; Gunawan, K. S.; Pratama, J. A. D.; Malwindasari, A.; Abdillah, A. I.; Bethiana, T. N.; Putra, Y. A.

    2017-11-01

    The development of green building has been growing in both design and quality. The development of green building was limited by the issue of expensive investment. Actually, green building can reduce the energy usage inside the building especially in utilization of cooling system. External load plays major role in reducing the usage of cooling system. External load is affected by type of wall sheathing, glass and roof. The proper selection of wall, type of glass and roof material are very important to reduce external load. Hence, the optimization of energy efficiency and conservation in green building design is required. Since this optimization consist of integer and non-linear equations, this problem falls into Mixed-Integer-Non-Linear-Programming (MINLP) that required global optimization technique such as stochastic optimization algorithms. In this paper the optimized variables i.e. type of glass and roof were chosen using Duelist, Killer-Whale and Rain-Water Algorithms to obtain the optimum energy and considering the minimal investment. The optimization results exhibited the single glass Planibel-G with the 3.2 mm thickness and glass wool insulation provided maximum ROI of 36.8486%, EUI reduction of 54 kWh/m2·year, CO2 emission reduction of 486.8971 tons/year and reduce investment of 4,078,905,465 IDR.

  2. Quadratic constrained mixed discrete optimization with an adiabatic quantum optimizer

    NASA Astrophysics Data System (ADS)

    Chandra, Rishabh; Jacobson, N. Tobias; Moussa, Jonathan E.; Frankel, Steven H.; Kais, Sabre

    2014-07-01

    We extend the family of problems that may be implemented on an adiabatic quantum optimizer (AQO). When a quadratic optimization problem has at least one set of discrete controls and the constraints are linear, we call this a quadratic constrained mixed discrete optimization (QCMDO) problem. QCMDO problems are NP-hard, and no efficient classical algorithm for their solution is known. Included in the class of QCMDO problems are combinatorial optimization problems constrained by a linear partial differential equation (PDE) or system of linear PDEs. An essential complication commonly encountered in solving this type of problem is that the linear constraint may introduce many intermediate continuous variables into the optimization while the computational cost grows exponentially with problem size. We resolve this difficulty by developing a constructive mapping from QCMDO to quadratic unconstrained binary optimization (QUBO) such that the size of the QUBO problem depends only on the number of discrete control variables. With a suitable embedding, taking into account the physical constraints of the realizable coupling graph, the resulting QUBO problem can be implemented on an existing AQO. The mapping itself is efficient, scaling cubically with the number of continuous variables in the general case and linearly in the PDE case if an efficient preconditioner is available.

  3. RSM 1.0 user's guide: A resupply scheduler using integer optimization

    NASA Technical Reports Server (NTRS)

    Viterna, Larry A.; Green, Robert D.; Reed, David M.

    1991-01-01

    The Resupply Scheduling Model (RSM) is a PC based, fully menu-driven computer program. It uses integer programming techniques to determine an optimum schedule to replace components on or before a fixed replacement period, subject to user defined constraints such as transportation mass and volume limits or available repair crew time. Principal input for RSJ includes properties such as mass and volume and an assembly sequence. Resource constraints are entered for each period corresponding to the component properties. Though written to analyze the electrical power system on the Space Station Freedom, RSM is quite general and can be used to model the resupply of almost any system subject to user defined resource constraints. Presented here is a step by step procedure for preparing the input, performing the analysis, and interpreting the results. Instructions for installing the program and information on the algorithms are given.

  4. Teachers' Construction of Meanings of Signed Quantities and Integer Operation

    ERIC Educational Resources Information Center

    Kumar, Ruchi S.; Subramaniam, K.; Naik, Shweta Shripad

    2017-01-01

    Understanding signed quantities and its arithmetic is one of the challenging topics of middle school mathematics. The "specialized content knowledge" (SCK) for teaching integers includes understanding of a variety of representations that may be used while teaching. In this study, we argue that meanings of integers and integer operations…

  5. Mixed-Integer Nonconvex Quadratic Optimization Relaxations and Performance Analysis

    DTIC Science & Technology

    2016-10-11

    Analysis of Interior Point Algorithms for Non-Lipschitz and Nonconvex Minimization,” (W. Bian, X. Chen, and Ye), Math Programming, 149 (2015) 301-327...Chen, Ge, Wang, Ye), Math Programming, 143 (1-2) (2014) 371-383. This paper resolved an important open question in cardinality constrained...Statistical Performance, and Algorithmic Theory for Local Solutions,” (H. Liu, T. Yao, R. Li, Y. Ye) manuscript, 2nd revision in Math Programming

  6. On-orbit servicing system assessment and optimization methods based on lifecycle simulation under mixed aleatory and epistemic uncertainties

    NASA Astrophysics Data System (ADS)

    Yao, Wen; Chen, Xiaoqian; Huang, Yiyong; van Tooren, Michel

    2013-06-01

    To assess the on-orbit servicing (OOS) paradigm and optimize its utilities by taking advantage of its inherent flexibility and responsiveness, the OOS system assessment and optimization methods based on lifecycle simulation under uncertainties are studied. The uncertainty sources considered in this paper include both the aleatory (random launch/OOS operation failure and on-orbit component failure) and the epistemic (the unknown trend of the end-used market price) types. Firstly, the lifecycle simulation under uncertainties is discussed. The chronological flowchart is presented. The cost and benefit models are established, and the uncertainties thereof are modeled. The dynamic programming method to make optimal decision in face of the uncertain events is introduced. Secondly, the method to analyze the propagation effects of the uncertainties on the OOS utilities is studied. With combined probability and evidence theory, a Monte Carlo lifecycle Simulation based Unified Uncertainty Analysis (MCS-UUA) approach is proposed, based on which the OOS utility assessment tool under mixed uncertainties is developed. Thirdly, to further optimize the OOS system under mixed uncertainties, the reliability-based optimization (RBO) method is studied. To alleviate the computational burden of the traditional RBO method which involves nested optimum search and uncertainty analysis, the framework of Sequential Optimization and Mixed Uncertainty Analysis (SOMUA) is employed to integrate MCS-UUA, and the RBO algorithm SOMUA-MCS is developed. Fourthly, a case study on the OOS system for a hypothetical GEO commercial communication satellite is investigated with the proposed assessment tool. Furthermore, the OOS system is optimized with SOMUA-MCS. Lastly, some conclusions are given and future research prospects are highlighted.

  7. Adaptive critics for dynamic optimization.

    PubMed

    Kulkarni, Raghavendra V; Venayagamoorthy, Ganesh Kumar

    2010-06-01

    A novel action-dependent adaptive critic design (ACD) is developed for dynamic optimization. The proposed combination of a particle swarm optimization-based actor and a neural network critic is demonstrated through dynamic sleep scheduling of wireless sensor motes for wildlife monitoring. The objective of the sleep scheduler is to dynamically adapt the sleep duration to node's battery capacity and movement pattern of animals in its environment in order to obtain snapshots of the animal on its trajectory uniformly. Simulation results show that the sleep time of the node determined by the actor critic yields superior quality of sensory data acquisition and enhanced node longevity. Copyright 2010 Elsevier Ltd. All rights reserved.

  8. Robust Dynamic Multi-objective Vehicle Routing Optimization Method.

    PubMed

    Guo, Yi-Nan; Cheng, Jian; Luo, Sha; Gong, Dun-Wei

    2017-03-21

    For dynamic multi-objective vehicle routing problems, the waiting time of vehicle, the number of serving vehicles, the total distance of routes were normally considered as the optimization objectives. Except for above objectives, fuel consumption that leads to the environmental pollution and energy consumption was focused on in this paper. Considering the vehicles' load and the driving distance, corresponding carbon emission model was built and set as an optimization objective. Dynamic multi-objective vehicle routing problems with hard time windows and randomly appeared dynamic customers, subsequently, were modeled. In existing planning methods, when the new service demand came up, global vehicle routing optimization method was triggered to find the optimal routes for non-served customers, which was time-consuming. Therefore, robust dynamic multi-objective vehicle routing method with two-phase is proposed. Three highlights of the novel method are: (i) After finding optimal robust virtual routes for all customers by adopting multi-objective particle swarm optimization in the first phase, static vehicle routes for static customers are formed by removing all dynamic customers from robust virtual routes in next phase. (ii)The dynamically appeared customers append to be served according to their service time and the vehicles' statues. Global vehicle routing optimization is triggered only when no suitable locations can be found for dynamic customers. (iii)A metric measuring the algorithms' robustness is given. The statistical results indicated that the routes obtained by the proposed method have better stability and robustness, but may be sub-optimum. Moreover, time-consuming global vehicle routing optimization is avoided as dynamic customers appear.

  9. An Investigation of Generalized Differential Evolution Metaheuristic for Multiobjective Optimal Crop-Mix Planning Decision

    PubMed Central

    Olugbara, Oludayo

    2014-01-01

    This paper presents an annual multiobjective crop-mix planning as a problem of concurrent maximization of net profit and maximization of crop production to determine an optimal cropping pattern. The optimal crop production in a particular planting season is a crucial decision making task from the perspectives of economic management and sustainable agriculture. A multiobjective optimal crop-mix problem is formulated and solved using the generalized differential evolution 3 (GDE3) metaheuristic to generate a globally optimal solution. The performance of the GDE3 metaheuristic is investigated by comparing its results with the results obtained using epsilon constrained and nondominated sorting genetic algorithms—being two representatives of state-of-the-art in evolutionary optimization. The performance metrics of additive epsilon, generational distance, inverted generational distance, and spacing are considered to establish the comparability. In addition, a graphical comparison with respect to the true Pareto front for the multiobjective optimal crop-mix planning problem is presented. Empirical results generally show GDE3 to be a viable alternative tool for solving a multiobjective optimal crop-mix planning problem. PMID:24883369

  10. An investigation of generalized differential evolution metaheuristic for multiobjective optimal crop-mix planning decision.

    PubMed

    Adekanmbi, Oluwole; Olugbara, Oludayo; Adeyemo, Josiah

    2014-01-01

    This paper presents an annual multiobjective crop-mix planning as a problem of concurrent maximization of net profit and maximization of crop production to determine an optimal cropping pattern. The optimal crop production in a particular planting season is a crucial decision making task from the perspectives of economic management and sustainable agriculture. A multiobjective optimal crop-mix problem is formulated and solved using the generalized differential evolution 3 (GDE3) metaheuristic to generate a globally optimal solution. The performance of the GDE3 metaheuristic is investigated by comparing its results with the results obtained using epsilon constrained and nondominated sorting genetic algorithms-being two representatives of state-of-the-art in evolutionary optimization. The performance metrics of additive epsilon, generational distance, inverted generational distance, and spacing are considered to establish the comparability. In addition, a graphical comparison with respect to the true Pareto front for the multiobjective optimal crop-mix planning problem is presented. Empirical results generally show GDE3 to be a viable alternative tool for solving a multiobjective optimal crop-mix planning problem.

  11. Optimal Facility Location Tool for Logistics Battle Command (LBC)

    DTIC Science & Technology

    2015-08-01

    64 Appendix B. VBA Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 Appendix C. Story...should city planners have located emergency service facilities so that all households (the demand) had equal access to coverage?” The critical...programming language called Visual Basic for Applications ( VBA ). CPLEX is a commercial solver for linear, integer, and mixed integer linear programming problems

  12. The Optimization dispatching of Micro Grid Considering Load Control

    NASA Astrophysics Data System (ADS)

    Zhang, Pengfei; Xie, Jiqiang; Yang, Xiu; He, Hongli

    2018-01-01

    This paper proposes an optimization control of micro-grid system economy operation model. It coordinates the new energy and storage operation with diesel generator output, so as to achieve the economic operation purpose of micro-grid. In this paper, the micro-grid network economic operation model is transformed into mixed integer programming problem, which is solved by the mature commercial software, and the new model is proved to be economical, and the load control strategy can reduce the charge and discharge times of energy storage devices, and extend the service life of the energy storage device to a certain extent.

  13. Random crystal field effects on the integer and half-integer mixed-spin system

    NASA Astrophysics Data System (ADS)

    Yigit, Ali; Albayrak, Erhan

    2018-05-01

    In this work, we have focused on the random crystal field effects on the phase diagrams of the mixed spin-1 and spin-5/2 Ising system obtained by utilizing the exact recursion relations (ERR) on the Bethe lattice (BL). The distribution function P(Di) = pδ [Di - D(1 + α) ] +(1 - p) δ [Di - D(1 - α) ] is used to randomize the crystal field.The phase diagrams are found to exhibit second- and first-order phase transitions depending on the values of α, D and p. It is also observed that the model displays tricritical point, isolated point, critical end point and three compensation temperatures for suitable values of the system parameters.

  14. Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks

    NASA Technical Reports Server (NTRS)

    Lee, Charles H.; Cheung, Kar-Ming

    2012-01-01

    In this paper, we propose to solve the constrained optimization problem in two phases. The first phase uses heuristic methods such as the ant colony method, particle swarming optimization, and genetic algorithm to seek a near optimal solution among a list of feasible initial populations. The final optimal solution can be found by using the solution of the first phase as the initial condition to the SQP algorithm. We demonstrate the above problem formulation and optimization schemes with a large-scale network that includes the DSN ground stations and a number of spacecraft of deep space missions.

  15. A Paper-and-Pencil gcd Algorithm for Gaussian Integers

    ERIC Educational Resources Information Center

    Szabo, Sandor

    2005-01-01

    As with natural numbers, a greatest common divisor of two Gaussian (complex) integers "a" and "b" is a Gaussian integer "d" that is a common divisor of both "a" and "b". This article explores an algorithm for such gcds that is easy to do by hand.

  16. Dynamic optimization case studies in DYNOPT tool

    NASA Astrophysics Data System (ADS)

    Ozana, Stepan; Pies, Martin; Docekal, Tomas

    2016-06-01

    Dynamic programming is typically applied to optimization problems. As the analytical solutions are generally very difficult, chosen software tools are used widely. These software packages are often third-party products bound for standard simulation software tools on the market. As typical examples of such tools, TOMLAB and DYNOPT could be effectively applied for solution of problems of dynamic programming. DYNOPT will be presented in this paper due to its licensing policy (free product under GPL) and simplicity of use. DYNOPT is a set of MATLAB functions for determination of optimal control trajectory by given description of the process, the cost to be minimized, subject to equality and inequality constraints, using orthogonal collocation on finite elements method. The actual optimal control problem is solved by complete parameterization both the control and the state profile vector. It is assumed, that the optimized dynamic model may be described by a set of ordinary differential equations (ODEs) or differential-algebraic equations (DAEs). This collection of functions extends the capability of the MATLAB Optimization Tool-box. The paper will introduce use of DYNOPT in the field of dynamic optimization problems by means of case studies regarding chosen laboratory physical educational models.

  17. Fuzzy Energy and Reserve Co-optimization With High Penetration of Renewable Energy

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

    Liu, Cong; Botterud, Audun; Zhou, Zhi

    In this study, we propose a fuzzy-based energy and reserve co-optimization model with consideration of high penetration of renewable energy. Under the assumption of a fixed uncertainty set of renewables, a two-stage robust model is proposed for clearing energy and reserves in the first stage and checking the feasibility and robustness of re-dispatches in the second stage. Fuzzy sets and their membership functions are introduced into the optimization model to represent the satisfaction degree of the variable uncertainty sets. The lower bound of the uncertainty set is expressed as fuzzy membership functions. The solutions are obtained by transforming the fuzzymore » mathematical programming formulation into traditional mixed integer linear programming problems.« less

  18. Fuzzy Energy and Reserve Co-optimization With High Penetration of Renewable Energy

    DOE PAGES

    Liu, Cong; Botterud, Audun; Zhou, Zhi; ...

    2016-10-21

    In this study, we propose a fuzzy-based energy and reserve co-optimization model with consideration of high penetration of renewable energy. Under the assumption of a fixed uncertainty set of renewables, a two-stage robust model is proposed for clearing energy and reserves in the first stage and checking the feasibility and robustness of re-dispatches in the second stage. Fuzzy sets and their membership functions are introduced into the optimization model to represent the satisfaction degree of the variable uncertainty sets. The lower bound of the uncertainty set is expressed as fuzzy membership functions. The solutions are obtained by transforming the fuzzymore » mathematical programming formulation into traditional mixed integer linear programming problems.« less

  19. Integer sequence discovery from small graphs

    PubMed Central

    Hoppe, Travis; Petrone, Anna

    2015-01-01

    We have exhaustively enumerated all simple, connected graphs of a finite order and have computed a selection of invariants over this set. Integer sequences were constructed from these invariants and checked against the Online Encyclopedia of Integer Sequences (OEIS). 141 new sequences were added and six sequences were extended. From the graph database, we were able to programmatically suggest relationships among the invariants. It will be shown that we can readily visualize any sequence of graphs with a given criteria. The code has been released as an open-source framework for further analysis and the database was constructed to be extensible to invariants not considered in this work. PMID:27034526

  20. Parallel integer sorting with medium and fine-scale parallelism

    NASA Technical Reports Server (NTRS)

    Dagum, Leonardo

    1993-01-01

    Two new parallel integer sorting algorithms, queue-sort and barrel-sort, are presented and analyzed in detail. These algorithms do not have optimal parallel complexity, yet they show very good performance in practice. Queue-sort designed for fine-scale parallel architectures which allow the queueing of multiple messages to the same destination. Barrel-sort is designed for medium-scale parallel architectures with a high message passing overhead. The performance results from the implementation of queue-sort on a Connection Machine CM-2 and barrel-sort on a 128 processor iPSC/860 are given. The two implementations are found to be comparable in performance but not as good as a fully vectorized bucket sort on the Cray YMP.

  1. Dynamic optimization and adaptive controller design

    NASA Astrophysics Data System (ADS)

    Inamdar, S. R.

    2010-10-01

    In this work I present a new type of controller which is an adaptive tracking controller which employs dynamic optimization for optimizing current value of controller action for the temperature control of nonisothermal continuously stirred tank reactor (CSTR). We begin with a two-state model of nonisothermal CSTR which are mass and heat balance equations and then add cooling system dynamics to eliminate input multiplicity. The initial design value is obtained using local stability of steady states where approach temperature for cooling action is specified as a steady state and a design specification. Later we make a correction in the dynamics where material balance is manipulated to use feed concentration as a system parameter as an adaptive control measure in order to avoid actuator saturation for the main control loop. The analysis leading to design of dynamic optimization based parameter adaptive controller is presented. The important component of this mathematical framework is reference trajectory generation to form an adaptive control measure.

  2. Computational fluid dynamics study on mixing mode and power consumption in anaerobic mono- and co-digestion.

    PubMed

    Zhang, Yuan; Yu, Guangren; Yu, Liang; Siddhu, Muhammad Abdul Hanan; Gao, Mengjiao; Abdeltawab, Ahmed A; Al-Deyab, Salem S; Chen, Xiaochun

    2016-03-01

    Computational fluid dynamics (CFD) was applied to investigate mixing mode and power consumption in anaerobic mono- and co-digestion. Cattle manure (CM) and corn stover (CS) were used as feedstock and stirred tank reactor (STR) was used as digester. Power numbers obtained by the CFD simulation were compared with those from the experimental correlation. Results showed that the standard k-ε model was more appropriate than other turbulence models. A new index, net power production instead of gas production, was proposed to optimize feedstock ratio for anaerobic co-digestion. Results showed that flow field and power consumption were significantly changed in co-digestion of CM and CS compared with those in mono-digestion of either CM or CS. For different mixing modes, the optimum feedstock ratio for co-digestion changed with net power production. The best option of CM/CS ratio for continuous mixing, intermittent mixing I, and intermittent mixing II were 1:1, 1:1 and 1:3, respectively. Copyright © 2016. Published by Elsevier Ltd.

  3. Optimal satisfaction degree in energy harvesting cognitive radio networks

    NASA Astrophysics Data System (ADS)

    Li, Zan; Liu, Bo-Yang; Si, Jiang-Bo; Zhou, Fu-Hui

    2015-12-01

    A cognitive radio (CR) network with energy harvesting (EH) is considered to improve both spectrum efficiency and energy efficiency. A hidden Markov model (HMM) is used to characterize the imperfect spectrum sensing process. In order to maximize the whole satisfaction degree (WSD) of the cognitive radio network, a tradeoff between the average throughput of the secondary user (SU) and the interference to the primary user (PU) is analyzed. We formulate the satisfaction degree optimization problem as a mixed integer nonlinear programming (MINLP) problem. The satisfaction degree optimization problem is solved by using differential evolution (DE) algorithm. The proposed optimization problem allows the network to adaptively achieve the optimal solution based on its required quality of service (Qos). Numerical results are given to verify our analysis. Project supported by the National Natural Science Foundation of China (Grant No. 61301179), the Doctorial Programs Foundation of the Ministry of Education of China (Grant No. 20110203110011), and the 111 Project (Grant No. B08038).

  4. COMPARISON OF NONLINEAR DYNAMICS OPTIMIZATION METHODS FOR APS-U

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

    Sun, Y.; Borland, Michael

    Many different objectives and genetic algorithms have been proposed for storage ring nonlinear dynamics performance optimization. These optimization objectives include nonlinear chromaticities and driving/detuning terms, on-momentum and off-momentum dynamic acceptance, chromatic detuning, local momentum acceptance, variation of transverse invariant, Touschek lifetime, etc. In this paper, the effectiveness of several different optimization methods and objectives are compared for the nonlinear beam dynamics optimization of the Advanced Photon Source upgrade (APS-U) lattice. The optimized solutions from these different methods are preliminarily compared in terms of the dynamic acceptance, local momentum acceptance, chromatic detuning, and other performance measures.

  5. Statistical analysis of the limitation of half integer resonances on the available momentum acceptance of the High Energy Photon Source

    NASA Astrophysics Data System (ADS)

    Jiao, Yi; Duan, Zhe

    2017-01-01

    In a diffraction-limited storage ring, half integer resonances can have strong effects on the beam dynamics, associated with the large detuning terms from the strong focusing and strong sextupoles as required for an ultralow emittance. In this study, the limitation of half integer resonances on the available momentum acceptance (MA) was statistically analyzed based on one design of the High Energy Photon Source (HEPS). It was found that the probability of MA reduction due to crossing of half integer resonances is closely correlated with the level of beta beats at the nominal tunes, but independent of the error sources. The analysis indicated that for the presented HEPS lattice design, the rms amplitude of beta beats should be kept below 1.5% horizontally and 2.5% vertically to reach a small MA reduction probability of about 1%.

  6. Combinatorial therapy discovery using mixed integer linear programming.

    PubMed

    Pang, Kaifang; Wan, Ying-Wooi; Choi, William T; Donehower, Lawrence A; Sun, Jingchun; Pant, Dhruv; Liu, Zhandong

    2014-05-15

    Combinatorial therapies play increasingly important roles in combating complex diseases. Owing to the huge cost associated with experimental methods in identifying optimal drug combinations, computational approaches can provide a guide to limit the search space and reduce cost. However, few computational approaches have been developed for this purpose, and thus there is a great need of new algorithms for drug combination prediction. Here we proposed to formulate the optimal combinatorial therapy problem into two complementary mathematical algorithms, Balanced Target Set Cover (BTSC) and Minimum Off-Target Set Cover (MOTSC). Given a disease gene set, BTSC seeks a balanced solution that maximizes the coverage on the disease genes and minimizes the off-target hits at the same time. MOTSC seeks a full coverage on the disease gene set while minimizing the off-target set. Through simulation, both BTSC and MOTSC demonstrated a much faster running time over exhaustive search with the same accuracy. When applied to real disease gene sets, our algorithms not only identified known drug combinations, but also predicted novel drug combinations that are worth further testing. In addition, we developed a web-based tool to allow users to iteratively search for optimal drug combinations given a user-defined gene set. Our tool is freely available for noncommercial use at http://www.drug.liuzlab.org/. zhandong.liu@bcm.edu Supplementary data are available at Bioinformatics online.

  7. Optimization Model for Web Based Multimodal Interactive Simulations.

    PubMed

    Halic, Tansel; Ahn, Woojin; De, Suvranu

    2015-07-15

    This paper presents a technique for optimizing the performance of web based multimodal interactive simulations. For such applications where visual quality and the performance of simulations directly influence user experience, overloading of hardware resources may result in unsatisfactory reduction in the quality of the simulation and user satisfaction. However, optimization of simulation performance on individual hardware platforms is not practical. Hence, we present a mixed integer programming model to optimize the performance of graphical rendering and simulation performance while satisfying application specific constraints. Our approach includes three distinct phases: identification, optimization and update . In the identification phase, the computing and rendering capabilities of the client device are evaluated using an exploratory proxy code. This data is utilized in conjunction with user specified design requirements in the optimization phase to ensure best possible computational resource allocation. The optimum solution is used for rendering (e.g. texture size, canvas resolution) and simulation parameters (e.g. simulation domain) in the update phase. Test results are presented on multiple hardware platforms with diverse computing and graphics capabilities to demonstrate the effectiveness of our approach.

  8. Optimization Model for Web Based Multimodal Interactive Simulations

    PubMed Central

    Halic, Tansel; Ahn, Woojin; De, Suvranu

    2015-01-01

    This paper presents a technique for optimizing the performance of web based multimodal interactive simulations. For such applications where visual quality and the performance of simulations directly influence user experience, overloading of hardware resources may result in unsatisfactory reduction in the quality of the simulation and user satisfaction. However, optimization of simulation performance on individual hardware platforms is not practical. Hence, we present a mixed integer programming model to optimize the performance of graphical rendering and simulation performance while satisfying application specific constraints. Our approach includes three distinct phases: identification, optimization and update. In the identification phase, the computing and rendering capabilities of the client device are evaluated using an exploratory proxy code. This data is utilized in conjunction with user specified design requirements in the optimization phase to ensure best possible computational resource allocation. The optimum solution is used for rendering (e.g. texture size, canvas resolution) and simulation parameters (e.g. simulation domain) in the update phase. Test results are presented on multiple hardware platforms with diverse computing and graphics capabilities to demonstrate the effectiveness of our approach. PMID:26085713

  9. Dynamic Flow Management Problems in Air Transportation

    NASA Technical Reports Server (NTRS)

    Patterson, Sarah Stock

    1997-01-01

    In 1995, over six hundred thousand licensed pilots flew nearly thirty-five million flights into over eighteen thousand U.S. airports, logging more than 519 billion passenger miles. Since demand for air travel has increased by more than 50% in the last decade while capacity has stagnated, congestion is a problem of undeniable practical significance. In this thesis, we will develop optimization techniques that reduce the impact of congestion on the national airspace. We start by determining the optimal release times for flights into the airspace and the optimal speed adjustment while airborne taking into account the capacitated airspace. This is called the Air Traffic Flow Management Problem (TFMP). We address the complexity, showing that it is NP-hard. We build an integer programming formulation that is quite strong as some of the proposed inequalities are facet defining for the convex hull of solutions. For practical problems, the solutions of the LP relaxation of the TFMP are very often integral. In essence, we reduce the problem to efficiently solving large scale linear programming problems. Thus, the computation times are reasonably small for large scale, practical problems involving thousands of flights. Next, we address the problem of determining how to reroute aircraft in the airspace system when faced with dynamically changing weather conditions. This is called the Air Traffic Flow Management Rerouting Problem (TFMRP) We present an integrated mathematical programming approach for the TFMRP, which utilizes several methodologies, in order to minimize delay costs. In order to address the high dimensionality, we present an aggregate model, in which we formulate the TFMRP as a multicommodity, integer, dynamic network flow problem with certain side constraints. Using Lagrangian relaxation, we generate aggregate flows that are decomposed into a collection of flight paths using a randomized rounding heuristic. This collection of paths is used in a packing integer

  10. Application of Mixed H2/H Infinity Optimization

    DTIC Science & Technology

    1991-11-01

    Standard Form .................. . 29 4.4 a-Plot of the Open Loop System .. ......... .. 31 4.5 o-Plot of the H2 & H. Optimal Td ........... . 32 4.6 o...o.34 4.9 o-Plot of Mixed SolutiCo,,, Te.. .......... 35 4.10 a-Plot of H. Central Solution Td ........ 36 4.11 a-Plot of the Mixed Controllers...norm = 3.7) .. .. ......... 41 4.18 a3-Plot of Ted (V-norm 2.8) .......... o.42 v 4.19 a-Plot of Td (ac-norm = 2.5) . . . . . .. 0 42 4.20 a-Plot

  11. Optimal Weights Mixed Filter for removing mixture of Gaussian and impulse noises

    PubMed Central

    Grama, Ion; Liu, Quansheng

    2017-01-01

    In this paper we consider the problem of restoration of a image contaminated by a mixture of Gaussian and impulse noises. We propose a new statistic called ROADGI which improves the well-known Rank-Ordered Absolute Differences (ROAD) statistic for detecting points contaminated with the impulse noise in this context. Combining ROADGI statistic with the method of weights optimization we obtain a new algorithm called Optimal Weights Mixed Filter (OWMF) to deal with the mixed noise. Our simulation results show that the proposed filter is effective for mixed noises, as well as for single impulse noise and for single Gaussian noise. PMID:28692667

  12. Optimal Weights Mixed Filter for removing mixture of Gaussian and impulse noises.

    PubMed

    Jin, Qiyu; Grama, Ion; Liu, Quansheng

    2017-01-01

    In this paper we consider the problem of restoration of a image contaminated by a mixture of Gaussian and impulse noises. We propose a new statistic called ROADGI which improves the well-known Rank-Ordered Absolute Differences (ROAD) statistic for detecting points contaminated with the impulse noise in this context. Combining ROADGI statistic with the method of weights optimization we obtain a new algorithm called Optimal Weights Mixed Filter (OWMF) to deal with the mixed noise. Our simulation results show that the proposed filter is effective for mixed noises, as well as for single impulse noise and for single Gaussian noise.

  13. Ionic force field optimization based on single-ion and ion-pair solvation properties: Going beyond standard mixing rules

    NASA Astrophysics Data System (ADS)

    Fyta, Maria; Netz, Roland R.

    2012-03-01

    Using molecular dynamics (MD) simulations in conjunction with the SPC/E water model, we optimize ionic force-field parameters for seven different halide and alkali ions, considering a total of eight ion-pairs. Our strategy is based on simultaneous optimizing single-ion and ion-pair properties, i.e., we first fix ion-water parameters based on single-ion solvation free energies, and in a second step determine the cation-anion interaction parameters (traditionally given by mixing or combination rules) based on the Kirkwood-Buff theory without modification of the ion-water interaction parameters. In doing so, we have introduced scaling factors for the cation-anion Lennard-Jones (LJ) interaction that quantify deviations from the standard mixing rules. For the rather size-symmetric salt solutions involving bromide and chloride ions, the standard mixing rules work fine. On the other hand, for the iodide and fluoride solutions, corresponding to the largest and smallest anion considered in this work, a rescaling of the mixing rules was necessary. For iodide, the experimental activities suggest more tightly bound ion pairing than given by the standard mixing rules, which is achieved in simulations by reducing the scaling factor of the cation-anion LJ energy. For fluoride, the situation is different and the simulations show too large attraction between fluoride and cations when compared with experimental data. For NaF, the situation can be rectified by increasing the cation-anion LJ energy. For KF, it proves necessary to increase the effective cation-anion Lennard-Jones diameter. The optimization strategy outlined in this work can be easily adapted to different kinds of ions.

  14. Online optimization of storage ring nonlinear beam dynamics

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

    Huang, Xiaobiao; Safranek, James

    2015-08-01

    We propose to optimize the nonlinear beam dynamics of existing and future storage rings with direct online optimization techniques. This approach may have crucial importance for the implementation of diffraction limited storage rings. In this paper considerations and algorithms for the online optimization approach are discussed. We have applied this approach to experimentally improve the dynamic aperture of the SPEAR3 storage ring with the robust conjugate direction search method and the particle swarm optimization method. The dynamic aperture was improved by more than 5 mm within a short period of time. Experimental setup and results are presented.

  15. A simple approximation of moments of the quasi-equilibrium distribution of an extended stochastic theta-logistic model with non-integer powers.

    PubMed

    Bhowmick, Amiya Ranjan; Bandyopadhyay, Subhadip; Rana, Sourav; Bhattacharya, Sabyasachi

    2016-01-01

    The stochastic versions of the logistic and extended logistic growth models are applied successfully to explain many real-life population dynamics and share a central body of literature in stochastic modeling of ecological systems. To understand the randomness in the population dynamics of the underlying processes completely, it is important to have a clear idea about the quasi-equilibrium distribution and its moments. Bartlett et al. (1960) took a pioneering attempt for estimating the moments of the quasi-equilibrium distribution of the stochastic logistic model. Matis and Kiffe (1996) obtain a set of more accurate and elegant approximations for the mean, variance and skewness of the quasi-equilibrium distribution of the same model using cumulant truncation method. The method is extended for stochastic power law logistic family by the same and several other authors (Nasell, 2003; Singh and Hespanha, 2007). Cumulant truncation and some alternative methods e.g. saddle point approximation, derivative matching approach can be applied if the powers involved in the extended logistic set up are integers, although plenty of evidence is available for non-integer powers in many practical situations (Sibly et al., 2005). In this paper, we develop a set of new approximations for mean, variance and skewness of the quasi-equilibrium distribution under more general family of growth curves, which is applicable for both integer and non-integer powers. The deterministic counterpart of this family of models captures both monotonic and non-monotonic behavior of the per capita growth rate, of which theta-logistic is a special case. The approximations accurately estimate the first three order moments of the quasi-equilibrium distribution. The proposed method is illustrated with simulated data and real data from global population dynamics database. Copyright © 2015 Elsevier Inc. All rights reserved.

  16. Weak Hamiltonian finite element method for optimal control problems

    NASA Technical Reports Server (NTRS)

    Hodges, Dewey H.; Bless, Robert R.

    1991-01-01

    A temporal finite element method based on a mixed form of the Hamiltonian weak principle is developed for dynamics and optimal control problems. The mixed form of Hamilton's weak principle contains both displacements and momenta as primary variables that are expanded in terms of nodal values and simple polynomial shape functions. Unlike other forms of Hamilton's principle, however, time derivatives of the momenta and displacements do not appear therein; instead, only the virtual momenta and virtual displacements are differentiated with respect to time. Based on the duality that is observed to exist between the mixed form of Hamilton's weak principle and variational principles governing classical optimal control problems, a temporal finite element formulation of the latter can be developed in a rather straightforward manner. Several well-known problems in dynamics and optimal control are illustrated. The example dynamics problem involves a time-marching problem. As optimal control examples, elementary trajectory optimization problems are treated.

  17. Optimal design of zero-water discharge rinsing systems.

    PubMed

    Thöming, Jorg

    2002-03-01

    This paper is about zero liquid discharge in processes that use water for rinsing. Emphasis was given to those systems that contaminate process water with valuable process liquor and compounds. The approach involved the synthesis of optimal rinsing and recycling networks (RRN) that had a priori excluded water discharge. The total annualized costs of the RRN were minimized by the use of a mixed-integer nonlinear program (MINLP). This MINLP was based on a hyperstructure of the RRN and contained eight counterflow rinsing stages and three regenerator units: electrodialysis, reverse osmosis, and ion exchange columns. A "large-scale nickel plating process" case study showed that by means of zero-water discharge and optimized rinsing the total waste could be reduced by 90.4% at a revenue of $448,000/yr. Furthermore, with the optimized RRN, the rinsing performance can be improved significantly at a low-cost increase. In all the cases, the amount of valuable compounds reclaimed was above 99%.

  18. Non-integer expansion embedding techniques for reversible image watermarking

    NASA Astrophysics Data System (ADS)

    Xiang, Shijun; Wang, Yi

    2015-12-01

    This work aims at reducing the embedding distortion of prediction-error expansion (PE)-based reversible watermarking. In the classical PE embedding method proposed by Thodi and Rodriguez, the predicted value is rounded to integer number for integer prediction-error expansion (IPE) embedding. The rounding operation makes a constraint on a predictor's performance. In this paper, we propose a non-integer PE (NIPE) embedding approach, which can proceed non-integer prediction errors for embedding data into an audio or image file by only expanding integer element of a prediction error while keeping its fractional element unchanged. The advantage of the NIPE embedding technique is that the NIPE technique can really bring a predictor into full play by estimating a sample/pixel in a noncausal way in a single pass since there is no rounding operation. A new noncausal image prediction method to estimate a pixel with four immediate pixels in a single pass is included in the proposed scheme. The proposed noncausal image predictor can provide better performance than Sachnev et al.'s noncausal double-set prediction method (where data prediction in two passes brings a distortion problem due to the fact that half of the pixels were predicted with the watermarked pixels). In comparison with existing several state-of-the-art works, experimental results have shown that the NIPE technique with the new noncausal prediction strategy can reduce the embedding distortion for the same embedding payload.

  19. Prediction of dynamic and mixing characteristics of drop-laden mixing layers using DNS and LES

    NASA Technical Reports Server (NTRS)

    Okong'o, N.; Leboissetier, A.; Bellan, J.

    2004-01-01

    Direct Numerical Simulation (DNS) and Large Eddy Simulation (LES) have been conducted of a temporal mixing layer laden with evaporating drops, in order to assess the ability of LES to reproduce dynamic and mixing aspects of the DNS which affect combustion, independently of combustion models.

  20. Exact calculation of distributions on integers, with application to sequence alignment.

    PubMed

    Newberg, Lee A; Lawrence, Charles E

    2009-01-01

    Computational biology is replete with high-dimensional discrete prediction and inference problems. Dynamic programming recursions can be applied to several of the most important of these, including sequence alignment, RNA secondary-structure prediction, phylogenetic inference, and motif finding. In these problems, attention is frequently focused on some scalar quantity of interest, a score, such as an alignment score or the free energy of an RNA secondary structure. In many cases, score is naturally defined on integers, such as a count of the number of pairing differences between two sequence alignments, or else an integer score has been adopted for computational reasons, such as in the test of significance of motif scores. The probability distribution of the score under an appropriate probabilistic model is of interest, such as in tests of significance of motif scores, or in calculation of Bayesian confidence limits around an alignment. Here we present three algorithms for calculating the exact distribution of a score of this type; then, in the context of pairwise local sequence alignments, we apply the approach so as to find the alignment score distribution and Bayesian confidence limits.

  1. A time series model: First-order integer-valued autoregressive (INAR(1))

    NASA Astrophysics Data System (ADS)

    Simarmata, D. M.; Novkaniza, F.; Widyaningsih, Y.

    2017-07-01

    Nonnegative integer-valued time series arises in many applications. A time series model: first-order Integer-valued AutoRegressive (INAR(1)) is constructed by binomial thinning operator to model nonnegative integer-valued time series. INAR (1) depends on one period from the process before. The parameter of the model can be estimated by Conditional Least Squares (CLS). Specification of INAR(1) is following the specification of (AR(1)). Forecasting in INAR(1) uses median or Bayesian forecasting methodology. Median forecasting methodology obtains integer s, which is cumulative density function (CDF) until s, is more than or equal to 0.5. Bayesian forecasting methodology forecasts h-step-ahead of generating the parameter of the model and parameter of innovation term using Adaptive Rejection Metropolis Sampling within Gibbs sampling (ARMS), then finding the least integer s, where CDF until s is more than or equal to u . u is a value taken from the Uniform(0,1) distribution. INAR(1) is applied on pneumonia case in Penjaringan, Jakarta Utara, January 2008 until April 2016 monthly.

  2. Microsecond Molecular Dynamics Simulations of Lipid Mixing

    PubMed Central

    2015-01-01

    Molecular dynamics (MD) simulations of membranes are often hindered by the slow lateral diffusion of lipids and the limited time scale of MD. In order to study the dynamics of mixing and characterize the lateral distribution of lipids in converged mixtures, we report microsecond-long all-atom MD simulations performed on the special-purpose machine Anton. Two types of mixed bilayers, POPE:POPG (3:1) and POPC:cholesterol (2:1), as well as a pure POPC bilayer, were each simulated for up to 2 μs. These simulations show that POPE:POPG and POPC:cholesterol are each fully miscible at the simulated conditions, with the final states of the mixed bilayers similar to a random mixture. By simulating three POPE:POPG bilayers at different NaCl concentrations (0, 0.15, and 1 M), we also examined the effect of salt concentration on lipid mixing. While an increase in NaCl concentration is shown to affect the area per lipid, tail order, and lipid lateral diffusion, the final states of mixing remain unaltered, which is explained by the largely uniform increase in Na+ ions around POPE and POPG. Direct measurement of water permeation reveals that the POPE:POPG bilayer with 1 M NaCl has reduced water permeability compared with those at zero or low salt concentration. Our calculations provide a benchmark to estimate the convergence time scale of all-atom MD simulations of lipid mixing. Additionally, equilibrated structures of POPE:POPG and POPC:cholesterol, which are frequently used to mimic bacterial and mammalian membranes, respectively, can be used as starting points of simulations involving these membranes. PMID:25237736

  3. Asphalt mix characterization using dynamic modulus and APA testing.

    DOT National Transportation Integrated Search

    2005-11-01

    final report summarizes two research efforts related to asphalt mix characterization: dynamic modulus and Asphalt Pavement Analyzer testing. One phase of the research consisted of a laboratory-based evaluation of dynamic modulus of Oregon dense-grade...

  4. A laboratory investigation of mixing dynamics between biofuels and surface waters

    NASA Astrophysics Data System (ADS)

    Wang, Xiaoxiang; Cotel, Aline

    2017-11-01

    Recently, production and usage of ethanol-blend fuels or biofuels have increased dramatically along with increasing risk of spilling into surface waters. Lack of understanding of the environmental impacts and absence of standard clean-up procedures make it crucial to study the mixing behavior between biofuels and water. Biofuels are represented by a solution of ethanol and glycol. A Plexiglas tank in conjunction with a wave generator is used to simulate the mixing of surface waters and biofuels under different natural conditions. In our previous experiments, two distinct mixing regimes were observed. One regime was driven by turbulence and the other by interfacial instabilities. However, under more realistic situations, without wind driven waves, only the first mixing regime was found. After one minute of rapid turbulent mixing, biofuels and water were fully mixed and no interface was formed. During the mixing process, chemical reactions happened simultaneously and influenced mixing dynamics. Current experiments are investigating the effect of waves on the mixing dynamics. Support from NSF CBET 1335878.

  5. Distributing Earthquakes Among California's Faults: A Binary Integer Programming Approach

    NASA Astrophysics Data System (ADS)

    Geist, E. L.; Parsons, T.

    2016-12-01

    Statement of the problem is simple: given regional seismicity specified by a Gutenber-Richter (G-R) relation, how are earthquakes distributed to match observed fault-slip rates? The objective is to determine the magnitude-frequency relation on individual faults. The California statewide G-R b-value and a-value are estimated from historical seismicity, with the a-value accounting for off-fault seismicity. UCERF3 consensus slip rates are used, based on geologic and geodetic data and include estimates of coupling coefficients. The binary integer programming (BIP) problem is set up such that each earthquake from a synthetic catalog spanning millennia can occur at any location along any fault. The decision vector, therefore, consists of binary variables, with values equal to one indicating the location of each earthquake that results in an optimal match of slip rates, in an L1-norm sense. Rupture area and slip associated with each earthquake are determined from a magnitude-area scaling relation. Uncertainty bounds on the UCERF3 slip rates provide explicit minimum and maximum constraints to the BIP model, with the former more important to feasibility of the problem. There is a maximum magnitude limit associated with each fault, based on fault length, providing an implicit constraint. Solution of integer programming problems with a large number of variables (>105 in this study) has been possible only since the late 1990s. In addition to the classic branch-and-bound technique used for these problems, several other algorithms have been recently developed, including pre-solving, sifting, cutting planes, heuristics, and parallelization. An optimal solution is obtained using a state-of-the-art BIP solver for M≥6 earthquakes and California's faults with slip-rates > 1 mm/yr. Preliminary results indicate a surprising diversity of on-fault magnitude-frequency relations throughout the state.

  6. Fuel Injector Design Optimization for an Annular Scramjet Geometry

    NASA Technical Reports Server (NTRS)

    Steffen, Christopher J., Jr.

    2003-01-01

    A four-parameter, three-level, central composite experiment design has been used to optimize the configuration of an annular scramjet injector geometry using computational fluid dynamics. The computational fluid dynamic solutions played the role of computer experiments, and response surface methodology was used to capture the simulation results for mixing efficiency and total pressure recovery within the scramjet flowpath. An optimization procedure, based upon the response surface results of mixing efficiency, was used to compare the optimal design configuration against the target efficiency value of 92.5%. The results of three different optimization procedures are presented and all point to the need to look outside the current design space for different injector geometries that can meet or exceed the stated mixing efficiency target.

  7. A weak Hamiltonian finite element method for optimal control problems

    NASA Technical Reports Server (NTRS)

    Hodges, Dewey H.; Bless, Robert R.

    1989-01-01

    A temporal finite element method based on a mixed form of the Hamiltonian weak principle is developed for dynamics and optimal control problems. The mixed form of Hamilton's weak principle contains both displacements and momenta as primary variables that are expanded in terms of nodal values and simple polynomial shape functions. Unlike other forms of Hamilton's principle, however, time derivatives of the momenta and displacements do not appear therein; instead, only the virtual momenta and virtual displacements are differentiated with respect to time. Based on the duality that is observed to exist between the mixed form of Hamilton's weak principle and variational principles governing classical optimal control problems, a temporal finite element formulation of the latter can be developed in a rather straightforward manner. Several well-known problems in dynamics and optimal control are illustrated. The example dynamics problem involves a time-marching problem. As optimal control examples, elementary trajectory optimization problems are treated.

  8. A weak Hamiltonian finite element method for optimal control problems

    NASA Technical Reports Server (NTRS)

    Hodges, Dewey H.; Bless, Robert R.

    1990-01-01

    A temporal finite element method based on a mixed form of the Hamiltonian weak principle is developed for dynamics and optimal control problems. The mixed form of Hamilton's weak principle contains both displacements and momenta as primary variables that are expanded in terms of nodal values and simple polynomial shape functions. Unlike other forms of Hamilton's principle, however, time derivatives of the momenta and displacements do not appear therein; instead, only the virtual momenta and virtual displacements are differentiated with respect to time. Based on the duality that is observed to exist between the mixed form of Hamilton's weak principle and variational principles governing classical optimal control problems, a temporal finite element formulation of the latter can be developed in a rather straightforward manner. Several well-known problems in dynamics and optimal control are illustrated. The example dynamics problem involves a time-marching problem. As optimal control examples, elementary trajectory optimization problems are treated.

  9. Integrated strategic and tactical biomass-biofuel supply chain optimization.

    PubMed

    Lin, Tao; Rodríguez, Luis F; Shastri, Yogendra N; Hansen, Alan C; Ting, K C

    2014-03-01

    To ensure effective biomass feedstock provision for large-scale biofuel production, an integrated biomass supply chain optimization model was developed to minimize annual biomass-ethanol production costs by optimizing both strategic and tactical planning decisions simultaneously. The mixed integer linear programming model optimizes the activities range from biomass harvesting, packing, in-field transportation, stacking, transportation, preprocessing, and storage, to ethanol production and distribution. The numbers, locations, and capacities of facilities as well as biomass and ethanol distribution patterns are key strategic decisions; while biomass production, delivery, and operating schedules and inventory monitoring are key tactical decisions. The model was implemented to study Miscanthus-ethanol supply chain in Illinois. The base case results showed unit Miscanthus-ethanol production costs were $0.72L(-1) of ethanol. Biorefinery related costs accounts for 62% of the total costs, followed by biomass procurement costs. Sensitivity analysis showed that a 50% reduction in biomass yield would increase unit production costs by 11%. Copyright © 2014 Elsevier Ltd. All rights reserved.

  10. Optimal mixing rate in linear solvent strength gradient liquid chromatography. Balanced mixing program.

    PubMed

    Blumberg, Leonid M; Desmet, Gert

    2016-12-09

    The mixing rate (R ϕ ) is the temporal rate of increase in the solvent strength in gradient LC. The optimal R ϕ (R ϕ ,Opt ) is the one at which a required peak capacity of gradient LC analysis is obtained in the shortest time. The balanced mixing program is a one where, for better separation of early eluting solutes, the mixing ramp is preceded by a balanced isocratic hold of the duration depending on R ϕ . The improvement in the separation of the earlier eluites due to the balanced programming has been evaluated. The value of R ϕ ,Opt depends on the solvent composition range covered by the mixing ramp and on the column pressure conditions. The R ϕ ,Opt for a column operating at maximum instrumental pressure is different from R ϕ ,Opt for a column operating below the instrumental pressure limit. On the other hand, it has been shown that the difference in the R ϕ ,Opt values under different conditions is not very large so that a single default R ϕ previously recommended for gradient analyses without the isocratic hold also yields a good approximation to the shortest analysis time for all conditions in the balanced analyses. With or without the initial balance isocratic hold, the recommended default R ϕ is about 5%/t 0 (5% increase in the solvent strength per each t 0 -long increment in time) for small-molecule samples, and about an order of magnitude slower (0.5%/t 0 ) for protein samples. A discussion illustrating the use of the optimization criteria employed here for the techniques other than LSS gradient LC is included. Copyright © 2016 Elsevier B.V. All rights reserved.

  11. Evolutionary Dynamic Multiobjective Optimization Via Kalman Filter Prediction.

    PubMed

    Muruganantham, Arrchana; Tan, Kay Chen; Vadakkepat, Prahlad

    2016-12-01

    Evolutionary algorithms are effective in solving static multiobjective optimization problems resulting in the emergence of a number of state-of-the-art multiobjective evolutionary algorithms (MOEAs). Nevertheless, the interest in applying them to solve dynamic multiobjective optimization problems has only been tepid. Benchmark problems, appropriate performance metrics, as well as efficient algorithms are required to further the research in this field. One or more objectives may change with time in dynamic optimization problems. The optimization algorithm must be able to track the moving optima efficiently. A prediction model can learn the patterns from past experience and predict future changes. In this paper, a new dynamic MOEA using Kalman filter (KF) predictions in decision space is proposed to solve the aforementioned problems. The predictions help to guide the search toward the changed optima, thereby accelerating convergence. A scoring scheme is devised to hybridize the KF prediction with a random reinitialization method. Experimental results and performance comparisons with other state-of-the-art algorithms demonstrate that the proposed algorithm is capable of significantly improving the dynamic optimization performance.

  12. 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.

  13. Integer Partitions and Convexity

    NASA Astrophysics Data System (ADS)

    Bouroubi, Sadek

    2007-06-01

    Let n be an integer >=1, and let p(n,k) and P(n,k) count the number of partitions of n into k parts, and the number of partitions of n into parts less than or equal to k, respectively. In this paper, we show that these functions are convex. The result includes the actual value of the constant of Bateman and Erdos.

  14. Classification of drug molecules considering their IC50 values using mixed-integer linear programming based hyper-boxes method.

    PubMed

    Armutlu, Pelin; Ozdemir, Muhittin E; Uney-Yuksektepe, Fadime; Kavakli, I Halil; Turkay, Metin

    2008-10-03

    A priori analysis of the activity of drugs on the target protein by computational approaches can be useful in narrowing down drug candidates for further experimental tests. Currently, there are a large number of computational methods that predict the activity of drugs on proteins. In this study, we approach the activity prediction problem as a classification problem and, we aim to improve the classification accuracy by introducing an algorithm that combines partial least squares regression with mixed-integer programming based hyper-boxes classification method, where drug molecules are classified as low active or high active regarding their binding activity (IC50 values) on target proteins. We also aim to determine the most significant molecular descriptors for the drug molecules. We first apply our approach by analyzing the activities of widely known inhibitor datasets including Acetylcholinesterase (ACHE), Benzodiazepine Receptor (BZR), Dihydrofolate Reductase (DHFR), Cyclooxygenase-2 (COX-2) with known IC50 values. The results at this stage proved that our approach consistently gives better classification accuracies compared to 63 other reported classification methods such as SVM, Naïve Bayes, where we were able to predict the experimentally determined IC50 values with a worst case accuracy of 96%. To further test applicability of this approach we first created dataset for Cytochrome P450 C17 inhibitors and then predicted their activities with 100% accuracy. Our results indicate that this approach can be utilized to predict the inhibitory effects of inhibitors based on their molecular descriptors. This approach will not only enhance drug discovery process, but also save time and resources committed.

  15. Using Integer Clocks to Verify the Timing-Sync Sensor Network Protocol

    NASA Technical Reports Server (NTRS)

    Huang, Xiaowan; Singh, Anu; Smolka, Scott A.

    2010-01-01

    We use the UPPAAL model checker for Timed Automata to verify the Timing-Sync time-synchronization protocol for sensor networks (TPSN). The TPSN protocol seeks to provide network-wide synchronization of the distributed clocks in a sensor network. Clock-synchronization algorithms for sensor networks such as TPSN must be able to perform arithmetic on clock values to calculate clock drift and network propagation delays. They must be able to read the value of a local clock and assign it to another local clock. Such operations are not directly supported by the theory of Timed Automata. To overcome this formal-modeling obstacle, we augment the UPPAAL specification language with the integer clock derived type. Integer clocks, which are essentially integer variables that are periodically incremented by a global pulse generator, greatly facilitate the encoding of the operations required to synchronize clocks as in the TPSN protocol. With this integer-clock-based model of TPSN in hand, we use UPPAAL to verify that the protocol achieves network-wide time synchronization and is devoid of deadlock. We also use the UPPAAL Tracer tool to illustrate how integer clocks can be used to capture clock drift and resynchronization during protocol execution

  16. Optimization of edge state velocity in the integer quantum Hall regime

    NASA Astrophysics Data System (ADS)

    Sahasrabudhe, H.; Novakovic, B.; Nakamura, J.; Fallahi, S.; Povolotskyi, M.; Klimeck, G.; Rahman, R.; Manfra, M. J.

    2018-02-01

    Observation of interference in the quantum Hall regime may be hampered by a small edge state velocity due to finite phase coherence time. Therefore designing two quantum point contact (QPCs) interferometers having a high edge state velocity is desirable. Here we present a new simulation method for designing heterostructures with high edge state velocity by realistically modeling edge states near QPCs in the integer quantum Hall effect (IQHE) regime. Using this simulation method, we also predict the filling factor at the center of QPCs and their conductance at different gate voltages. The 3D Schrödinger equation is split into 1D and 2D parts. Quasi-1D Schrödinger and Poisson equations are solved self-consistently in the IQHE regime to obtain the potential profile, and quantum transport is used to solve for the edge state wave functions. The velocity of edge states is found to be /B , where is the expectation value of the electric field for the edge state. Anisotropically etched trench gated heterostructures with double-sided delta doping have the highest edge state velocity among the structures considered.

  17. Integer Operations Using a Whiteboard

    ERIC Educational Resources Information Center

    Andrews, Delise R.

    2011-01-01

    Interactive whiteboards are somewhat unimpressive at first and look like the whiteboards that already hang on the walls of many classrooms. However, integrating interactive whiteboard technology in a unit on adding and subtracting integers enhances student engagement and understanding. In this article, the author describes how she used an…

  18. Reconstructing the competitive dynamics of mixed-oak neighborhoods

    Treesearch

    Eric K. Zenner; Daniel J. Heggenstaller; Patrick H. Brose; JeriLynn E. Peck; Kim C. Steiner

    2012-01-01

    The disparity between the potential for latent oak dominance within a stand and their region-wide decline in importance value raises questions about the competitiveness of oaks in early stand dynamics. We reconstructed tree height growth dynamics in mixed-species neighborhoods to determine if currently dominant oaks were ever shorter than their competitors and at what...

  19. Energy-optimal path planning by stochastic dynamically orthogonal level-set optimization

    NASA Astrophysics Data System (ADS)

    Subramani, Deepak N.; Lermusiaux, Pierre F. J.

    2016-04-01

    A stochastic optimization methodology is formulated for computing energy-optimal paths from among time-optimal paths of autonomous vehicles navigating in a dynamic flow field. Based on partial differential equations, the methodology rigorously leverages the level-set equation that governs time-optimal reachability fronts for a given relative vehicle-speed function. To set up the energy optimization, the relative vehicle-speed and headings are considered to be stochastic and new stochastic Dynamically Orthogonal (DO) level-set equations are derived. Their solution provides the distribution of time-optimal reachability fronts and corresponding distribution of time-optimal paths. An optimization is then performed on the vehicle's energy-time joint distribution to select the energy-optimal paths for each arrival time, among all stochastic time-optimal paths for that arrival time. Numerical schemes to solve the reduced stochastic DO level-set equations are obtained, and accuracy and efficiency considerations are discussed. These reduced equations are first shown to be efficient at solving the governing stochastic level-sets, in part by comparisons with direct Monte Carlo simulations. To validate the methodology and illustrate its accuracy, comparisons with semi-analytical energy-optimal path solutions are then completed. In particular, we consider the energy-optimal crossing of a canonical steady front and set up its semi-analytical solution using a energy-time nested nonlinear double-optimization scheme. We then showcase the inner workings and nuances of the energy-optimal path planning, considering different mission scenarios. Finally, we study and discuss results of energy-optimal missions in a wind-driven barotropic quasi-geostrophic double-gyre ocean circulation.

  20. Mixing, segregation, and flow of granular materials

    NASA Astrophysics Data System (ADS)

    McCarthy, Joseph J.

    1998-11-01

    This dissertation addresses mixing, segregation, and flow of granular materials with the ultimate goal of providing fundamental understanding and tools for the rational design and optimization of mixing devices. In particular, the paradigm cases of a slowly rotated tumbler mixer and flow down an inclined plane are examined. Computational work, as well as supporting experiments, are used to probe both two and three dimensional systems. In the avalanching regime, the mixing and flow can be viewed either on a global-scale or a local-scale. On the global-scale, material is transported via avalanches whose gross motion can be well described by geometrical considerations. On the local-scale, the dynamics of the particle motion becomes important; particles follow complicated trajectories that are highly sensitive to differences in size/density/morphology. By decomposing the problem in this way, it is possible to study the implications of the geometry and dynamics separately and to add complexities in a controlled fashion. This methodology allows even seemingly difficult problems (i.e., mixing in non-convex geometries, and mixing of dissimilar particles) to be probed in a simple yet methodical way. In addition this technique provides predictions of optimal mixing conditions in an avalanching tumbler, a criterion for evaluating the effect of mixer shape, and mixing enhancement strategies for both two and three dimensional mixers. In the continuous regime, the flow can be divided into two regions: a rapid flow region of the cascading layer at the free surface, and a fixed bed region undergoing solid body rotation. A continuum-based description, in which averages are taken across the layer, generates quantitative predictions about the flow in the cascading layer and agrees well with experiment. Incorporating mixing through a diffusive flux (as well as constitutive expression for segregation) within the cascading layer allows for the determination of optimal mixing conditions

  1. Enhanced ant colony optimization for inventory routing problem

    NASA Astrophysics Data System (ADS)

    Wong, Lily; Moin, Noor Hasnah

    2015-10-01

    The inventory routing problem (IRP) integrates and coordinates two important components of supply chain management which are transportation and inventory management. We consider a one-to-many IRP network for a finite planning horizon. The demand for each product is deterministic and time varying as well as a fleet of capacitated homogeneous vehicles, housed at a depot/warehouse, delivers the products from the warehouse to meet the demand specified by the customers in each period. The inventory holding cost is product specific and is incurred at the customer sites. The objective is to determine the amount of inventory and to construct a delivery routing that minimizes both the total transportation and inventory holding cost while ensuring each customer's demand is met over the planning horizon. The problem is formulated as a mixed integer programming problem and is solved using CPLEX 12.4 to get the lower and upper bound (best integer) for each instance considered. We propose an enhanced ant colony optimization (ACO) to solve the problem and the built route is improved by using local search. The computational experiments demonstrating the effectiveness of our approach is presented.

  2. Dynamic UNITY

    DTIC Science & Technology

    2002-01-01

    UNITY program that implements exactly the same algorithm as Specification 1.1. The correctness of this program is proven in amanner sim- 4 program...chapter, we introduce the Dynamic UNITY formalism, which allows us to reason about algorithms and protocols in which the sets of participating processes...implements Euclid’s algorithm for calculating the greatest common divisor (GCD) of two integers; it repeat- edly reads an integer message from each of its

  3. Footstep Planning on Uneven Terrain with Mixed-Integer Convex Optimization

    DTIC Science & Technology

    2014-08-01

    ORGANIZATION NAME(S) AND ADDRESS(ES) Massachusetts Institute of Technology,Computer Science and Artificial Intellegence Laboratory,Cambridge,MA,02139...the MIT Energy Initiative, MIT CSAIL, and the DARPA Robotics Challenge. 1Robin Deits is with the Computer Science and Artificial Intelligence Laboratory

  4. Center for Parallel Optimization

    DTIC Science & Technology

    1993-09-30

    BOLLING AFB DC 20332-0001 _ii _ 11. SUPPLEMENTARY NOTES 12a. DISTRIBUTION/ AVAILABILITY STATEMENT 12b. DISTRIBUTION CODE APPROVED FOR PUBLIC RELEASE...Machines Corporation, March 16-19, 1993 , A Branch- and-Bound Method for Mixed Integer Programming on the CM-.5 "* Dr. Roberto Musmanno, University of

  5. DAKOTA Design Analysis Kit for Optimization and Terascale

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

    Adams, Brian M.; Dalbey, Keith R.; Eldred, Michael S.

    2010-02-24

    The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes (computational models) and iterative analysis methods. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and analysis of computational models on high performance computers.A user provides a set of DAKOTA commands in an input file and launches DAKOTA. DAKOTA invokes instances of the computational models, collects their results, and performs systems analyses. DAKOTA contains algorithms for optimization with gradient and nongradient-basedmore » methods; uncertainty quantification with sampling, reliability, polynomial chaos, stochastic collocation, and epistemic methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as hybrid optimization, surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. Services for parallel computing, simulation interfacing, approximation modeling, fault tolerance, restart, and graphics are also included.« less

  6. Optimization of Airport Surface Traffic: A Case-Study of Incheon International Airport

    NASA Technical Reports Server (NTRS)

    Eun, Yeonju; Jeon, Daekeun; Lee, Hanbong; Jung, Yoon C.; Zhu, Zhifan; Jeong, Myeongsook; Kim, Hyounkong; Oh, Eunmi; Hong, Sungkwon

    2017-01-01

    This study aims to develop a controllers decision support tool for departure and surface management of ICN. Airport surface traffic optimization for Incheon International Airport (ICN) in South Korea was studied based on the operational characteristics of ICN and airspace of Korea. For surface traffic optimization, a multiple runway scheduling problem and a taxi scheduling problem were formulated into two Mixed Integer Linear Programming (MILP) optimization models. The Miles-In-Trail (MIT) separation constraint at the departure fix shared by the departure flights from multiple runways and the runway crossing constraints due to the taxi route configuration specific to ICN were incorporated into the runway scheduling and taxiway scheduling problems, respectively. Since the MILP-based optimization model for the multiple runway scheduling problem may be computationally intensive, computation times and delay costs of different solving methods were compared for a practical implementation. This research was a collaboration between Korea Aerospace Research Institute (KARI) and National Aeronautics and Space Administration (NASA).

  7. Optimization of Airport Surface Traffic: A Case-Study of Incheon International Airport

    NASA Technical Reports Server (NTRS)

    Eun, Yeonju; Jeon, Daekeun; Lee, Hanbong; Jung, Yoon Chul; Zhu, Zhifan; Jeong, Myeong-Sook; Kim, Hyoun Kyoung; Oh, Eunmi; Hong, Sungkwon

    2017-01-01

    This study aims to develop a controllers' decision support tool for departure and surface management of ICN. Airport surface traffic optimization for Incheon International Airport (ICN) in South Korea was studied based on the operational characteristics of ICN and airspace of Korea. For surface traffic optimization, a multiple runway scheduling problem and a taxi scheduling problem were formulated into two Mixed Integer Linear Programming (MILP) optimization models. The Miles-In-Trail (MIT) separation constraint at the departure fix shared by the departure flights from multiple runways and the runway crossing constraints due to the taxi route configuration specific to ICN were incorporated into the runway scheduling and taxiway scheduling problems, respectively. Since the MILP-based optimization model for the multiple runway scheduling problem may be computationally intensive, computation times and delay costs of different solving methods were compared for a practical implementation. This research was a collaboration between Korea Aerospace Research Institute (KARI) and National Aeronautics and Space Administration (NASA).

  8. Microgrid to enable optimal distributed energy retail and end-user demand response

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

    Jin, Ming; Feng, Wei; Marnay, Chris

    In the face of unprecedented challenges in environmental sustainability and grid resilience, there is an increasingly held consensus regarding the adoption of distributed and renewable energy resources such as microgrids (MGs), and the utilization of flexible electric loads by demand response (DR) to potentially drive a necessary paradigm shift in energy production and consumption patterns. However, the potential value of distributed generation and demand flexibility has not yet been fully realized in the operation of MGs. This study investigates the pricing and operation strategy with DR for a MG retailer in an integrated energy system (IES). Based on co-optimizing retailmore » rates and MG dispatch formulated as a mixed integer quadratic programming (MIQP) problem, our model devises a dynamic pricing scheme that reflects the cost of generation and promotes DR, in tandem with an optimal dispatch plan that exploits spark spread and facilitates the integration of renewables, resulting in improved retailer profits and system stability. Main issues like integrated energy coupling and customer bill reduction are addressed during pricing to ensure rates competitiveness and customer protection. By evaluating on real datasets, the system is demonstrated to optimally coordinate storage, renewables, and combined heat and power (CHP), reduce carbon dioxide emission while maintaining profits, and effectively alleviate the PV curtailment problem. Finally, the model can be used by retailers and MG operators to optimize their operations, as well as regulators to design new utility rates in support of the ongoing transformation of energy systems.« less

  9. Microgrid to enable optimal distributed energy retail and end-user demand response

    DOE PAGES

    Jin, Ming; Feng, Wei; Marnay, Chris; ...

    2018-06-07

    In the face of unprecedented challenges in environmental sustainability and grid resilience, there is an increasingly held consensus regarding the adoption of distributed and renewable energy resources such as microgrids (MGs), and the utilization of flexible electric loads by demand response (DR) to potentially drive a necessary paradigm shift in energy production and consumption patterns. However, the potential value of distributed generation and demand flexibility has not yet been fully realized in the operation of MGs. This study investigates the pricing and operation strategy with DR for a MG retailer in an integrated energy system (IES). Based on co-optimizing retailmore » rates and MG dispatch formulated as a mixed integer quadratic programming (MIQP) problem, our model devises a dynamic pricing scheme that reflects the cost of generation and promotes DR, in tandem with an optimal dispatch plan that exploits spark spread and facilitates the integration of renewables, resulting in improved retailer profits and system stability. Main issues like integrated energy coupling and customer bill reduction are addressed during pricing to ensure rates competitiveness and customer protection. By evaluating on real datasets, the system is demonstrated to optimally coordinate storage, renewables, and combined heat and power (CHP), reduce carbon dioxide emission while maintaining profits, and effectively alleviate the PV curtailment problem. Finally, the model can be used by retailers and MG operators to optimize their operations, as well as regulators to design new utility rates in support of the ongoing transformation of energy systems.« less

  10. Numerical Analysis of Micromixers for Optimization of Mixing Action

    NASA Astrophysics Data System (ADS)

    Panta, Yogendra; Adhikari, Param

    2011-03-01

    Micro-bio/chemical applications often require rapid and uniform mixing of a number of fluid streams that carries bio/chemical species in the solution. At microscale, fluid flow is highly laminar with low Reynolds number, fluids mixing mechanism is primarily by diffusion and free from any turbulence. Demand for highly efficient micromixers for microfluidic networks is due to slower mixing process for larger bio-molecules such as peptides, proteins, and nucleic acids compared to micro-scale molecules. Passive and active mixers are two basic mixers that are currently in use for these applications. Passive mixers often require very long mixing channels where are most active mixers require bulky moving parts to stir the fluids. In this study, electroosmotic effects orthogonally aligned with the fluid flowstream are utilized for optimum mixing effect in various micromixers. Cross-dependencies among several geometrical, electrical, and fluid parameters and their significance are studied in order to achieve an optimum mixing effect. It has been planned to optimize the mixer by non-moving stirring actions provided by an external magnetic field. Acknowledgements to School of Graduate Studies and Research at YSU for URC Grant and RP Award 2009-2010.

  11. Design optimization of single mixed refrigerant LNG process using a hybrid modified coordinate descent algorithm

    NASA Astrophysics Data System (ADS)

    Qyyum, Muhammad Abdul; Long, Nguyen Van Duc; Minh, Le Quang; Lee, Moonyong

    2018-01-01

    Design optimization of the single mixed refrigerant (SMR) natural gas liquefaction (LNG) process involves highly non-linear interactions between decision variables, constraints, and the objective function. These non-linear interactions lead to an irreversibility, which deteriorates the energy efficiency of the LNG process. In this study, a simple and highly efficient hybrid modified coordinate descent (HMCD) algorithm was proposed to cope with the optimization of the natural gas liquefaction process. The single mixed refrigerant process was modeled in Aspen Hysys® and then connected to a Microsoft Visual Studio environment. The proposed optimization algorithm provided an improved result compared to the other existing methodologies to find the optimal condition of the complex mixed refrigerant natural gas liquefaction process. By applying the proposed optimization algorithm, the SMR process can be designed with the 0.2555 kW specific compression power which is equivalent to 44.3% energy saving as compared to the base case. Furthermore, in terms of coefficient of performance (COP), it can be enhanced up to 34.7% as compared to the base case. The proposed optimization algorithm provides a deep understanding of the optimization of the liquefaction process in both technical and numerical perspectives. In addition, the HMCD algorithm can be employed to any mixed refrigerant based liquefaction process in the natural gas industry.

  12. Integrated aerodynamic/dynamic optimization of helicopter rotor blades

    NASA Technical Reports Server (NTRS)

    Chattopadhyay, Aditi; Walsh, Joanne L.; Riley, Michael F.

    1989-01-01

    An integrated aerodynamic/dynamic optimization procedure is used to minimize blade weight and 4 per rev vertical hub shear for a rotor blade in forward flight. The coupling of aerodynamics and dynamics is accomplished through the inclusion of airloads which vary with the design variables during the optimization process. Both single and multiple objective functions are used in the optimization formulation. The Global Criteria Approach is used to formulate the multiple objective optimization and results are compared with those obtained by using single objective function formulations. Constraints are imposed on natural frequencies, autorotational inertia, and centrifugal stress. The program CAMRAD is used for the blade aerodynamic and dynamic analyses, and the program CONMIN is used for the optimization. Since the spanwise and the azimuthal variations of loading are responsible for most rotor vibration and noise, the vertical airload distributions on the blade, before and after optimization, are compared. The total power required by the rotor to produce the same amount of thrust for a given area is also calculated before and after optimization. Results indicate that integrated optimization can significantly reduce the blade weight, the hub shear and the amplitude of the vertical airload distributions on the blade and the total power required by the rotor.

  13. Fully integrated aerodynamic/dynamic optimization of helicopter rotor blades

    NASA Technical Reports Server (NTRS)

    Walsh, Joanne L.; Lamarsh, William J., II; Adelman, Howard M.

    1992-01-01

    This paper describes a fully integrated aerodynamic/dynamic optimization procedure for helicopter rotor blades. The procedure combines performance and dynamics analyses with a general purpose optimizer. The procedure minimizes a linear combination of power required (in hover, forward flight, and maneuver) and vibratory hub shear. The design variables include pretwist, taper initiation, taper ratio, root chord, blade stiffnesses, tuning masses, and tuning mass locations. Aerodynamic constraints consist of limits on power required in hover, forward flight and maneuver; airfoil section stall; drag divergence Mach number; minimum tip chord; and trim. Dynamic constraints are on frequencies, minimum autorotational inertia, and maximum blade weight. The procedure is demonstrated for two cases. In the first case the objective function involves power required (in hover, forward flight, and maneuver) and dynamics. The second case involves only hover power and dynamics. The designs from the integrated procedure are compared with designs from a sequential optimization approach in which the blade is first optimized for performance and then for dynamics. In both cases, the integrated approach is superior.

  14. Fully integrated aerodynamic/dynamic optimization of helicopter rotor blades

    NASA Technical Reports Server (NTRS)

    Walsh, Joanne L.; Lamarsh, William J., II; Adelman, Howard M.

    1992-01-01

    A fully integrated aerodynamic/dynamic optimization procedure is described for helicopter rotor blades. The procedure combines performance and dynamic analyses with a general purpose optimizer. The procedure minimizes a linear combination of power required (in hover, forward flight, and maneuver) and vibratory hub shear. The design variables include pretwist, taper initiation, taper ratio, root chord, blade stiffnesses, tuning masses, and tuning mass locations. Aerodynamic constraints consist of limits on power required in hover, forward flight and maneuvers; airfoil section stall; drag divergence Mach number; minimum tip chord; and trim. Dynamic constraints are on frequencies, minimum autorotational inertia, and maximum blade weight. The procedure is demonstrated for two cases. In the first case, the objective function involves power required (in hover, forward flight and maneuver) and dynamics. The second case involves only hover power and dynamics. The designs from the integrated procedure are compared with designs from a sequential optimization approach in which the blade is first optimized for performance and then for dynamics. In both cases, the integrated approach is superior.

  15. Integer Optimization Model for a Logistic System based on Location-Routing Considering Distance and Chosen Route

    NASA Astrophysics Data System (ADS)

    Mulyasari, Joni; Mawengkang, Herman; Efendi, Syahril

    2018-02-01

    In a distribution network it is important to decide the locations of facilities that impacts not only the profitability of an organization but the ability to serve customers.Generally the location-routing problem is to minimize the overall cost by simultaneously selecting a subset of candidate facilities and constructing a set of delivery routes that satisfy some restrictions. In this paper we impose restriction on the route that should be passed for delivery. We use integer programming model to describe the problem. A feasible neighbourhood search is proposed to solve the result model.

  16. Design and optimization of mixed flow pump impeller blades by varying semi-cone angle

    NASA Astrophysics Data System (ADS)

    Dash, Nehal; Roy, Apurba Kumar; Kumar, Kaushik

    2018-03-01

    The mixed flow pump is a cross between the axial and radial flow pump. These pumps are used in a large number of applications in modern fields. For the designing of these mixed flow pump impeller blades, a lot number of design parameters are needed to be considered which makes this a tedious task for which fundamentals of turbo-machinery and fluid mechanics are always prerequisites. The semi-cone angle of mixed flow pump impeller blade has a specified range of variations generally between 45o to 60o. From the literature review done related to this topic researchers have considered only a particular semi-cone angle and all the calculations are based on this very same semi-cone angle. By varying this semi-cone angle in the specified range, it can be verified if that affects the designing of the impeller blades for a mixed flow pump. Although a lot of methods are available for designing of mixed flow pump impeller blades like inverse time marching method, the pseudo-stream function method, Fourier expansion singularity method, free vortex method, mean stream line theory method etc. still the optimized design of the mixed flow pump impeller blade has been a cumbersome work. As stated above since all the available research works suggest or propose the blade designs with constant semi-cone angle, here the authors have designed the impeller blades by varying the semi-cone angle in a particular range with regular intervals for a Mixed-Flow pump. Henceforth several relevant impeller blade designs are obtained and optimization is carried out to obtain the optimized design (blade with optimal geometry) of impeller blade.

  17. Metamodeling and the Critic-based approach to multi-level optimization.

    PubMed

    Werbos, Ludmilla; Kozma, Robert; Silva-Lugo, Rodrigo; Pazienza, Giovanni E; Werbos, Paul J

    2012-08-01

    Large-scale networks with hundreds of thousands of variables and constraints are becoming more and more common in logistics, communications, and distribution domains. Traditionally, the utility functions defined on such networks are optimized using some variation of Linear Programming, such as Mixed Integer Programming (MIP). Despite enormous progress both in hardware (multiprocessor systems and specialized processors) and software (Gurobi) we are reaching the limits of what these tools can handle in real time. Modern logistic problems, for example, call for expanding the problem both vertically (from one day up to several days) and horizontally (combining separate solution stages into an integrated model). The complexity of such integrated models calls for alternative methods of solution, such as Approximate Dynamic Programming (ADP), which provide a further increase in the performance necessary for the daily operation. In this paper, we present the theoretical basis and related experiments for solving the multistage decision problems based on the results obtained for shorter periods, as building blocks for the models and the solution, via Critic-Model-Action cycles, where various types of neural networks are combined with traditional MIP models in a unified optimization system. In this system architecture, fast and simple feed-forward networks are trained to reasonably initialize more complicated recurrent networks, which serve as approximators of the value function (Critic). The combination of interrelated neural networks and optimization modules allows for multiple queries for the same system, providing flexibility and optimizing performance for large-scale real-life problems. A MATLAB implementation of our solution procedure for a realistic set of data and constraints shows promising results, compared to the iterative MIP approach. Copyright © 2012 Elsevier Ltd. All rights reserved.

  18. Dynamic optimization of metabolic networks coupled with gene expression.

    PubMed

    Waldherr, Steffen; Oyarzún, Diego A; Bockmayr, Alexander

    2015-01-21

    The regulation of metabolic activity by tuning enzyme expression levels is crucial to sustain cellular growth in changing environments. Metabolic networks are often studied at steady state using constraint-based models and optimization techniques. However, metabolic adaptations driven by changes in gene expression cannot be analyzed by steady state models, as these do not account for temporal changes in biomass composition. Here we present a dynamic optimization framework that integrates the metabolic network with the dynamics of biomass production and composition. An approximation by a timescale separation leads to a coupled model of quasi-steady state constraints on the metabolic reactions, and differential equations for the substrate concentrations and biomass composition. We propose a dynamic optimization approach to determine reaction fluxes for this model, explicitly taking into account enzyme production costs and enzymatic capacity. In contrast to the established dynamic flux balance analysis, our approach allows predicting dynamic changes in both the metabolic fluxes and the biomass composition during metabolic adaptations. Discretization of the optimization problems leads to a linear program that can be efficiently solved. We applied our algorithm in two case studies: a minimal nutrient uptake network, and an abstraction of core metabolic processes in bacteria. In the minimal model, we show that the optimized uptake rates reproduce the empirical Monod growth for bacterial cultures. For the network of core metabolic processes, the dynamic optimization algorithm predicted commonly observed metabolic adaptations, such as a diauxic switch with a preference ranking for different nutrients, re-utilization of waste products after depletion of the original substrate, and metabolic adaptation to an impending nutrient depletion. These examples illustrate how dynamic adaptations of enzyme expression can be predicted solely from an optimization principle. Copyright

  19. Mixture optimization for mixed gas Joule-Thomson cycle

    NASA Astrophysics Data System (ADS)

    Detlor, J.; Pfotenhauer, J.; Nellis, G.

    2017-12-01

    An appropriate gas mixture can provide lower temperatures and higher cooling power when used in a Joule-Thomson (JT) cycle than is possible with a pure fluid. However, selecting gas mixtures to meet specific cooling loads and cycle parameters is a challenging design problem. This study focuses on the development of a computational tool to optimize gas mixture compositions for specific operating parameters. This study expands on prior research by exploring higher heat rejection temperatures and lower pressure ratios. A mixture optimization model has been developed which determines an optimal three-component mixture based on the analysis of the maximum value of the minimum value of isothermal enthalpy change, ΔhT , that occurs over the temperature range. This allows optimal mixture compositions to be determined for a mixed gas JT system with load temperatures down to 110 K and supply temperatures above room temperature for pressure ratios as small as 3:1. The mixture optimization model has been paired with a separate evaluation of the percent of the heat exchanger that exists in a two-phase range in order to begin the process of selecting a mixture for experimental investigation.

  20. Efficient dynamic optimization of logic programs

    NASA Technical Reports Server (NTRS)

    Laird, Phil

    1992-01-01

    A summary is given of the dynamic optimization approach to speed up learning for logic programs. The problem is to restructure a recursive program into an equivalent program whose expected performance is optimal for an unknown but fixed population of problem instances. We define the term 'optimal' relative to the source of input instances and sketch an algorithm that can come within a logarithmic factor of optimal with high probability. Finally, we show that finding high-utility unfolding operations (such as EBG) can be reduced to clause reordering.

  1. Mathematical model and metaheuristics for simultaneous balancing and sequencing of a robotic mixed-model assembly line

    NASA Astrophysics Data System (ADS)

    Li, Zixiang; Janardhanan, Mukund Nilakantan; Tang, Qiuhua; Nielsen, Peter

    2018-05-01

    This article presents the first method to simultaneously balance and sequence robotic mixed-model assembly lines (RMALB/S), which involves three sub-problems: task assignment, model sequencing and robot allocation. A new mixed-integer programming model is developed to minimize makespan and, using CPLEX solver, small-size problems are solved for optimality. Two metaheuristics, the restarted simulated annealing algorithm and co-evolutionary algorithm, are developed and improved to address this NP-hard problem. The restarted simulated annealing method replaces the current temperature with a new temperature to restart the search process. The co-evolutionary method uses a restart mechanism to generate a new population by modifying several vectors simultaneously. The proposed algorithms are tested on a set of benchmark problems and compared with five other high-performing metaheuristics. The proposed algorithms outperform their original editions and the benchmarked methods. The proposed algorithms are able to solve the balancing and sequencing problem of a robotic mixed-model assembly line effectively and efficiently.

  2. Two-dimensional convolute integers for analytical instrumentation

    NASA Technical Reports Server (NTRS)

    Edwards, T. R.

    1982-01-01

    As new analytical instruments and techniques emerge with increased dimensionality, a corresponding need is seen for data processing logic which can appropriately address the data. Two-dimensional measurements reveal enhanced unknown mixture analysis capability as a result of the greater spectral information content over two one-dimensional methods taken separately. It is noted that two-dimensional convolute integers are merely an extension of the work by Savitzky and Golay (1964). It is shown that these low-pass, high-pass and band-pass digital filters are truly two-dimensional and that they can be applied in a manner identical with their one-dimensional counterpart, that is, a weighted nearest-neighbor, moving average with zero phase shifting, convoluted integer (universal number) weighting coefficients.

  3. Optimal traffic resource allocation and management.

    DOT National Transportation Integrated Search

    2010-05-01

    "In this paper, we address the problem of determining the patrol routes of state troopers for maximum coverage of : highway spots with high frequencies of crashes (hot spots). We develop a mixed integer linear programming model : for this problem und...

  4. VMAT optimization with dynamic collimator rotation.

    PubMed

    Lyu, Qihui; O'Connor, Daniel; Ruan, Dan; Yu, Victoria; Nguyen, Dan; Sheng, Ke

    2018-04-16

    Although collimator rotation is an optimization variable that can be exploited for dosimetric advantages, existing Volumetric Modulated Arc Therapy (VMAT) optimization uses a fixed collimator angle in each arc and only rotates the collimator between arcs. In this study, we develop a novel integrated optimization method for VMAT, accounting for dynamic collimator angles during the arc motion. Direct Aperture Optimization (DAO) for Dynamic Collimator in VMAT (DC-VMAT) was achieved by adding to the existing dose fidelity objective an anisotropic total variation term for regulating the fluence smoothness, a binary variable for forming simple apertures, and a group sparsity term for controlling collimator rotation. The optimal collimator angle for each beam angle was selected using the Dijkstra's algorithm, where the node costs depend on the estimated fluence map at the current iteration and the edge costs account for the mechanical constraints of multi-leaf collimator (MLC). An alternating optimization strategy was implemented to solve the DAO and collimator angle selection (CAS). Feasibility of DC-VMAT using one full-arc with dynamic collimator rotation was tested on a phantom with two small spherical targets, a brain, a lung and a prostate cancer patient. The plan was compared against a static collimator VMAT (SC-VMAT) plan using three full arcs with 60 degrees of collimator angle separation in patient studies. With the same target coverage, DC-VMAT achieved 20.3% reduction of R50 in the phantom study, and reduced the average max and mean OAR dose by 4.49% and 2.53% of the prescription dose in patient studies, as compared with SC-VMAT. The collimator rotation co-ordinated with the gantry rotation in DC-VMAT plans for deliverability. There were 13 beam angles in the single-arc DC-VMAT plan in patient studies that requires slower gantry rotation to accommodate multiple collimator angles. The novel DC-VMAT approach utilizes the dynamic collimator rotation during arc

  5. Determining the optimal number of Kanban in multi-products supply chain system

    NASA Astrophysics Data System (ADS)

    Widyadana, G. A.; Wee, H. M.; Chang, Jer-Yuan

    2010-02-01

    Kanban, a key element of just-in-time system, is a re-order card or signboard giving instruction or triggering the pull system to manufacture or supply a component based on actual usage of material. There are two types of Kanban: production Kanban and withdrawal Kanban. This study uses optimal and meta-heuristic methods to determine the Kanban quantity and withdrawal lot sizes in a supply chain system. Although the mix integer programming method gives an optimal solution, it is not time efficient. For this reason, the meta-heuristic methods are suggested. In this study, a genetic algorithm (GA) and a hybrid of genetic algorithm and simulated annealing (GASA) are used. The study compares the performance of GA and GASA with that of the optimal method using MIP. The given problems show that both GA and GASA result in a near optimal solution, and they outdo the optimal method in term of run time. In addition, the GASA heuristic method gives a better performance than the GA heuristic method.

  6. Automated Static Culture System Cell Module Mixing Protocol and Computational Fluid Dynamics Analysis

    NASA Technical Reports Server (NTRS)

    Kleis, Stanley J.; Truong, Tuan; Goodwin, Thomas J,

    2004-01-01

    This report is a documentation of a fluid dynamic analysis of the proposed Automated Static Culture System (ASCS) cell module mixing protocol. The report consists of a review of some basic fluid dynamics principles appropriate for the mixing of a patch of high oxygen content media into the surrounding media which is initially depleted of oxygen, followed by a computational fluid dynamics (CFD) study of this process for the proposed protocol over a range of the governing parameters. The time histories of oxygen concentration distributions and mechanical shear levels generated are used to characterize the mixing process for different parameter values.

  7. Dynamic positioning configuration and its first-order optimization

    NASA Astrophysics Data System (ADS)

    Xue, Shuqiang; Yang, Yuanxi; Dang, Yamin; Chen, Wu

    2014-02-01

    Traditional geodetic network optimization deals with static and discrete control points. The modern space geodetic network is, on the other hand, composed of moving control points in space (satellites) and on the Earth (ground stations). The network configuration composed of these facilities is essentially dynamic and continuous. Moreover, besides the position parameter which needs to be estimated, other geophysical information or signals can also be extracted from the continuous observations. The dynamic (continuous) configuration of the space network determines whether a particular frequency of signals can be identified by this system. In this paper, we employ the functional analysis and graph theory to study the dynamic configuration of space geodetic networks, and mainly focus on the optimal estimation of the position and clock-offset parameters. The principle of the D-optimization is introduced in the Hilbert space after the concept of the traditional discrete configuration is generalized from the finite space to the infinite space. It shows that the D-optimization developed in the discrete optimization is still valid in the dynamic configuration optimization, and this is attributed to the natural generalization of least squares from the Euclidean space to the Hilbert space. Then, we introduce the principle of D-optimality invariance under the combination operation and rotation operation, and propose some D-optimal simplex dynamic configurations: (1) (Semi) circular configuration in 2-dimensional space; (2) the D-optimal cone configuration and D-optimal helical configuration which is close to the GPS constellation in 3-dimensional space. The initial design of GPS constellation can be approximately treated as a combination of 24 D-optimal helixes by properly adjusting the ascending node of different satellites to realize a so-called Walker constellation. In the case of estimating the receiver clock-offset parameter, we show that the circular configuration, the

  8. Combined optimization model for sustainable energization strategy

    NASA Astrophysics Data System (ADS)

    Abtew, Mohammed Seid

    Access to energy is a foundation to establish a positive impact on multiple aspects of human development. Both developed and developing countries have a common concern of achieving a sustainable energy supply to fuel economic growth and improve the quality of life with minimal environmental impacts. The Least Developing Countries (LDCs), however, have different economic, social, and energy systems. Prevalence of power outage, lack of access to electricity, structural dissimilarity between rural and urban regions, and traditional fuel dominance for cooking and the resultant health and environmental hazards are some of the distinguishing characteristics of these nations. Most energy planning models have been designed for developed countries' socio-economic demographics and have missed the opportunity to address special features of the poor countries. An improved mixed-integer programming energy-source optimization model is developed to address limitations associated with using current energy optimization models for LDCs, tackle development of the sustainable energization strategies, and ensure diversification and risk management provisions in the selected energy mix. The Model predicted a shift from traditional fuels reliant and weather vulnerable energy source mix to a least cost and reliable modern clean energy sources portfolio, a climb on the energy ladder, and scored multifaceted economic, social, and environmental benefits. At the same time, it represented a transition strategy that evolves to increasingly cleaner energy technologies with growth as opposed to an expensive solution that leapfrogs immediately to the cleanest possible, overreaching technologies.

  9. Making Sense of Integer Arithmetic: The Effect of Using Virtual Manipulatives on Students' Representational Fluency

    ERIC Educational Resources Information Center

    Bolyard, Johnna; Moyer-Packenham, Patricia

    2012-01-01

    This study investigated how the use of virtual manipulatives in integer instruction impacts student achievement for integer addition and subtraction. Of particular interest was the influence of using virtual manipulatives on students' ability to create and translate among representations for integer computation. The research employed a…

  10. Mixing effects on the kinetics and the dynamics of two-phase enzymatic hydrolysis of hemicellulose for biofuel production.

    PubMed

    Dutta, Sajal Kanti; Chakraborty, Saikat

    2018-07-01

    This work uses a coupled experimental and modeling approach to explore the effects of macro- and micro-mixing on the kinetics and the dynamics of two-phase enzymatic hydrolysis of hemicellulose. Reactor mixing does not alter the non-competitive nature of product inhibition in hemicellulose hydrolysis by endoxylanase, but produces stronger inhibition that reduces the soluble sugar yield by 8-14.5%, as the mixing speed increases from 0 to 200 rpm. The kinetic constants (K m , V max , K x ) assume mass-transfer disguised values at 0-200 rpm. An optimal mixing strategy, comprising of 55-70 min of initial rapid convective macromixing followed by diffusive micromixing (without any macromixing) for the rest of the hydrolysis, increases xylose and reducing sugar yields by 6.3-8% and 13-20%, respectively, over continuous mixing at 200 rpm, for 1-5 mg/ml substrate loading at an optimum enzyme to substrate ratio of 1:20, with an energy saving of 94-96% over 24 h. Copyright © 2018 Elsevier Ltd. All rights reserved.

  11. Combinatorial optimization games

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

    Deng, X.; Ibaraki, Toshihide; Nagamochi, Hiroshi

    1997-06-01

    We introduce a general integer programming formulation for a class of combinatorial optimization games, which immediately allows us to improve the algorithmic result for finding amputations in the core (an important solution concept in cooperative game theory) of the network flow game on simple networks by Kalai and Zemel. An interesting result is a general theorem that the core for this class of games is nonempty if and only if a related linear program has an integer optimal solution. We study the properties for this mathematical condition to hold for several interesting problems, and apply them to resolve algorithmic andmore » complexity issues for their cores along the line as put forward in: decide whether the core is empty; if the core is empty, find an imputation in the core; given an imputation x, test whether x is in the core. We also explore the properties of totally balanced games in this succinct formulation of cooperative games.« less

  12. On the phenomenon of mixed dynamics in Pikovsky-Topaj system of coupled rotators

    NASA Astrophysics Data System (ADS)

    Gonchenko, A. S.; Gonchenko, S. V.; Kazakov, A. O.; Turaev, D. V.

    2017-07-01

    A one-parameter family of time-reversible systems on three-dimensional torus is considered. It is shown that the dynamics is not conservative, namely the attractor and repeller intersect but not coincide. We explain this as the manifestation of the so-called mixed dynamics phenomenon which corresponds to a persistent intersection of the closure of the stable periodic orbits and the closure of the completely unstable periodic orbits. We search for the stable and unstable periodic orbits indirectly, by finding non-conservative saddle periodic orbits and heteroclinic connections between them. In this way, we are able to claim the existence of mixed dynamics for a large range of parameter values. We investigate local and global bifurcations that can be used for the detection of mixed dynamics.

  13. Capacity planning of link restorable optical networks under dynamic change of traffic

    NASA Astrophysics Data System (ADS)

    Ho, Kwok Shing; Cheung, Kwok Wai

    2005-11-01

    Future backbone networks shall require full-survivability and support dynamic changes of traffic demands. The Generalized Survivable Networks (GSN) was proposed to meet these challenges. GSN is fully-survivable under dynamic traffic demand changes, so it offers a practical and guaranteed characterization framework for ASTN / ASON survivable network planning and bandwidth-on-demand resource allocation 4. The basic idea of GSN is to incorporate the non-blocking network concept into the survivable network models. In GSN, each network node must specify its I/O capacity bound which is taken as constraints for any allowable traffic demand matrix. In this paper, we consider the following generic GSN network design problem: Given the I/O bounds of each network node, find a routing scheme (and the corresponding rerouting scheme under failure) and the link capacity assignment (both working and spare) which minimize the cost, such that any traffic matrix consistent with the given I/O bounds can be feasibly routed and it is single-fault tolerant under the link restoration scheme. We first show how the initial, infeasible formal mixed integer programming formulation can be transformed into a more feasible problem using the duality transformation of the linear program. Then we show how the problem can be simplified using the Lagrangian Relaxation approach. Previous work has outlined a two-phase approach for solving this problem where the first phase optimizes the working capacity assignment and the second phase optimizes the spare capacity assignment. In this paper, we present a jointly optimized framework for dimensioning the survivable optical network with the GSN model. Experiment results show that the jointly optimized GSN can bring about on average of 3.8% cost savings when compared with the separate, two-phase approach. Finally, we perform a cost comparison and show that GSN can be deployed with a reasonable cost.

  14. An Optimization Framework for Dynamic Hybrid Energy Systems

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

    Wenbo Du; Humberto E Garcia; Christiaan J.J. Paredis

    A computational framework for the efficient analysis and optimization of dynamic hybrid energy systems (HES) is developed. A microgrid system with multiple inputs and multiple outputs (MIMO) is modeled using the Modelica language in the Dymola environment. The optimization loop is implemented in MATLAB, with the FMI Toolbox serving as the interface between the computational platforms. Two characteristic optimization problems are selected to demonstrate the methodology and gain insight into the system performance. The first is an unconstrained optimization problem that optimizes the dynamic properties of the battery, reactor and generator to minimize variability in the HES. The second problemmore » takes operating and capital costs into consideration by imposing linear and nonlinear constraints on the design variables. The preliminary optimization results obtained in this study provide an essential step towards the development of a comprehensive framework for designing HES.« less

  15. Campaign-level dynamic network modelling for spaceflight logistics for the flexible path concept

    NASA Astrophysics Data System (ADS)

    Ho, Koki; de Weck, Olivier L.; Hoffman, Jeffrey A.; Shishko, Robert

    2016-06-01

    This paper develops a network optimization formulation for dynamic campaign-level space mission planning. Although many past space missions have been designed mainly from a mission-level perspective, a campaign-level perspective will be important for future space exploration. In order to find the optimal campaign-level space transportation architecture, a mixed-integer linear programming (MILP) formulation with a generalized multi-commodity flow and a time-expanded network is developed. Particularly, a new heuristics-based method, a partially static time-expanded network, is developed to provide a solution quickly. The developed method is applied to a case study containing human exploration of a near-Earth object (NEO) and Mars, related to the concept of the Flexible Path. The numerical results show that using the specific combinations of propulsion technologies, in-situ resource utilization (ISRU), and other space infrastructure elements can reduce the initial mass in low-Earth orbit (IMLEO) significantly. In addition, the case study results also show that we can achieve large IMLEO reduction by designing NEO and Mars missions together as a campaign compared with designing them separately owing to their common space infrastructure pre-deployment. This research will be an important step toward efficient and flexible campaign-level space mission planning.

  16. Leveraging human decision making through the optimal management of centralized resources

    NASA Astrophysics Data System (ADS)

    Hyden, Paul; McGrath, Richard G.

    2016-05-01

    Combining results from mixed integer optimization, stochastic modeling and queuing theory, we will advance the interdisciplinary problem of efficiently and effectively allocating centrally managed resources. Academia currently fails to address this, as the esoteric demands of each of these large research areas limits work across traditional boundaries. The commercial space does not currently address these challenges due to the absence of a profit metric. By constructing algorithms that explicitly use inputs across boundaries, we are able to incorporate the advantages of using human decision makers. Key improvements in the underlying algorithms are made possible by aligning decision maker goals with the feedback loops introduced between the core optimization step and the modeling of the overall stochastic process of supply and demand. A key observation is that human decision-makers must be explicitly included in the analysis for these approaches to be ultimately successful. Transformative access gives warfighters and mission owners greater understanding of global needs and allows for relationships to guide optimal resource allocation decisions. Mastery of demand processes and optimization bottlenecks reveals long term maximum marginal utility gaps in capabilities.

  17. Can Structural Optimization Explain Slow Dynamics of Rocks?

    NASA Astrophysics Data System (ADS)

    Kim, H.; Vistisen, O.; Tencate, J. A.

    2009-12-01

    Slow dynamics is a recovery process that describes the return to an equilibrium state after some external energy input is applied and then removed. Experimental studies on many rocks have shown that a modest acoustic energy input results in slow dynamics. The recovery process of the stiffness has consistently been found to be linear to log(time) for a wide range of geomaterials and the time constants appear to be unique to the material [TenCate JA, Shankland TJ (1996), Geophys Res Lett 23, 3019-3022]. Measurements of this nonequilibrium effect in rocks (e.g. sandstones and limestones) have been linked directly to the cement holding the individual grains together [Darling TW, TenCate JA, Brown DW, Clausen B, Vogel SC (2004), Geophys Res Lett 31, L16604], also suggesting a potential link to porosity and permeability. Noting that slow dynamics consistently returns the overall stiffness of rocks to its maximum (original) state, it is hypothesized that the original state represents the global minimum strain energy state. Consequently the slow dynamics process represents the global minimization or optimization process. Structural optimization, which has been developed for engineering design, minimises the total strain energy by rearranging the material distribution [Kim H, Querin OM, Steven GP, Xie YM (2002), Struct Multidiscip Optim 24, 441-448]. The optimization process effectively rearranges the way the material is cemented. One of the established global optimization methods is simulated annealing (SA). Derived from cooling of metal to a thermal equilibrium, SA finds an optimum solution by iteratively moving the system towards the minimum energy state with a probability of 'uphill' moves. It has been established that the global optimum can be guaranteed by applying a log(time) linear cooling schedule [Hajek B (1988, Math Ops Res, 15, 311-329]. This work presents the original study of applying SA to the maximum stiffness optimization problem. Preliminary results

  18. Improving the mixing performance of side channel type micromixers using an optimal voltage control model.

    PubMed

    Wu, Chien-Hsien; Yang, Ruey-Jen

    2006-06-01

    Electroosmotic flow in microchannels is restricted to low Reynolds number regimes. Since the inertia forces are extremely weak in such regimes, turbulent conditions do not readily develop, and hence species mixing occurs primarily as a result of diffusion. Consequently, achieving a thorough species mixing generally relies upon the use of extended mixing channels. This paper aims to improve the mixing performance of conventional side channel type micromixers by specifying the optimal driving voltages to be applied to each channel. In the proposed approach, the driving voltages are identified by constructing a simple theoretical scheme based on a 'flow-rate-ratio' model and Kirchhoff's law. The numerical and experimental results confirm that the optimal voltage control approach provides a better mixing performance than the use of a single driving voltage gradient.

  19. Breast Radiotherapy with Mixed Energy Photons; a Model for Optimal Beam Weighting.

    PubMed

    Birgani, Mohammadjavad Tahmasebi; Fatahiasl, Jafar; Hosseini, Seyed Mohammad; Bagheri, Ali; Behrooz, Mohammad Ali; Zabiehzadeh, Mansour; Meskani, Reza; Gomari, Maryam Talaei

    2015-01-01

    Utilization of high energy photons (>10 MV) with an optimal weight using a mixed energy technique is a practical way to generate a homogenous dose distribution while maintaining adequate target coverage in intact breast radiotherapy. This study represents a model for estimation of this optimal weight for day to day clinical usage. For this purpose, treatment planning computed tomography scans of thirty-three consecutive early stage breast cancer patients following breast conservation surgery were analyzed. After delineation of the breast clinical target volume (CTV) and placing opposed wedge paired isocenteric tangential portals, dosimeteric calculations were conducted and dose volume histograms (DVHs) were generated, first with pure 6 MV photons and then these calculations were repeated ten times with incorporating 18 MV photons (ten percent increase in weight per step) in each individual patient. For each calculation two indexes including maximum dose in the breast CTV (Dmax) and the volume of CTV which covered with 95% Isodose line (VCTV, 95%IDL) were measured according to the DVH data and then normalized values were plotted in a graph. The optimal weight of 18 MV photons was defined as the intersection point of Dmax and VCTV, 95%IDL graphs. For creating a model to predict this optimal weight multiple linear regression analysis was used based on some of the breast and tangential field parameters. The best fitting model for prediction of 18 MV photons optimal weight in breast radiotherapy using mixed energy technique, incorporated chest wall separation plus central lung distance (Adjusted R2=0.776). In conclusion, this study represents a model for the estimation of optimal beam weighting in breast radiotherapy using mixed photon energy technique for routine day to day clinical usage.

  20. Behavioral heterogeneity affects individual performances in experimental and computational lowest unique integer games

    NASA Astrophysics Data System (ADS)

    Yamada, Takashi

    2017-12-01

    This study computationally examines (1) how the behaviors of subjects are represented, (2) whether the classification of subjects is related to the scale of the game, and (3) what kind of behavioral models are successful in small-sized lowest unique integer games (LUIGs). In a LUIG, N (>= 3) players submit a positive integer up to M(> 1) and the player choosing the smallest number not chosen by anyone else wins. For this purpose, the author considers four LUIGs with N = {3, 4} and M = {3, 4} and uses the behavioral data obtained in the laboratory experiment by Yamada and Hanaki (Physica A 463, pp. 88–102, 2016). For computational experiments, the author calibrates the parameters of typical learning models for each subject and then pursues round robin competitions. The main findings are in the following: First, the subjects who played not differently from the mixed-strategy Nash equilibrium (MSE) prediction tended to made use of not only their choices but also the game outcomes. Meanwhile those who deviated from the MSE prediction took care of only their choices as the complexity of the game increased. Second, the heterogeneity of player strategies depends on both the number of players (N) and the upper limit (M). Third, when groups consist of different agents like in the earlier laboratory experiment, sticking behavior is quite effective to win.

  1. Topology optimization of reduced rare-earth permanent magnet arrays with finite coercivity

    NASA Astrophysics Data System (ADS)

    Teyber, R.; Trevizoli, P. V.; Christiaanse, T. V.; Govindappa, P.; Rowe, A.

    2018-05-01

    The supply chain risk of rare-earth permanent magnets has yielded research efforts to improve both materials and magnetic circuits. While a number of magnet optimization techniques exist, literature has not incorporated the permanent magnet failure process stemming from finite coercivity. To address this, a mixed-integer topology optimization is formulated to maximize the flux density of a segmented Halbach cylinder while avoiding permanent demagnetization. The numerical framework is used to assess the efficacy of low-cost (rare-earth-free ferrite C9), medium-cost (rare-earth-free MnBi), and higher-cost (Dy-free NdFeB) permanent magnet materials. Novel magnet designs are generated that produce flux densities 70% greater than the segmented Halbach array, albeit with increased magnet mass. Three optimization formulations are then explored using ferrite C9 that demonstrates the trade-off between manufacturability and design sophistication, generating flux densities in the range of 0.366-0.483 T.

  2. System design optimization for stand-alone photovoltaic systems sizing by using superstructure model

    NASA Astrophysics Data System (ADS)

    Azau, M. A. M.; Jaafar, S.; Samsudin, K.

    2013-06-01

    Although the photovoltaic (PV) systems have been increasingly installed as an alternative and renewable green power generation, the initial set up cost, maintenance cost and equipment mismatch are some of the key issues that slows down the installation in small household. This paper presents the design optimization of stand-alone photovoltaic systems using superstructure model where all possible types of technology of the equipment are captured and life cycle cost analysis is formulated as a mixed integer programming (MIP). A model for investment planning of power generation and long-term decision model are developed in order to help the system engineer to build a cost effective system.

  3. Optimal clinical trial design based on a dichotomous Markov-chain mixed-effect sleep model.

    PubMed

    Steven Ernest, C; Nyberg, Joakim; Karlsson, Mats O; Hooker, Andrew C

    2014-12-01

    D-optimal designs for discrete-type responses have been derived using generalized linear mixed models, simulation based methods and analytical approximations for computing the fisher information matrix (FIM) of non-linear mixed effect models with homogeneous probabilities over time. In this work, D-optimal designs using an analytical approximation of the FIM for a dichotomous, non-homogeneous, Markov-chain phase advanced sleep non-linear mixed effect model was investigated. The non-linear mixed effect model consisted of transition probabilities of dichotomous sleep data estimated as logistic functions using piecewise linear functions. Theoretical linear and nonlinear dose effects were added to the transition probabilities to modify the probability of being in either sleep stage. D-optimal designs were computed by determining an analytical approximation the FIM for each Markov component (one where the previous state was awake and another where the previous state was asleep). Each Markov component FIM was weighted either equally or by the average probability of response being awake or asleep over the night and summed to derive the total FIM (FIM(total)). The reference designs were placebo, 0.1, 1-, 6-, 10- and 20-mg dosing for a 2- to 6-way crossover study in six dosing groups. Optimized design variables were dose and number of subjects in each dose group. The designs were validated using stochastic simulation/re-estimation (SSE). Contrary to expectations, the predicted parameter uncertainty obtained via FIM(total) was larger than the uncertainty in parameter estimates computed by SSE. Nevertheless, the D-optimal designs decreased the uncertainty of parameter estimates relative to the reference designs. Additionally, the improvement for the D-optimal designs were more pronounced using SSE than predicted via FIM(total). Through the use of an approximate analytic solution and weighting schemes, the FIM(total) for a non-homogeneous, dichotomous Markov-chain phase

  4. Optimization Models for Scheduling of Jobs

    PubMed Central

    Indika, S. H. Sathish; Shier, Douglas R.

    2006-01-01

    This work is motivated by a particular scheduling problem that is faced by logistics centers that perform aircraft maintenance and modification. Here we concentrate on a single facility (hangar) which is equipped with several work stations (bays). Specifically, a number of jobs have already been scheduled for processing at the facility; the starting times, durations, and work station assignments for these jobs are assumed to be known. We are interested in how best to schedule a number of new jobs that the facility will be processing in the near future. We first develop a mixed integer quadratic programming model (MIQP) for this problem. Since the exact solution of this MIQP formulation is time consuming, we develop a heuristic procedure, based on existing bin packing techniques. This heuristic is further enhanced by application of certain local optimality conditions. PMID:27274921

  5. A multi-period optimization model for energy planning with CO(2) emission consideration.

    PubMed

    Mirzaesmaeeli, H; Elkamel, A; Douglas, P L; Croiset, E; Gupta, M

    2010-05-01

    A novel deterministic multi-period mixed-integer linear programming (MILP) model for the power generation planning of electric systems is described and evaluated in this paper. The model is developed with the objective of determining the optimal mix of energy supply sources and pollutant mitigation options that meet a specified electricity demand and CO(2) emission targets at minimum cost. Several time-dependent parameters are included in the model formulation; they include forecasted energy demand, fuel price variability, construction lead time, conservation initiatives, and increase in fixed operational and maintenance costs over time. The developed model is applied to two case studies. The objective of the case studies is to examine the economical, structural, and environmental effects that would result if the electricity sector was required to reduce its CO(2) emissions to a specified limit. Copyright 2009 Elsevier Ltd. All rights reserved.

  6. Particle swarm optimization with recombination and dynamic linkage discovery.

    PubMed

    Chen, Ying-Ping; Peng, Wen-Chih; Jian, Ming-Chung

    2007-12-01

    In this paper, we try to improve the performance of the particle swarm optimizer by incorporating the linkage concept, which is an essential mechanism in genetic algorithms, and design a new linkage identification technique called dynamic linkage discovery to address the linkage problem in real-parameter optimization problems. Dynamic linkage discovery is a costless and effective linkage recognition technique that adapts the linkage configuration by employing only the selection operator without extra judging criteria irrelevant to the objective function. Moreover, a recombination operator that utilizes the discovered linkage configuration to promote the cooperation of particle swarm optimizer and dynamic linkage discovery is accordingly developed. By integrating the particle swarm optimizer, dynamic linkage discovery, and recombination operator, we propose a new hybridization of optimization methodologies called particle swarm optimization with recombination and dynamic linkage discovery (PSO-RDL). In order to study the capability of PSO-RDL, numerical experiments were conducted on a set of benchmark functions as well as on an important real-world application. The benchmark functions used in this paper were proposed in the 2005 Institute of Electrical and Electronics Engineers Congress on Evolutionary Computation. The experimental results on the benchmark functions indicate that PSO-RDL can provide a level of performance comparable to that given by other advanced optimization techniques. In addition to the benchmark, PSO-RDL was also used to solve the economic dispatch (ED) problem for power systems, which is a real-world problem and highly constrained. The results indicate that PSO-RDL can successfully solve the ED problem for the three-unit power system and obtain the currently known best solution for the 40-unit system.

  7. Optimal Operation System of the Integrated District Heating System with Multiple Regional Branches

    NASA Astrophysics Data System (ADS)

    Kim, Ui Sik; Park, Tae Chang; Kim, Lae-Hyun; Yeo, Yeong Koo

    This paper presents an optimal production and distribution management for structural and operational optimization of the integrated district heating system (DHS) with multiple regional branches. A DHS consists of energy suppliers and consumers, district heating pipelines network and heat storage facilities in the covered region. In the optimal management system, production of heat and electric power, regional heat demand, electric power bidding and sales, transport and storage of heat at each regional DHS are taken into account. The optimal management system is formulated as a mixed integer linear programming (MILP) where the objectives is to minimize the overall cost of the integrated DHS while satisfying the operation constraints of heat units and networks as well as fulfilling heating demands from consumers. Piecewise linear formulation of the production cost function and stairwise formulation of the start-up cost function are used to compute nonlinear cost function approximately. Evaluation of the total overall cost is based on weekly operations at each district heat branches. Numerical simulations show the increase of energy efficiency due to the introduction of the present optimal management system.

  8. MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics.

    PubMed

    Egea, Jose A; Henriques, David; Cokelaer, Thomas; Villaverde, Alejandro F; MacNamara, Aidan; Danciu, Diana-Patricia; Banga, Julio R; Saez-Rodriguez, Julio

    2014-05-10

    Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools. We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods. MEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods.

  9. MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics

    PubMed Central

    2014-01-01

    Background Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools. Results We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods. Conclusions MEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods. PMID:24885957

  10. Should We Stop Developing Heuristics and Only Rely on Mixed Integer Programming Solvers in Automated Test Assembly? A Rejoinder to van der Linden and Li (2016).

    PubMed

    Chen, Pei-Hua

    2017-05-01

    This rejoinder responds to the commentary by van der Linden and Li entiled "Comment on Three-Element Item Selection Procedures for Multiple Forms Assembly: An Item Matching Approach" on the article "Three-Element Item Selection Procedures for Multiple Forms Assembly: An Item Matching Approach" by Chen. Van der Linden and Li made a strong statement calling for the cessation of test assembly heuristics development, and instead encouraged embracing mixed integer programming (MIP). This article points out the nondeterministic polynomial (NP)-hard nature of MIP problems and how solutions found using heuristics could be useful in an MIP context. Although van der Linden and Li provided several practical examples of test assembly supporting their view, the examples ignore the cases in which a slight change of constraints or item pool data might mean it would not be possible to obtain solutions as quickly as before. The article illustrates the use of heuristic solutions to improve both the performance of MIP solvers and the quality of solutions. Additional responses to the commentary by van der Linden and Li are included.

  11. Elasticity of fractal materials using the continuum model with non-integer dimensional space

    NASA Astrophysics Data System (ADS)

    Tarasov, Vasily E.

    2015-01-01

    Using a generalization of vector calculus for space with non-integer dimension, we consider elastic properties of fractal materials. Fractal materials are described by continuum models with non-integer dimensional space. A generalization of elasticity equations for non-integer dimensional space, and its solutions for the equilibrium case of fractal materials are suggested. Elasticity problems for fractal hollow ball and cylindrical fractal elastic pipe with inside and outside pressures, for rotating cylindrical fractal pipe, for gradient elasticity and thermoelasticity of fractal materials are solved.

  12. Optimal design and dispatch of a system of diesel generators, photovoltaics and batteries for remote locations

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

    Scioletti, Michael S.; Newman, Alexandra M.; Goodman, Johanna K.

    Renewable energy technologies, specifically, solar photovoltaic cells, combined with battery storage and diesel generators, form a hybrid system capable of independently powering remote locations, i.e., those isolated from larger grids. If sized correctly, hybrid systems reduce fuel consumption compared to diesel generator-only alternatives. We present an optimization model for establishing a hybrid power design and dispatch strategy for remote locations, such as a military forward operating base, that models the acquisition of different power technologies as integer variables and their operation using nonlinear expressions. Our cost-minimizing, nonconvex, mixed-integer, nonlinear program contains a detailed battery model. Due to its complexities, wemore » present linearizations, which include exact and convex under-estimation techniques, and a heuristic, which determines an initial feasible solution to serve as a “warm start” for the solver. We determine, in a few hours at most, solutions within 5% of optimality for a candidate set of technologies; these solutions closely resemble those from the nonlinear model. Lastly, our instances contain real data spanning a yearly horizon at hour fidelity and demonstrate that a hybrid system could reduce fuel consumption by as much as 50% compared to a generator-only solution.« less

  13. Optimal design and dispatch of a system of diesel generators, photovoltaics and batteries for remote locations

    DOE PAGES

    Scioletti, Michael S.; Newman, Alexandra M.; Goodman, Johanna K.; ...

    2017-05-08

    Renewable energy technologies, specifically, solar photovoltaic cells, combined with battery storage and diesel generators, form a hybrid system capable of independently powering remote locations, i.e., those isolated from larger grids. If sized correctly, hybrid systems reduce fuel consumption compared to diesel generator-only alternatives. We present an optimization model for establishing a hybrid power design and dispatch strategy for remote locations, such as a military forward operating base, that models the acquisition of different power technologies as integer variables and their operation using nonlinear expressions. Our cost-minimizing, nonconvex, mixed-integer, nonlinear program contains a detailed battery model. Due to its complexities, wemore » present linearizations, which include exact and convex under-estimation techniques, and a heuristic, which determines an initial feasible solution to serve as a “warm start” for the solver. We determine, in a few hours at most, solutions within 5% of optimality for a candidate set of technologies; these solutions closely resemble those from the nonlinear model. Lastly, our instances contain real data spanning a yearly horizon at hour fidelity and demonstrate that a hybrid system could reduce fuel consumption by as much as 50% compared to a generator-only solution.« less

  14. Population dynamics with a mixed type of sexual and asexual reproduction in a fluctuating environment

    PubMed Central

    2012-01-01

    Background Carassius gibelio, a cyprinid fish from Eurasia, has the ability to reproduce both sexually and asexually. This fish is also known as an invasive species which colonized almost all continental Europe, most likely originating from Asia and Eastern Europe. Populations of both sexually and asexually reproducing individuals exist in sympatry. In this study we try to elucidate the advantages of such a mixed type of reproduction. We investigate the dynamics of two sympatric populations with sexual and asexual reproduction in a periodically fluctuating environment. We define an individual-based computational model in which genotypes are represented by L loci, and the environment is composed of L resources for which the two populations compete. Results Our model demonstrates advantageous population dynamics where the optimal percentage of asexual reproduction depends on selection strength, on the number of selected loci and on the timescale of environmental fluctuations. We show that the sexual reproduction is necessary for "generating" fit genotypes, while the asexual reproduction is suitable for "amplifying" them. The simulations show that the optimal percentage of asexual reproduction increases with the length of the environment stability period and decrease with the strength of the selection and the number of loci. Conclusions In this paper we addressed the advantages of a mixed type of sexual and asexual reproduction in a changing environment and explored the idea that a species that is able to adapt itself to environmental fluctuation can easily colonize a new habitat. Our results could provide a possible explanation for the rapid and efficient invasion of species with a variable ratio of sexual and asexual reproduction such as Carassius gibelio. PMID:22489797

  15. Vector calculus in non-integer dimensional space and its applications to fractal media

    NASA Astrophysics Data System (ADS)

    Tarasov, Vasily E.

    2015-02-01

    We suggest a generalization of vector calculus for the case of non-integer dimensional space. The first and second orders operations such as gradient, divergence, the scalar and vector Laplace operators for non-integer dimensional space are defined. For simplification we consider scalar and vector fields that are independent of angles. We formulate a generalization of vector calculus for rotationally covariant scalar and vector functions. This generalization allows us to describe fractal media and materials in the framework of continuum models with non-integer dimensional space. As examples of application of the suggested calculus, we consider elasticity of fractal materials (fractal hollow ball and fractal cylindrical pipe with pressure inside and outside), steady distribution of heat in fractal media, electric field of fractal charged cylinder. We solve the correspondent equations for non-integer dimensional space models.

  16. Multiobjective Optimization of Low-Energy Trajectories Using Optimal Control on Dynamical Channels

    NASA Technical Reports Server (NTRS)

    Coffee, Thomas M.; Anderson, Rodney L.; Lo, Martin W.

    2011-01-01

    We introduce a computational method to design efficient low-energy trajectories by extracting initial solutions from dynamical channels formed by invariant manifolds, and improving these solutions through variational optimal control. We consider trajectories connecting two unstable periodic orbits in the circular restricted 3-body problem (CR3BP). Our method leverages dynamical channels to generate a range of solutions, and approximates the areto front for impulse and time of flight through a multiobjective optimization of these solutions based on primer vector theory. We demonstrate the application of our method to a libration orbit transfer in the Earth-Moon system.

  17. Dynamic Airspace Configuration

    NASA Technical Reports Server (NTRS)

    Bloem, Michael J.

    2014-01-01

    In air traffic management systems, airspace is partitioned into regions in part to distribute the tasks associated with managing air traffic among different systems and people. These regions, as well as the systems and people allocated to each, are changed dynamically so that air traffic can be safely and efficiently managed. It is expected that new air traffic control systems will enable greater flexibility in how airspace is partitioned and how resources are allocated to airspace regions. In this talk, I will begin by providing an overview of some previous work and open questions in Dynamic Airspace Configuration research, which is concerned with how to partition airspace and assign resources to regions of airspace. For example, I will introduce airspace partitioning algorithms based on clustering, integer programming optimization, and computational geometry. I will conclude by discussing the development of a tablet-based tool that is intended to help air traffic controller supervisors configure airspace and controllers in current operations.

  18. Neural dynamic optimization for control systems. I. Background.

    PubMed

    Seong, C Y; Widrow, B

    2001-01-01

    The paper presents neural dynamic optimization (NDO) as a method of optimal feedback control for nonlinear multi-input-multi-output (MIMO) systems. The main feature of NDO is that it enables neural networks to approximate the optimal feedback solution whose existence dynamic programming (DP) justifies, thereby reducing the complexities of computation and storage problems of the classical methods such as DP. This paper mainly describes the background and motivations for the development of NDO, while the two other subsequent papers of this topic present the theory of NDO and demonstrate the method with several applications including control of autonomous vehicles and of a robot arm, respectively.

  19. Neural dynamic optimization for control systems.III. Applications.

    PubMed

    Seong, C Y; Widrow, B

    2001-01-01

    For pt.II. see ibid., p. 490-501. The paper presents neural dynamic optimization (NDO) as a method of optimal feedback control for nonlinear multi-input-multi-output (MIMO) systems. The main feature of NDO is that it enables neural networks to approximate the optimal feedback solution whose existence dynamic programming (DP) justifies, thereby reducing the complexities of computation and storage problems of the classical methods such as DP. This paper demonstrates NDO with several applications including control of autonomous vehicles and of a robot-arm, while the two other companion papers of this topic describes the background for the development of NDO and present the theory of the method, respectively.

  20. Neural dynamic optimization for control systems.II. Theory.

    PubMed

    Seong, C Y; Widrow, B

    2001-01-01

    The paper presents neural dynamic optimization (NDO) as a method of optimal feedback control for nonlinear multi-input-multi-output (MIMO) systems. The main feature of NDO is that it enables neural networks to approximate the optimal feedback solution whose existence dynamic programming (DP) justifies, thereby reducing the complexities of computation and storage problems of the classical methods such as DP. This paper mainly describes the theory of NDO, while the two other companion papers of this topic explain the background for the development of NDO and demonstrate the method with several applications including control of autonomous vehicles and of a robot arm, respectively.

  1. Discrete Optimization Model for Vehicle Routing Problem with Scheduling Side Cosntraints

    NASA Astrophysics Data System (ADS)

    Juliandri, Dedy; Mawengkang, Herman; Bu'ulolo, F.

    2018-01-01

    Vehicle Routing Problem (VRP) is an important element of many logistic systems which involve routing and scheduling of vehicles from a depot to a set of customers node. This is a hard combinatorial optimization problem with the objective to find an optimal set of routes used by a fleet of vehicles to serve the demands a set of customers It is required that these vehicles return to the depot after serving customers’ demand. The problem incorporates time windows, fleet and driver scheduling, pick-up and delivery in the planning horizon. The goal is to determine the scheduling of fleet and driver and routing policies of the vehicles. The objective is to minimize the overall costs of all routes over the planning horizon. We model the problem as a linear mixed integer program. We develop a combination of heuristics and exact method for solving the model.

  2. Optimizing decentralized production-distribution planning problem in a multi-period supply chain network under uncertainty

    NASA Astrophysics Data System (ADS)

    Nourifar, Raheleh; Mahdavi, Iraj; Mahdavi-Amiri, Nezam; Paydar, Mohammad Mahdi

    2017-09-01

    Decentralized supply chain management is found to be significantly relevant in today's competitive markets. Production and distribution planning is posed as an important optimization problem in supply chain networks. Here, we propose a multi-period decentralized supply chain network model with uncertainty. The imprecision related to uncertain parameters like demand and price of the final product is appropriated with stochastic and fuzzy numbers. We provide mathematical formulation of the problem as a bi-level mixed integer linear programming model. Due to problem's convolution, a structure to solve is developed that incorporates a novel heuristic algorithm based on Kth-best algorithm, fuzzy approach and chance constraint approach. Ultimately, a numerical example is constructed and worked through to demonstrate applicability of the optimization model. A sensitivity analysis is also made.

  3. An optimization model for energy generation and distribution in a dynamic facility

    NASA Technical Reports Server (NTRS)

    Lansing, F. L.

    1981-01-01

    An analytical model is described using linear programming for the optimum generation and distribution of energy demands among competing energy resources and different economic criteria. The model, which will be used as a general engineering tool in the analysis of the Deep Space Network ground facility, considers several essential decisions for better design and operation. The decisions sought for the particular energy application include: the optimum time to build an assembly of elements, inclusion of a storage medium of some type, and the size or capacity of the elements that will minimize the total life-cycle cost over a given number of years. The model, which is structured in multiple time divisions, employ the decomposition principle for large-size matrices, the branch-and-bound method in mixed-integer programming, and the revised simplex technique for efficient and economic computer use.

  4. The effects of mixed layer dynamics on ice growth in the central Arctic

    NASA Astrophysics Data System (ADS)

    Kitchen, Bruce R.

    1992-09-01

    The thermodynamic model of Thorndike (1992) is coupled to a one dimensional, two layer ocean entrainment model to study the effect of mixed layer dynamics on ice growth and the variation in the ocean heat flux into the ice due to mixed layer entrainment. Model simulations show the existence of a negative feedback between the ice growth and the mixed layer entrainment, and that the underlying ocean salinity has a greater effect on the ocean beat flux than does variations in the underlying ocean temperature. Model simulations for a variety of surface forcings and initial conditions demonstrate the need to include mixed layer dynamics for realistic ice prediction in the arctic.

  5. Dollars & Sense: Students' Integer Perspectives

    ERIC Educational Resources Information Center

    Whitacre, Ian; Bishop, Jessica Pierson; Philipp, Randolph A.; Lamb, Lisa L.; Schappelle, Bonnie P.

    2014-01-01

    A story problem about borrowing money, presented in this article, may be represented with positive or negative numbers and thought about in different ways. The authors describe ideas related to integers (both positive and negative) and how students used them in relation to a story problem, and how they related these ideas to equations.

  6. An interactive approach based on a discrete differential evolution algorithm for a class of integer bilevel programming problems

    NASA Astrophysics Data System (ADS)

    Li, Hong; Zhang, Li; Jiao, Yong-Chang

    2016-07-01

    This paper presents an interactive approach based on a discrete differential evolution algorithm to solve a class of integer bilevel programming problems, in which integer decision variables are controlled by an upper-level decision maker and real-value or continuous decision variables are controlled by a lower-level decision maker. Using the Karush--Kuhn-Tucker optimality conditions in the lower-level programming, the original discrete bilevel formulation can be converted into a discrete single-level nonlinear programming problem with the complementarity constraints, and then the smoothing technique is applied to deal with the complementarity constraints. Finally, a discrete single-level nonlinear programming problem is obtained, and solved by an interactive approach. In each iteration, for each given upper-level discrete variable, a system of nonlinear equations including the lower-level variables and Lagrange multipliers is solved first, and then a discrete nonlinear programming problem only with inequality constraints is handled by using a discrete differential evolution algorithm. Simulation results show the effectiveness of the proposed approach.

  7. The effect of dropout on the efficiency of D-optimal designs of linear mixed models.

    PubMed

    Ortega-Azurduy, S A; Tan, F E S; Berger, M P F

    2008-06-30

    Dropout is often encountered in longitudinal data. Optimal designs will usually not remain optimal in the presence of dropout. In this paper, we study D-optimal designs for linear mixed models where dropout is encountered. Moreover, we estimate the efficiency loss in cases where a D-optimal design for complete data is chosen instead of that for data with dropout. Two types of monotonically decreasing response probability functions are investigated to describe dropout. Our results show that the location of D-optimal design points for the dropout case will shift with respect to that for the complete and uncorrelated data case. Owing to this shift, the information collected at the D-optimal design points for the complete data case does not correspond to the smallest variance. We show that the size of the displacement of the time points depends on the linear mixed model and that the efficiency loss is moderate.

  8. Mixed H(2)/H(sub infinity): Control with output feedback compensators using parameter optimization

    NASA Technical Reports Server (NTRS)

    Schoemig, Ewald; Ly, Uy-Loi

    1992-01-01

    Among the many possible norm-based optimization methods, the concept of H-infinity optimal control has gained enormous attention in the past few years. Here the H-infinity framework, based on the Small Gain Theorem and the Youla Parameterization, effectively treats system uncertainties in the control law synthesis. A design approach involving a mixed H(sub 2)/H-infinity norm strives to combine the advantages of both methods. This advantage motivates researchers toward finding solutions to the mixed H(sub 2)/H-infinity control problem. The approach developed in this research is based on a finite time cost functional that depicts an H-infinity bound control problem in a H(sub 2)-optimization setting. The goal is to define a time-domain cost function that optimizes the H(sub 2)-norm of a system with an H-infinity-constraint function.

  9. Mixed H2/H(infinity)-Control with an output-feedback compensator using parameter optimization

    NASA Technical Reports Server (NTRS)

    Schoemig, Ewald; Ly, Uy-Loi

    1992-01-01

    Among the many possible norm-based optimization methods, the concept of H-infinity optimal control has gained enormous attention in the past few years. Here the H-infinity framework, based on the Small Gain Theorem and the Youla Parameterization, effectively treats system uncertainties in the control law synthesis. A design approach involving a mixed H(sub 2)/H-infinity norm strives to combine the advantages of both methods. This advantage motivates researchers toward finding solutions to the mixed H(sub 2)/H-infinity control problem. The approach developed in this research is based on a finite time cost functional that depicts an H-infinity bound control problem in a H(sub 2)-optimization setting. The goal is to define a time-domain cost function that optimizes the H(sub 2)-norm of a system with an H-infinity-constraint function.

  10. Fuzzy multiobjective models for optimal operation of a hydropower system

    NASA Astrophysics Data System (ADS)

    Teegavarapu, Ramesh S. V.; Ferreira, André R.; Simonovic, Slobodan P.

    2013-06-01

    Optimal operation models for a hydropower system using new fuzzy multiobjective mathematical programming models are developed and evaluated in this study. The models use (i) mixed integer nonlinear programming (MINLP) with binary variables and (ii) integrate a new turbine unit commitment formulation along with water quality constraints used for evaluation of reservoir downstream impairment. Reardon method used in solution of genetic algorithm optimization problems forms the basis for development of a new fuzzy multiobjective hydropower system optimization model with creation of Reardon type fuzzy membership functions. The models are applied to a real-life hydropower reservoir system in Brazil. Genetic Algorithms (GAs) are used to (i) solve the optimization formulations to avoid computational intractability and combinatorial problems associated with binary variables in unit commitment, (ii) efficiently address Reardon method formulations, and (iii) deal with local optimal solutions obtained from the use of traditional gradient-based solvers. Decision maker's preferences are incorporated within fuzzy mathematical programming formulations to obtain compromise operating rules for a multiobjective reservoir operation problem dominated by conflicting goals of energy production, water quality and conservation releases. Results provide insight into compromise operation rules obtained using the new Reardon fuzzy multiobjective optimization framework and confirm its applicability to a variety of multiobjective water resources problems.

  11. Dynamics of Mixed- Candida Species Biofilms in Response to Antifungals.

    PubMed

    Vipulanandan, G; Herrera, M; Wiederhold, N P; Li, X; Mintz, J; Wickes, B L; Kadosh, D

    2018-01-01

    Oral infections caused by Candida species, the most commonly isolated human fungal pathogen, are frequently associated with biofilms. Although Candida albicans is the predominant organism found in patients with oral thrush, a biofilm infection, there is an increasing incidence of oral colonization and infections caused by non- albicans Candida species, including C. glabrata, C. dubliniensis, and C. tropicalis, which are frequently more resistant to antifungal treatment. While single-species Candida biofilms have been well studied, considerably less is known about the dynamics of mixed- Candida species biofilms and how these dynamics are altered by antifungal treatment. To address these questions, we developed a quantitative polymerase chain reaction-based approach to determine the precise species composition of mixed- Candida species biofilms formed by clinical isolates and laboratory strains in the presence and absence of clinically relevant concentrations of 3 commonly used antifungals: fluconazole, caspofungin, and amphotericin B. In monospecies biofilms, fluconazole exposure favored growth of C. glabrata and C. tropicalis, while caspofungin generally favored significant growth of all species to a varying degree. Fluconazole was not effective against preformed mixed- Candida species biofilms while amphotericin B was potent. As a general trend, in mixed- Candida species biofilms, C. albicans lost dominance in the presence of antifungals. Interestingly, presence in mixed versus monospecies biofilms reduced susceptibility to amphotericin B for C. tropicalis and C. glabrata. Overall, our data suggest that antifungal treatment favors the growth of specific non- albicans Candida species in mixed- Candida species biofilms.

  12. Optimal control problem for linear fractional-order systems, described by equations with Hadamard-type derivative

    NASA Astrophysics Data System (ADS)

    Postnov, Sergey

    2017-11-01

    Two kinds of optimal control problem are investigated for linear time-invariant fractional-order systems with lumped parameters which dynamics described by equations with Hadamard-type derivative: the problem of control with minimal norm and the problem of control with minimal time at given restriction on control norm. The problem setting with nonlocal initial conditions studied. Admissible controls allowed to be the p-integrable functions (p > 1) at half-interval. The optimal control problem studied by moment method. The correctness and solvability conditions for the corresponding moment problem are derived. For several special cases the optimal control problems stated are solved analytically. Some analogies pointed for results obtained with the results which are known for integer-order systems and fractional-order systems describing by equations with Caputo- and Riemann-Liouville-type derivatives.

  13. A framework for modeling and optimizing dynamic systems under uncertainty

    DOE PAGES

    Nicholson, Bethany; Siirola, John

    2017-11-11

    Algebraic modeling languages (AMLs) have drastically simplified the implementation of algebraic optimization problems. However, there are still many classes of optimization problems that are not easily represented in most AMLs. These classes of problems are typically reformulated before implementation, which requires significant effort and time from the modeler and obscures the original problem structure or context. In this work we demonstrate how the Pyomo AML can be used to represent complex optimization problems using high-level modeling constructs. We focus on the operation of dynamic systems under uncertainty and demonstrate the combination of Pyomo extensions for dynamic optimization and stochastic programming.more » We use a dynamic semibatch reactor model and a large-scale bubbling fluidized bed adsorber model as test cases.« less

  14. A framework for modeling and optimizing dynamic systems under uncertainty

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

    Nicholson, Bethany; Siirola, John

    Algebraic modeling languages (AMLs) have drastically simplified the implementation of algebraic optimization problems. However, there are still many classes of optimization problems that are not easily represented in most AMLs. These classes of problems are typically reformulated before implementation, which requires significant effort and time from the modeler and obscures the original problem structure or context. In this work we demonstrate how the Pyomo AML can be used to represent complex optimization problems using high-level modeling constructs. We focus on the operation of dynamic systems under uncertainty and demonstrate the combination of Pyomo extensions for dynamic optimization and stochastic programming.more » We use a dynamic semibatch reactor model and a large-scale bubbling fluidized bed adsorber model as test cases.« less

  15. The optimal dynamic immunization under a controlled heterogeneous node-based SIRS model

    NASA Astrophysics Data System (ADS)

    Yang, Lu-Xing; Draief, Moez; Yang, Xiaofan

    2016-05-01

    Dynamic immunizations, under which the state of the propagation network of electronic viruses can be changed by adjusting the control measures, are regarded as an alternative to static immunizations. This paper addresses the optimal dynamical immunization under the widely accepted SIRS assumption. First, based on a controlled heterogeneous node-based SIRS model, an optimal control problem capturing the optimal dynamical immunization is formulated. Second, the existence of an optimal dynamical immunization scheme is shown, and the corresponding optimality system is derived. Next, some numerical examples are given to show that an optimal immunization strategy can be worked out by numerically solving the optimality system, from which it is found that the network topology has a complex impact on the optimal immunization strategy. Finally, the difference between a payoff and the minimum payoff is estimated in terms of the deviation of the corresponding immunization strategy from the optimal immunization strategy. The proposed optimal immunization scheme is justified, because it can achieve a low level of infections at a low cost.

  16. Obstacles and Affordances for Integer Reasoning: An Analysis of Children's Thinking and the History of Mathematics

    ERIC Educational Resources Information Center

    Bishop, Jessica Pierson; Lamb, Lisa L.; Philipp, Randolph A.; Whitacre, Ian; Schappelle, Bonnie P.; Lewis, Melinda L.

    2014-01-01

    We identify and document 3 cognitive obstacles, 3 cognitive affordances, and 1 type of integer understanding that can function as either an obstacle or affordance for learners while they extend their numeric domains from whole numbers to include negative integers. In particular, we highlight 2 key subsets of integer reasoning: understanding or…

  17. How to Differentiate an Integer Modulo n

    ERIC Educational Resources Information Center

    Emmons, Caleb; Krebs, Mike; Shaheen, Anthony

    2009-01-01

    A number derivative is a numerical mapping that satisfies the product rule. In this paper, we determine all number derivatives on the set of integers modulo n. We also give a list of undergraduate research projects to pursue using these maps as a starting point.

  18. Chromosome structures: reduction of certain problems with unequal gene content and gene paralogs to integer linear programming.

    PubMed

    Lyubetsky, Vassily; Gershgorin, Roman; Gorbunov, Konstantin

    2017-12-06

    Chromosome structure is a very limited model of the genome including the information about its chromosomes such as their linear or circular organization, the order of genes on them, and the DNA strand encoding a gene. Gene lengths, nucleotide composition, and intergenic regions are ignored. Although highly incomplete, such structure can be used in many cases, e.g., to reconstruct phylogeny and evolutionary events, to identify gene synteny, regulatory elements and promoters (considering highly conserved elements), etc. Three problems are considered; all assume unequal gene content and the presence of gene paralogs. The distance problem is to determine the minimum number of operations required to transform one chromosome structure into another and the corresponding transformation itself including the identification of paralogs in two structures. We use the DCJ model which is one of the most studied combinatorial rearrangement models. Double-, sesqui-, and single-operations as well as deletion and insertion of a chromosome region are considered in the model; the single ones comprise cut and join. In the reconstruction problem, a phylogenetic tree with chromosome structures in the leaves is given. It is necessary to assign the structures to inner nodes of the tree to minimize the sum of distances between terminal structures of each edge and to identify the mutual paralogs in a fairly large set of structures. A linear algorithm is known for the distance problem without paralogs, while the presence of paralogs makes it NP-hard. If paralogs are allowed but the insertion and deletion operations are missing (and special constraints are imposed), the reduction of the distance problem to integer linear programming is known. Apparently, the reconstruction problem is NP-hard even in the absence of paralogs. The problem of contigs is to find the optimal arrangements for each given set of contigs, which also includes the mutual identification of paralogs. We proved that these

  19. Optimizing Dynamical Network Structure for Pinning Control

    NASA Astrophysics Data System (ADS)

    Orouskhani, Yasin; Jalili, Mahdi; Yu, Xinghuo

    2016-04-01

    Controlling dynamics of a network from any initial state to a final desired state has many applications in different disciplines from engineering to biology and social sciences. In this work, we optimize the network structure for pinning control. The problem is formulated as four optimization tasks: i) optimizing the locations of driver nodes, ii) optimizing the feedback gains, iii) optimizing simultaneously the locations of driver nodes and feedback gains, and iv) optimizing the connection weights. A newly developed population-based optimization technique (cat swarm optimization) is used as the optimization method. In order to verify the methods, we use both real-world networks, and model scale-free and small-world networks. Extensive simulation results show that the optimal placement of driver nodes significantly outperforms heuristic methods including placing drivers based on various centrality measures (degree, betweenness, closeness and clustering coefficient). The pinning controllability is further improved by optimizing the feedback gains. We also show that one can significantly improve the controllability by optimizing the connection weights.

  20. Preserving electron spin coherence in solids by optimal dynamical decoupling.

    PubMed

    Du, Jiangfeng; Rong, Xing; Zhao, Nan; Wang, Ya; Yang, Jiahui; Liu, R B

    2009-10-29

    To exploit the quantum coherence of electron spins in solids in future technologies such as quantum computing, it is first vital to overcome the problem of spin decoherence due to their coupling to the noisy environment. Dynamical decoupling, which uses stroboscopic spin flips to give an average coupling to the environment that is effectively zero, is a particularly promising strategy for combating decoherence because it can be naturally integrated with other desired functionalities, such as quantum gates. Errors are inevitably introduced in each spin flip, so it is desirable to minimize the number of control pulses used to realize dynamical decoupling having a given level of precision. Such optimal dynamical decoupling sequences have recently been explored. The experimental realization of optimal dynamical decoupling in solid-state systems, however, remains elusive. Here we use pulsed electron paramagnetic resonance to demonstrate experimentally optimal dynamical decoupling for preserving electron spin coherence in irradiated malonic acid crystals at temperatures from 50 K to room temperature. Using a seven-pulse optimal dynamical decoupling sequence, we prolonged the spin coherence time to about 30 mus; it would otherwise be about 0.04 mus without control or 6.2 mus under one-pulse control. By comparing experiments with microscopic theories, we have identified the relevant electron spin decoherence mechanisms in the solid. Optimal dynamical decoupling may be applied to other solid-state systems, such as diamonds with nitrogen-vacancy centres, and so lay the foundation for quantum coherence control of spins in solids at room temperature.

  1. Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis :

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

    Adams, Brian M.; Ebeida, Mohamed Salah; Eldred, Michael S.

    The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components requiredmore » for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.« less

  2. Effects of imperfect mixing on low-density polyethylene reactor dynamics

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

    Villa, C.M.; Dihora, J.O.; Ray, W.H.

    1998-07-01

    Earlier work considered the effect of feed conditions and controller configuration on the runaway behavior of LDPE autoclave reactors assuming a perfectly mixed reactor. This study provides additional insight on the dynamics of such reactors by using an imperfectly mixed reactor model and bifurcation analysis to show the changes in the stability region when there is imperfect macroscale mixing. The presence of imperfect mixing substantially increases the range of stable operation of the reactor and makes the process much easier to control than for a perfectly mixed reactor. The results of model analysis and simulations are used to identify somemore » of the conditions that lead to unstable reactor behavior and to suggest ways to avoid reactor runaway or reactor extinction during grade transitions and other process operation disturbances.« less

  3. Dynamics of Transformation from Segregation to Mixed Wealth Cities

    PubMed Central

    Sahasranaman, Anand; Jensen, Henrik Jeldtoft

    2016-01-01

    We model the dynamics of a variation of the Schelling model for agents described simply by a continuously distributed variable—wealth. Agent movement is not dictated by agent choice as in the classic Schelling model, but by their wealth status. Agents move to neighborhoods where their wealth is not lesser than that of some proportion of their neighbors, the threshold level. As in the case of the classic Schelling model, we find here that wealth-based segregation occurs and persists. However, introducing uncertainty into the decision to move—that is, with some probability, if agents are allowed to move even though the threshold condition is contravened—we find that even for small proportions of such disallowed moves, the dynamics no longer yield segregation but instead sharply transition into a persistent mixed wealth distribution, consistent with empirical findings of Benenson, Hatna, and Or. We investigate the nature of this sharp transformation, and find that it is because of a non-linear relationship between allowed moves (moves where threshold condition is satisfied) and disallowed moves (moves where it is not). For small increases in disallowed moves, there is a rapid corresponding increase in allowed moves (before the rate of increase tapers off and tends to zero), and it is the effect of this non-linearity on the dynamics of the system that causes the rapid transition from a segregated to a mixed wealth state. The contravention of the tolerance condition, sanctioning disallowed moves, could be interpreted as public policy interventions to drive de-segregation. Our finding therefore suggests that it might require limited, but continually implemented, public intervention—just sufficient to enable a small, persistently sustained fraction of disallowed moves so as to trigger the dynamics that drive the transformation from a segregated to mixed equilibrium. PMID:27861578

  4. New Approaches to Minimum-Energy Design of Integer- and Fractional-Order Perfect Control Algorithms

    NASA Astrophysics Data System (ADS)

    Hunek, Wojciech P.; Wach, Łukasz

    2017-10-01

    In this paper the new methods concerning the energy-based minimization of the perfect control inputs is presented. For that reason the multivariable integer- and fractional-order models are applied which can be used for describing a various real world processes. Up to now, the classical approaches have been used in forms of minimum-norm/least squares inverses. Notwithstanding, the above-mentioned tool do not guarantee the optimal control corresponding to optimal input energy. Therefore the new class of inversebased methods has been introduced, in particular the new σ- and H-inverse of nonsquare parameter and polynomial matrices. Thus a proposed solution remarkably outperforms the typical ones in systems where the control runs can be understood in terms of different physical quantities, for example heat and mass transfer, electricity etc. A simulation study performed in Matlab/Simulink environment confirms the big potential of the new energy-based approaches.

  5. Uncluttered Single-Image Visualization of Vascular Structures using GPU and Integer Programming

    PubMed Central

    Won, Joong-Ho; Jeon, Yongkweon; Rosenberg, Jarrett; Yoon, Sungroh; Rubin, Geoffrey D.; Napel, Sandy

    2013-01-01

    Direct projection of three-dimensional branching structures, such as networks of cables, blood vessels, or neurons onto a 2D image creates the illusion of intersecting structural parts and creates challenges for understanding and communication. We present a method for visualizing such structures, and demonstrate its utility in visualizing the abdominal aorta and its branches, whose tomographic images might be obtained by computed tomography or magnetic resonance angiography, in a single two-dimensional stylistic image, without overlaps among branches. The visualization method, termed uncluttered single-image visualization (USIV), involves optimization of geometry. This paper proposes a novel optimization technique that utilizes an interesting connection of the optimization problem regarding USIV to the protein structure prediction problem. Adopting the integer linear programming-based formulation for the protein structure prediction problem, we tested the proposed technique using 30 visualizations produced from five patient scans with representative anatomical variants in the abdominal aortic vessel tree. The novel technique can exploit commodity-level parallelism, enabling use of general-purpose graphics processing unit (GPGPU) technology that yields a significant speedup. Comparison of the results with the other optimization technique previously reported elsewhere suggests that, in most aspects, the quality of the visualization is comparable to that of the previous one, with a significant gain in the computation time of the algorithm. PMID:22291148

  6. A new approach to mixed H2/H infinity controller synthesis using gradient-based parameter optimization methods

    NASA Technical Reports Server (NTRS)

    Ly, Uy-Loi; Schoemig, Ewald

    1993-01-01

    In the past few years, the mixed H(sub 2)/H-infinity control problem has been the object of much research interest since it allows the incorporation of robust stability into the LQG framework. The general mixed H(sub 2)/H-infinity design problem has yet to be solved analytically. Numerous schemes have considered upper bounds for the H(sub 2)-performance criterion and/or imposed restrictive constraints on the class of systems under investigation. Furthermore, many modern control applications rely on dynamic models obtained from finite-element analysis and thus involve high-order plant models. Hence the capability to design low-order (fixed-order) controllers is of great importance. In this research a new design method was developed that optimizes the exact H(sub 2)-norm of a certain subsystem subject to robust stability in terms of H-infinity constraints and a minimal number of system assumptions. The derived algorithm is based on a differentiable scalar time-domain penalty function to represent the H-infinity constraints in the overall optimization. The scheme is capable of handling multiple plant conditions and hence multiple performance criteria and H-infinity constraints and incorporates additional constraints such as fixed-order and/or fixed structure controllers. The defined penalty function is applicable to any constraint that is expressible in form of a real symmetric matrix-inequity.

  7. Regulation of Dynamical Systems to Optimal Solutions of Semidefinite Programs: Algorithms and Applications to AC Optimal Power Flow

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

    Dall'Anese, Emiliano; Dhople, Sairaj V.; Giannakis, Georgios B.

    2015-07-01

    This paper considers a collection of networked nonlinear dynamical systems, and addresses the synthesis of feedback controllers that seek optimal operating points corresponding to the solution of pertinent network-wide optimization problems. Particular emphasis is placed on the solution of semidefinite programs (SDPs). The design of the feedback controller is grounded on a dual e-subgradient approach, with the dual iterates utilized to dynamically update the dynamical-system reference signals. Global convergence is guaranteed for diminishing stepsize rules, even when the reference inputs are updated at a faster rate than the dynamical-system settling time. The application of the proposed framework to the controlmore » of power-electronic inverters in AC distribution systems is discussed. The objective is to bridge the time-scale separation between real-time inverter control and network-wide optimization. Optimization objectives assume the form of SDP relaxations of prototypical AC optimal power flow problems.« less

  8. Integer ambiguity resolution in precise point positioning: method comparison

    NASA Astrophysics Data System (ADS)

    Geng, Jianghui; Meng, Xiaolin; Dodson, Alan H.; Teferle, Felix N.

    2010-09-01

    Integer ambiguity resolution at a single receiver can be implemented by applying improved satellite products where the fractional-cycle biases (FCBs) have been separated from the integer ambiguities in a network solution. One method to achieve these products is to estimate the FCBs by averaging the fractional parts of the float ambiguity estimates, and the other is to estimate the integer-recovery clocks by fixing the undifferenced ambiguities to integers in advance. In this paper, we theoretically prove the equivalence of the ambiguity-fixed position estimates derived from these two methods by assuming that the FCBs are hardware-dependent and only they are assimilated into the clocks and ambiguities. To verify this equivalence, we implement both methods in the Position and Navigation Data Analyst software to process 1 year of GPS data from a global network of about 350 stations. The mean biases between all daily position estimates derived from these two methods are only 0.2, 0.1 and 0.0 mm, whereas the standard deviations of all position differences are only 1.3, 0.8 and 2.0 mm for the East, North and Up components, respectively. Moreover, the differences of the position repeatabilities are below 0.2 mm on average for all three components. The RMS of the position estimates minus those from the International GNSS Service weekly solutions for the former method differs by below 0.1 mm on average for each component from that for the latter method. Therefore, considering the recognized millimeter-level precision of current GPS-derived daily positions, these statistics empirically demonstrate the theoretical equivalence of the ambiguity-fixed position estimates derived from these two methods. In practice, we note that the former method is compatible with current official clock-generation methods, whereas the latter method is not, but can potentially lead to slightly better positioning quality.

  9. Recursive multibody dynamics and discrete-time optimal control

    NASA Technical Reports Server (NTRS)

    Deleuterio, G. M. T.; Damaren, C. J.

    1989-01-01

    A recursive algorithm is developed for the solution of the simulation dynamics problem for a chain of rigid bodies. Arbitrary joint constraints are permitted, that is, joints may allow translational and/or rotational degrees of freedom. The recursive procedure is shown to be identical to that encountered in a discrete-time optimal control problem. For each relevant quantity in the multibody dynamics problem, there exists an analog in the context of optimal control. The performance index that is minimized in the control problem is identified as Gibbs' function for the chain of bodies.

  10. Synthesizing optimal waste blends

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

    Narayan, V.; Diwekar, W.M.; Hoza, M.

    Vitrification of tank wastes to form glass is a technique that will be used for the disposal of high-level waste at Hanford. Process and storage economics show that minimizing the total number of glass logs produced is the key to keeping cost as low as possible. The amount of glass produced can be reduced by blending of the wastes. The optimal way to combine the tanks to minimize the vole of glass can be determined from a discrete blend calculation. However, this problem results in a combinatorial explosion as the number of tanks increases. Moreover, the property constraints make thismore » problem highly nonconvex where many algorithms get trapped in local minima. In this paper the authors examine the use of different combinatorial optimization approaches to solve this problem. A two-stage approach using a combination of simulated annealing and nonlinear programming (NLP) is developed. The results of different methods such as the heuristics approach based on human knowledge and judgment, the mixed integer nonlinear programming (MINLP) approach with GAMS, and branch and bound with lower bound derived from the structure of the given blending problem are compared with this coupled simulated annealing and NLP approach.« less

  11. Rarity-weighted richness: a simple and reliable alternative to integer programming and heuristic algorithms for minimum set and maximum coverage problems in conservation planning.

    PubMed

    Albuquerque, Fabio; Beier, Paul

    2015-01-01

    Here we report that prioritizing sites in order of rarity-weighted richness (RWR) is a simple, reliable way to identify sites that represent all species in the fewest number of sites (minimum set problem) or to identify sites that represent the largest number of species within a given number of sites (maximum coverage problem). We compared the number of species represented in sites prioritized by RWR to numbers of species represented in sites prioritized by the Zonation software package for 11 datasets in which the size of individual planning units (sites) ranged from <1 ha to 2,500 km2. On average, RWR solutions were more efficient than Zonation solutions. Integer programming remains the only guaranteed way find an optimal solution, and heuristic algorithms remain superior for conservation prioritizations that consider compactness and multiple near-optimal solutions in addition to species representation. But because RWR can be implemented easily and quickly in R or a spreadsheet, it is an attractive alternative to integer programming or heuristic algorithms in some conservation prioritization contexts.

  12. Prepositioning emergency supplies under uncertainty: a parametric optimization method

    NASA Astrophysics Data System (ADS)

    Bai, Xuejie; Gao, Jinwu; Liu, Yankui

    2018-07-01

    Prepositioning of emergency supplies is an effective method for increasing preparedness for disasters and has received much attention in recent years. In this article, the prepositioning problem is studied by a robust parametric optimization method. The transportation cost, supply, demand and capacity are unknown prior to the extraordinary event, which are represented as fuzzy parameters with variable possibility distributions. The variable possibility distributions are obtained through the credibility critical value reduction method for type-2 fuzzy variables. The prepositioning problem is formulated as a fuzzy value-at-risk model to achieve a minimum total cost incurred in the whole process. The key difficulty in solving the proposed optimization model is to evaluate the quantile of the fuzzy function in the objective and the credibility in the constraints. The objective function and constraints can be turned into their equivalent parametric forms through chance constrained programming under the different confidence levels. Taking advantage of the structural characteristics of the equivalent optimization model, a parameter-based domain decomposition method is developed to divide the original optimization problem into six mixed-integer parametric submodels, which can be solved by standard optimization solvers. Finally, to explore the viability of the developed model and the solution approach, some computational experiments are performed on realistic scale case problems. The computational results reported in the numerical example show the credibility and superiority of the proposed parametric optimization method.

  13. MORE: mixed optimization for reverse engineering--an application to modeling biological networks response via sparse systems of nonlinear differential equations.

    PubMed

    Sambo, Francesco; de Oca, Marco A Montes; Di Camillo, Barbara; Toffolo, Gianna; Stützle, Thomas

    2012-01-01

    Reverse engineering is the problem of inferring the structure of a network of interactions between biological variables from a set of observations. In this paper, we propose an optimization algorithm, called MORE, for the reverse engineering of biological networks from time series data. The model inferred by MORE is a sparse system of nonlinear differential equations, complex enough to realistically describe the dynamics of a biological system. MORE tackles separately the discrete component of the problem, the determination of the biological network topology, and the continuous component of the problem, the strength of the interactions. This approach allows us both to enforce system sparsity, by globally constraining the number of edges, and to integrate a priori information about the structure of the underlying interaction network. Experimental results on simulated and real-world networks show that the mixed discrete/continuous optimization approach of MORE significantly outperforms standard continuous optimization and that MORE is competitive with the state of the art in terms of accuracy of the inferred networks.

  14. A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: II. Probabilistic Guarantees on Constraint Satisfaction

    PubMed Central

    Li, Zukui; Floudas, Christodoulos A.

    2012-01-01

    Probabilistic guarantees on constraint satisfaction for robust counterpart optimization are studied in this paper. The robust counterpart optimization formulations studied are derived from box, ellipsoidal, polyhedral, “interval+ellipsoidal” and “interval+polyhedral” uncertainty sets (Li, Z., Ding, R., and Floudas, C.A., A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: I. Robust Linear and Robust Mixed Integer Linear Optimization, Ind. Eng. Chem. Res, 2011, 50, 10567). For those robust counterpart optimization formulations, their corresponding probability bounds on constraint satisfaction are derived for different types of uncertainty characteristic (i.e., bounded or unbounded uncertainty, with or without detailed probability distribution information). The findings of this work extend the results in the literature and provide greater flexibility for robust optimization practitioners in choosing tighter probability bounds so as to find less conservative robust solutions. Extensive numerical studies are performed to compare the tightness of the different probability bounds and the conservatism of different robust counterpart optimization formulations. Guiding rules for the selection of robust counterpart optimization models and for the determination of the size of the uncertainty set are discussed. Applications in production planning and process scheduling problems are presented. PMID:23329868

  15. A multiobjective optimization model and an orthogonal design-based hybrid heuristic algorithm for regional urban mining management problems.

    PubMed

    Wu, Hao; Wan, Zhong

    2018-02-01

    In this paper, a multiobjective mixed-integer piecewise nonlinear programming model (MOMIPNLP) is built to formulate the management problem of urban mining system, where the decision variables are associated with buy-back pricing, choices of sites, transportation planning, and adjustment of production capacity. Different from the existing approaches, the social negative effect, generated from structural optimization of the recycling system, is minimized in our model, as well as the total recycling profit and utility from environmental improvement are jointly maximized. For solving the problem, the MOMIPNLP model is first transformed into an ordinary mixed-integer nonlinear programming model by variable substitution such that the piecewise feature of the model is removed. Then, based on technique of orthogonal design, a hybrid heuristic algorithm is developed to find an approximate Pareto-optimal solution, where genetic algorithm is used to optimize the structure of search neighborhood, and both local branching algorithm and relaxation-induced neighborhood search algorithm are employed to cut the searching branches and reduce the number of variables in each branch. Numerical experiments indicate that this algorithm spends less CPU (central processing unit) time in solving large-scale regional urban mining management problems, especially in comparison with the similar ones available in literature. By case study and sensitivity analysis, a number of practical managerial implications are revealed from the model. Since the metal stocks in society are reliable overground mineral sources, urban mining has been paid great attention as emerging strategic resources in an era of resource shortage. By mathematical modeling and development of efficient algorithms, this paper provides decision makers with useful suggestions on the optimal design of recycling system in urban mining. For example, this paper can answer how to encourage enterprises to join the recycling activities

  16. Measurement configuration optimization for dynamic metrology using Stokes polarimetry

    NASA Astrophysics Data System (ADS)

    Liu, Jiamin; Zhang, Chuanwei; Zhong, Zhicheng; Gu, Honggang; Chen, Xiuguo; Jiang, Hao; Liu, Shiyuan

    2018-05-01

    As dynamic loading experiments such as a shock compression test are usually characterized by short duration, unrepeatability and high costs, high temporal resolution and precise accuracy of the measurements is required. Due to high temporal resolution up to a ten-nanosecond-scale, a Stokes polarimeter with six parallel channels has been developed to capture such instantaneous changes in optical properties in this paper. Since the measurement accuracy heavily depends on the configuration of the probing beam incident angle and the polarizer azimuth angle, it is important to select an optimal combination from the numerous options. In this paper, a systematic error propagation-based measurement configuration optimization method corresponding to the Stokes polarimeter was proposed. The maximal Frobenius norm of the combinatorial matrix of the configuration error propagating matrix and the intrinsic error propagating matrix is introduced to assess the measurement accuracy. The optimal configuration for thickness measurement of a SiO2 thin film deposited on a Si substrate has been achieved by minimizing the merit function. Simulation and experimental results show a good agreement between the optimal measurement configuration achieved experimentally using the polarimeter and the theoretical prediction. In particular, the experimental result shows that the relative error in the thickness measurement can be reduced from 6% to 1% by using the optimal polarizer azimuth angle when the incident angle is 45°. Furthermore, the optimal configuration for the dynamic metrology of a nickel foil under quasi-dynamic loading is investigated using the proposed optimization method.

  17. An Optimization Framework for Dynamic, Distributed Real-Time Systems

    NASA Technical Reports Server (NTRS)

    Eckert, Klaus; Juedes, David; Welch, Lonnie; Chelberg, David; Bruggerman, Carl; Drews, Frank; Fleeman, David; Parrott, David; Pfarr, Barbara

    2003-01-01

    Abstract. This paper presents a model that is useful for developing resource allocation algorithms for distributed real-time systems .that operate in dynamic environments. Interesting aspects of the model include dynamic environments, utility and service levels, which provide a means for graceful degradation in resource-constrained situations and support optimization of the allocation of resources. The paper also provides an allocation algorithm that illustrates how to use the model for producing feasible, optimal resource allocations.

  18. Optimization of municipal solid waste collection and transportation routes.

    PubMed

    Das, Swapan; Bhattacharyya, Bidyut Kr

    2015-09-01

    Optimization of municipal solid waste (MSW) collection and transportation through source separation becomes one of the major concerns in the MSW management system design, due to the fact that the existing MSW management systems suffer by the high collection and transportation cost. Generally, in a city different waste sources scatter throughout the city in heterogeneous way that increase waste collection and transportation cost in the waste management system. Therefore, a shortest waste collection and transportation strategy can effectively reduce waste collection and transportation cost. In this paper, we propose an optimal MSW collection and transportation scheme that focus on the problem of minimizing the length of each waste collection and transportation route. We first formulize the MSW collection and transportation problem into a mixed integer program. Moreover, we propose a heuristic solution for the waste collection and transportation problem that can provide an optimal way for waste collection and transportation. Extensive simulations and real testbed results show that the proposed solution can significantly improve the MSW performance. Results show that the proposed scheme is able to reduce more than 30% of the total waste collection path length. Copyright © 2015 Elsevier Ltd. All rights reserved.

  19. Optimal control and Galois theory

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

    Zelikin, M I; Kiselev, D D; Lokutsievskiy, L V

    2013-11-30

    An important role is played in the solution of a class of optimal control problems by a certain special polynomial of degree 2(n−1) with integer coefficients. The linear independence of a family of k roots of this polynomial over the field Q implies the existence of a solution of the original problem with optimal control in the form of an irrational winding of a k-dimensional Clifford torus, which is passed in finite time. In the paper, we prove that for n≤15 one can take an arbitrary positive integer not exceeding [n/2] for k. The apparatus developed in the paper is applied to the systems ofmore » Chebyshev-Hermite polynomials and generalized Chebyshev-Laguerre polynomials. It is proved that for such polynomials of degree 2m every subsystem of [(m+1)/2] roots with pairwise distinct squares is linearly independent over the field Q. Bibliography: 11 titles.« less

  20. Fabry-Perot Interferometry in the Integer and Fractional Quantum Hall Regimes

    NASA Astrophysics Data System (ADS)

    McClure, Douglas; Chang, Willy; Kou, Angela; Marcus, Charles; Pfeiffer, Loren; West, Ken

    2011-03-01

    We present measurements of electronic Fabry-Perot interferometers in the integer and fractional quantum Hall regimes. Two classes of resistance oscillations may be seen as a function of magnetic field and gate voltage, as we have previously reported. In small interferometers in the integer regime, oscillations of the type associated with Coulomb interaction are ubiquitous, while those consistent with single-particle Aharonov-Bohm interference are seen to co-exist in some configurations. The amplitude scaling of both types with temperature and device size is consistent with a theoretical model. Oscillations are further observed in the fractional quantum Hall regime. Here the dependence of the period on the filling factors in the constrictions and bulk of the interferometer can shed light on the effective charge of the interfering quasiparticles, but care is needed to distinguish these oscillations from those associated with integer quantum Hall states. We acknowledge funding from Microsoft Project Q and IBM.

  1. Optimizing legacy molecular dynamics software with directive-based offload

    NASA Astrophysics Data System (ADS)

    Michael Brown, W.; Carrillo, Jan-Michael Y.; Gavhane, Nitin; Thakkar, Foram M.; Plimpton, Steven J.

    2015-10-01

    Directive-based programming models are one solution for exploiting many-core coprocessors to increase simulation rates in molecular dynamics. They offer the potential to reduce code complexity with offload models that can selectively target computations to run on the CPU, the coprocessor, or both. In this paper, we describe modifications to the LAMMPS molecular dynamics code to enable concurrent calculations on a CPU and coprocessor. We demonstrate that standard molecular dynamics algorithms can run efficiently on both the CPU and an x86-based coprocessor using the same subroutines. As a consequence, we demonstrate that code optimizations for the coprocessor also result in speedups on the CPU; in extreme cases up to 4.7X. We provide results for LAMMPS benchmarks and for production molecular dynamics simulations using the Stampede hybrid supercomputer with both Intel® Xeon Phi™ coprocessors and NVIDIA GPUs. The optimizations presented have increased simulation rates by over 2X for organic molecules and over 7X for liquid crystals on Stampede. The optimizations are available as part of the "Intel package" supplied with LAMMPS.

  2. The impact of short-term stochastic variability in solar irradiance on optimal microgrid design

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

    Schittekatte, Tim; Stadler, Michael; Cardoso, Gonçalo

    2016-07-01

    This paper proposes a new methodology to capture the impact of fast moving clouds on utility power demand charges observed in microgrids with photovoltaic (PV) arrays, generators, and electrochemical energy storage. It consists of a statistical approach to introduce sub-hourly events in the hourly economic accounting process. The methodology is implemented in the Distributed Energy Resources Customer Adoption Model (DER-CAM), a state of the art mixed integer linear model used to optimally size DER in decentralized energy systems. Results suggest that previous iterations of DER-CAM could undersize battery capacities. The improved model depicts more accurately the economic value of PVmore » as well as the synergistic benefits of pairing PV with storage.« less

  3. Optimal response to attacks on the open science grids.

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

    Altunay, M.; Leyffer, S.; Linderoth, J. T.

    2011-01-01

    Cybersecurity is a growing concern, especially in open grids, where attack propagation is easy because of prevalent collaborations among thousands of users and hundreds of institutions. The collaboration rules that typically govern large science experiments as well as social networks of scientists span across the institutional security boundaries. A common concern is that the increased openness may allow malicious attackers to spread more readily around the grid. We consider how to optimally respond to attacks in open grid environments. To show how and why attacks spread more readily around the grid, we first discuss how collaborations manifest themselves in themore » grids and form the collaboration network graph, and how this collaboration network graph affects the security threat levels of grid participants. We present two mixed-integer program (MIP) models to find the optimal response to attacks in open grid environments, and also calculate the threat level associated with each grid participant. Given an attack scenario, our optimal response model aims to minimize the threat levels at unaffected participants while maximizing the uninterrupted scientific production (continuing collaborations). By adopting some of the collaboration rules (e.g., suspending a collaboration or shutting down a site), the model finds optimal response to subvert an attack scenario.« less

  4. A Unified Approach to Optimization

    DTIC Science & Technology

    2014-10-02

    employee scheduling, ad placement, latin squares, disjunctions of linear systems, temporal modeling with interval variables, and traveling salesman problems ...integrating technologies. A key to integrated modeling is to formulate a problem with high-levelmetaconstraints, which are inspired by the “global... problem substructure to the solver. This contrasts with the atomistic modeling style of mixed integer programming (MIP) and satisfiability (SAT) solvers

  5. Practical synchronization on complex dynamical networks via optimal pinning control

    NASA Astrophysics Data System (ADS)

    Li, Kezan; Sun, Weigang; Small, Michael; Fu, Xinchu

    2015-07-01

    We consider practical synchronization on complex dynamical networks under linear feedback control designed by optimal control theory. The control goal is to minimize global synchronization error and control strength over a given finite time interval, and synchronization error at terminal time. By utilizing the Pontryagin's minimum principle, and based on a general complex dynamical network, we obtain an optimal system to achieve the control goal. The result is verified by performing some numerical simulations on Star networks, Watts-Strogatz networks, and Barabási-Albert networks. Moreover, by combining optimal control and traditional pinning control, we propose an optimal pinning control strategy which depends on the network's topological structure. Obtained results show that optimal pinning control is very effective for synchronization control in real applications.

  6. The whole number axis integer linear transformation reversible information hiding algorithm on wavelet domain

    NASA Astrophysics Data System (ADS)

    Jiang, Zhuo; Xie, Chengjun

    2013-12-01

    This paper improved the algorithm of reversible integer linear transform on finite interval [0,255], which can realize reversible integer linear transform in whole number axis shielding data LSB (least significant bit). Firstly, this method use integer wavelet transformation based on lifting scheme to transform the original image, and select the transformed high frequency areas as information hiding area, meanwhile transform the high frequency coefficients blocks in integer linear way and embed the secret information in LSB of each coefficient, then information hiding by embedding the opposite steps. To extract data bits and recover the host image, a similar reverse procedure can be conducted, and the original host image can be lossless recovered. The simulation experimental results show that this method has good secrecy and concealment, after conducted the CDF (m, n) and DD (m, n) series of wavelet transformed. This method can be applied to information security domain, such as medicine, law and military.

  7. Optimal control of multiphoton ionization dynamics of small alkali aggregates

    NASA Astrophysics Data System (ADS)

    Lindinger, A.; Bartelt, A.; Lupulescu, C.; Vajda, S.; Woste, Ludger

    2003-11-01

    We have performed transient multi-photon ionization experiments on small alkali clusters of different size in order to probe their wave packet dynamics, structural reorientations, charge transfers and dissociative events in different vibrationally excited electronic states including their ground state. The observed processes were highly dependent on the irradiated pulse parameters like wavelength range or its phase and amplitude; an emphasis to employ a feedback control system for generating the optimum pulse shapes. Their spectral and temporal behavior reflects interesting properties about the investigated system and the irradiated photo-chemical process. First, we present the vibrational dynamics of bound electronically excited states of alkali dimers and trimers. The scheme for observing the wave packet dynamics in the electronic ground state using stimulated Raman-pumping is shown. Since the employed pulse parameters significantly influence the efficiency of the irradiated dynamic pathways photo-induced ioniziation experiments were carried out. The controllability of 3-photon ionization pathways is investigated on the model-like systems NaK and K2. A closed learning loop for adaptive feedback control is used to find the optimal fs pulse shape. Sinusoidal parameterizations of the spectral phase modulation are investigated in regard to the obtained optimal field. By reducing the number of parameters and thereby the complexity of the phase moduation, optimal pulse shapes can be generated that carry fingerprints of the molecule's dynamical properties. This enables to find "understandable" optimal pulse forms and offers the possiblity to gain insight into the photo-induced control process. Characteristic motions of the involved wave packets are proposed to explain the optimized dynamic dissociation pathways.

  8. Fixed-Order Mixed Norm Designs for Building Vibration Control

    NASA Technical Reports Server (NTRS)

    Whorton, Mark S.; Calise, Anthony J.

    2000-01-01

    This study investigates the use of H2, mu-synthesis, and mixed H2/mu methods to construct full order controllers and optimized controllers of fixed dimensions. The benchmark problem definition is first extended to include uncertainty within the controller bandwidth in the form of parametric uncertainty representative of uncertainty in the natural frequencies of the design model. The sensitivity of H2 design to unmodeled dynamics and parametric uncertainty is evaluated for a range of controller levels of authority. Next, mu-synthesis methods are applied to design full order compensators that are robust to both unmodeled dynamics and to parametric uncertainty. Finally, a set of mixed H2/mu compensators are designed which are optimized for a fixed compensator dimension. These mixed norm designs recover the H2 design performance levels while providing the same levels of robust stability as the mu designs. It is shown that designing with the mixed norm approach permits higher levels of controller authority for which the H2 designs are destabilizing. The benchmark problem is that of an active tendon system. The controller designs are all based on the use of acceleration feedback.

  9. Mixing Dynamics Induced by Traveling Magnetic Fields

    NASA Technical Reports Server (NTRS)

    Grugel, Richard N.; Mazuruk, Konstantin

    2000-01-01

    Microstructural and compositional homogeneity in metals and alloys can only be achieved if the initial melt is homogeneous prior to the onset of solidification processing. Naturally induced convection may initially facilitate this requirement but upon the onset of solidification significant compositional variations generally arise leading to undesired segregation. Application of alternating magnetic fields to promote a uniform bulk liquid concentration during solidification processing has been suggested. To investigate such possibilities an initial study of using traveling magnetic fields (TMF) to promote melt homogenization is reported in this work. Theoretically, the effect of TMF-induced convection on mixing phenomena is studied in the laminar regime of flow. Experimentally, with and without applied fields, both: mixing dynamics by optically monitoring the spreading of an initially localized dye in transparent fluids and, compositional variations in metal alloys have been investigated.

  10. Mixing Dynamics Induced by Traveling Magnetic Fields

    NASA Technical Reports Server (NTRS)

    Grugel, Richard N.; Mazuruk, Konstantin; Rose, M. Franklin (Technical Monitor)

    2001-01-01

    Microstructural and compositional homogeneity in metals and alloys can only be achieved if the initial melt is homogeneous prior to the onset of solidification processing. Naturally induced convection may initially facilitate this requirement but upon the onset of solidification significant compositional variations generally arise leading to undesired segregation. Application of alternating magnetic fields to promote a uniform bulk liquid concentration during solidification processing has been suggested. To investigate such possibilities an initial study of using traveling magnetic fields (TMF) to promote melt homogenization is reported in this work. Theoretically, the effect of TMF-induced convection on mixing phenomena is studied in the laminar regime of flow. Experimentally, with and without applied fields, both 1) mixing dynamics by optically monitoring the spreading of an initially localized dye in transparent fluids and, 2) compositional variations in metal alloys have been investigated.

  11. Predicting Displaceable Water Sites Using Mixed-Solvent Molecular Dynamics.

    PubMed

    Graham, Sarah E; Smith, Richard D; Carlson, Heather A

    2018-02-26

    Water molecules are an important factor in protein-ligand binding. Upon binding of a ligand with a protein's surface, waters can either be displaced by the ligand or may be conserved and possibly bridge interactions between the protein and ligand. Depending on the specific interactions made by the ligand, displacing waters can yield a gain in binding affinity. The extent to which binding affinity may increase is difficult to predict, as the favorable displacement of a water molecule is dependent on the site-specific interactions made by the water and the potential ligand. Several methods have been developed to predict the location of water sites on a protein's surface, but the majority of methods are not able to take into account both protein dynamics and the interactions made by specific functional groups. Mixed-solvent molecular dynamics (MixMD) is a cosolvent simulation technique that explicitly accounts for the interaction of both water and small molecule probes with a protein's surface, allowing for their direct competition. This method has previously been shown to identify both active and allosteric sites on a protein's surface. Using a test set of eight systems, we have developed a method using MixMD to identify conserved and displaceable water sites. Conserved sites can be determined by an occupancy-based metric to identify sites which are consistently occupied by water even in the presence of probe molecules. Conversely, displaceable water sites can be found by considering the sites which preferentially bind probe molecules. Furthermore, the inclusion of six probe types allows the MixMD method to predict which functional groups are capable of displacing which water sites. The MixMD method consistently identifies sites which are likely to be nondisplaceable and predicts the favorable displacement of water sites that are known to be displaced upon ligand binding.

  12. Optimal multi-floor plant layout based on the mathematical programming and particle swarm optimization.

    PubMed

    Lee, Chang Jun

    2015-01-01

    In the fields of researches associated with plant layout optimization, the main goal is to minimize the costs of pipelines and pumping between connecting equipment under various constraints. However, what is the lacking of considerations in previous researches is to transform various heuristics or safety regulations into mathematical equations. For example, proper safety distances between equipments have to be complied for preventing dangerous accidents on a complex plant. Moreover, most researches have handled single-floor plant. However, many multi-floor plants have been constructed for the last decade. Therefore, the proper algorithm handling various regulations and multi-floor plant should be developed. In this study, the Mixed Integer Non-Linear Programming (MINLP) problem including safety distances, maintenance spaces, etc. is suggested based on mathematical equations. The objective function is a summation of pipeline and pumping costs. Also, various safety and maintenance issues are transformed into inequality or equality constraints. However, it is really hard to solve this problem due to complex nonlinear constraints. Thus, it is impossible to use conventional MINLP solvers using derivatives of equations. In this study, the Particle Swarm Optimization (PSO) technique is employed. The ethylene oxide plant is illustrated to verify the efficacy of this study.

  13. Optimal control of anthracnose using mixed strategies.

    PubMed

    Fotsa Mbogne, David Jaures; Thron, Christopher

    2015-11-01

    In this paper we propose and study a spatial diffusion model for the control of anthracnose disease in a bounded domain. The model is a generalization of the one previously developed in [15]. We use the model to simulate two different types of control strategies against anthracnose disease. Strategies that employ chemical fungicides are modeled using a continuous control function; while strategies that rely on cultivational practices (such as pruning and removal of mummified fruits) are modeled with a control function which is discrete in time (though not in space). For comparative purposes, we perform our analyses for a spatially-averaged model as well as the space-dependent diffusion model. Under weak smoothness conditions on parameters we demonstrate the well-posedness of both models by verifying existence and uniqueness of the solution for the growth inhibition rate for given initial conditions. We also show that the set [0, 1] is positively invariant. We first study control by impulsive strategies, then analyze the simultaneous use of mixed continuous and pulse strategies. In each case we specify a cost functional to be minimized, and we demonstrate the existence of optimal control strategies. In the case of pulse-only strategies, we provide explicit algorithms for finding the optimal control strategies for both the spatially-averaged model and the space-dependent model. We verify the algorithms for both models via simulation, and discuss properties of the optimal solutions. Copyright © 2015 Elsevier Inc. All rights reserved.

  14. Suppression of chaos at slow variables by rapidly mixing fast dynamics

    NASA Astrophysics Data System (ADS)

    Abramov, R.

    2012-04-01

    One of the key questions about chaotic multiscale systems is how the fast dynamics affects chaos at the slow variables, and, therefore, impacts uncertainty and predictability of the slow dynamics. Here we demonstrate that the linear slow-fast coupling with the total energy conservation property promotes the suppression of chaos at the slow variables through the rapid mixing at the fast variables, both theoretically and through numerical simulations. A suitable mathematical framework is developed, connecting the slow dynamics on the tangent subspaces to the infinite-time linear response of the mean state to a constant external forcing at the fast variables. Additionally, it is shown that the uncoupled dynamics for the slow variables may remain chaotic while the complete multiscale system loses chaos and becomes completely predictable at the slow variables through increasing chaos and turbulence at the fast variables. This result contradicts the common sense intuition, where, naturally, one would think that coupling a slow weakly chaotic system with another much faster and much stronger mixing system would result in general increase of chaos at the slow variables.

  15. An approach to design controllers for MIMO fractional-order plants based on parameter optimization algorithm.

    PubMed

    Xue, Dingyü; Li, Tingxue

    2017-04-27

    The parameter optimization method for multivariable systems is extended to the controller design problems for multiple input multiple output (MIMO) square fractional-order plants. The algorithm can be applied to search for the optimal parameters of integer-order controllers for fractional-order plants with or without time delays. Two examples are given to present the controller design procedures for MIMO fractional-order systems. Simulation studies show that the integer-order controllers designed are robust to plant gain variations. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  16. Triangular Numbers, Gaussian Integers, and KenKen

    ERIC Educational Resources Information Center

    Watkins, John J.

    2012-01-01

    Latin squares form the basis for the recreational puzzles sudoku and KenKen. In this article we show how useful several ideas from number theory are in solving a KenKen puzzle. For example, the simple notion of triangular number is surprisingly effective. We also introduce a variation of KenKen that uses the Gaussian integers in order to…

  17. Optimal control of HIV/AIDS dynamic: Education and treatment

    NASA Astrophysics Data System (ADS)

    Sule, Amiru; Abdullah, Farah Aini

    2014-07-01

    A mathematical model which describes the transmission dynamics of HIV/AIDS is developed. The optimal control representing education and treatment for this model is explored. The existence of optimal Control is established analytically by the use of optimal control theory. Numerical simulations suggest that education and treatment for the infected has a positive impact on HIV/AIDS control.

  18. Global dynamic optimization approach to predict activation in metabolic pathways.

    PubMed

    de Hijas-Liste, Gundián M; Klipp, Edda; Balsa-Canto, Eva; Banga, Julio R

    2014-01-06

    During the last decade, a number of authors have shown that the genetic regulation of metabolic networks may follow optimality principles. Optimal control theory has been successfully used to compute optimal enzyme profiles considering simple metabolic pathways. However, applying this optimal control framework to more general networks (e.g. branched networks, or networks incorporating enzyme production dynamics) yields problems that are analytically intractable and/or numerically very challenging. Further, these previous studies have only considered a single-objective framework. In this work we consider a more general multi-objective formulation and we present solutions based on recent developments in global dynamic optimization techniques. We illustrate the performance and capabilities of these techniques considering two sets of problems. First, we consider a set of single-objective examples of increasing complexity taken from the recent literature. We analyze the multimodal character of the associated non linear optimization problems, and we also evaluate different global optimization approaches in terms of numerical robustness, efficiency and scalability. Second, we consider generalized multi-objective formulations for several examples, and we show how this framework results in more biologically meaningful results. The proposed strategy was used to solve a set of single-objective case studies related to unbranched and branched metabolic networks of different levels of complexity. All problems were successfully solved in reasonable computation times with our global dynamic optimization approach, reaching solutions which were comparable or better than those reported in previous literature. Further, we considered, for the first time, multi-objective formulations, illustrating how activation in metabolic pathways can be explained in terms of the best trade-offs between conflicting objectives. This new methodology can be applied to metabolic networks with arbitrary

  19. Optimal mission planning of GEO on-orbit refueling in mixed strategy

    NASA Astrophysics Data System (ADS)

    Chen, Xiao-qian; Yu, Jing

    2017-04-01

    The mission planning of GEO on-orbit refueling (OOR) in Mixed strategy is studied in this paper. Specifically, one SSc will be launched to an orbital slot near the depot when multiple GEO satellites are reaching their end of lives. The SSc replenishes fuel from the depot and then extends the lifespan of the target satellites via refueling. In the mixed scenario, only some of the target satellites could be served by the SSc, and the remaining ones will be fueled by Pseudo SScs (the target satellite which has already been refueled by the SSc and now has sufficient fuel for its operation as well as the fuel to refuel other target satellites is called Pseudo SSc here). The mission sequences and fuel mass of the SSc and Pseudo SScs, the dry mass of the SSc are used as design variables, whereas the economic benefit of the whole mission is used as design objective. The economic cost and benefit models are stated first, and then a mathematical optimization model is proposed. A comprehensive solution method involving enumeration, particle swarm optimization and modification is developed. Numerical examples are carried out to demonstrate the effectiveness of the model and solution method. Economic efficiencies of different OOR strategies are compared and discussed. The mixed strategy would perform better than the other strategies only when the target satellites satisfy some conditions. This paper presents an available mixed strategy scheme for users and analyzes its advantages and disadvantages by comparing with some other OOR strategies, providing helpful references to decision makers. The best strategy in practical applications depends on the specific demands and user preference.

  20. Optimizing legacy molecular dynamics software with directive-based offload

    DOE PAGES

    Michael Brown, W.; Carrillo, Jan-Michael Y.; Gavhane, Nitin; ...

    2015-05-14

    The directive-based programming models are one solution for exploiting many-core coprocessors to increase simulation rates in molecular dynamics. They offer the potential to reduce code complexity with offload models that can selectively target computations to run on the CPU, the coprocessor, or both. In our paper, we describe modifications to the LAMMPS molecular dynamics code to enable concurrent calculations on a CPU and coprocessor. We also demonstrate that standard molecular dynamics algorithms can run efficiently on both the CPU and an x86-based coprocessor using the same subroutines. As a consequence, we demonstrate that code optimizations for the coprocessor also resultmore » in speedups on the CPU; in extreme cases up to 4.7X. We provide results for LAMMAS benchmarks and for production molecular dynamics simulations using the Stampede hybrid supercomputer with both Intel (R) Xeon Phi (TM) coprocessors and NVIDIA GPUs: The optimizations presented have increased simulation rates by over 2X for organic molecules and over 7X for liquid crystals on Stampede. The optimizations are available as part of the "Intel package" supplied with LAMMPS. (C) 2015 Elsevier B.V. All rights reserved.« less

  1. GLOBAL SOLUTIONS TO FOLDED CONCAVE PENALIZED NONCONVEX LEARNING

    PubMed Central

    Liu, Hongcheng; Yao, Tao; Li, Runze

    2015-01-01

    This paper is concerned with solving nonconvex learning problems with folded concave penalty. Despite that their global solutions entail desirable statistical properties, there lack optimization techniques that guarantee global optimality in a general setting. In this paper, we show that a class of nonconvex learning problems are equivalent to general quadratic programs. This equivalence facilitates us in developing mixed integer linear programming reformulations, which admit finite algorithms that find a provably global optimal solution. We refer to this reformulation-based technique as the mixed integer programming-based global optimization (MIPGO). To our knowledge, this is the first global optimization scheme with a theoretical guarantee for folded concave penalized nonconvex learning with the SCAD penalty (Fan and Li, 2001) and the MCP penalty (Zhang, 2010). Numerical results indicate a significant outperformance of MIPGO over the state-of-the-art solution scheme, local linear approximation, and other alternative solution techniques in literature in terms of solution quality. PMID:27141126

  2. A hybrid Jaya algorithm for reliability-redundancy allocation problems

    NASA Astrophysics Data System (ADS)

    Ghavidel, Sahand; Azizivahed, Ali; Li, Li

    2018-04-01

    This article proposes an efficient improved hybrid Jaya algorithm based on time-varying acceleration coefficients (TVACs) and the learning phase introduced in teaching-learning-based optimization (TLBO), named the LJaya-TVAC algorithm, for solving various types of nonlinear mixed-integer reliability-redundancy allocation problems (RRAPs) and standard real-parameter test functions. RRAPs include series, series-parallel, complex (bridge) and overspeed protection systems. The search power of the proposed LJaya-TVAC algorithm for finding the optimal solutions is first tested on the standard real-parameter unimodal and multi-modal functions with dimensions of 30-100, and then tested on various types of nonlinear mixed-integer RRAPs. The results are compared with the original Jaya algorithm and the best results reported in the recent literature. The optimal results obtained with the proposed LJaya-TVAC algorithm provide evidence for its better and acceptable optimization performance compared to the original Jaya algorithm and other reported optimal results.

  3. Optimization of fuel-cell tram operation based on two dimension dynamic programming

    NASA Astrophysics Data System (ADS)

    Zhang, Wenbin; Lu, Xuecheng; Zhao, Jingsong; Li, Jianqiu

    2018-02-01

    This paper proposes an optimal control strategy based on the two-dimension dynamic programming (2DDP) algorithm targeting at minimizing operation energy consumption for a fuel-cell tram. The energy consumption model with the tram dynamics is firstly deduced. Optimal control problem are analyzed and the 2DDP strategy is applied to solve the problem. The optimal tram speed profiles are obtained for each interstation which consist of three stages: accelerate to the set speed with the maximum traction power, dynamically adjust to maintain a uniform speed and decelerate to zero speed with the maximum braking power at a suitable timing. The optimal control curves of all the interstations are connected with the parking time to form the optimal control method of the whole line. The optimized speed profiles are also simplified for drivers to follow.

  4. On the preventive management of sediment-related sewer blockages: a combined maintenance and routing optimization approach.

    PubMed

    Fontecha, John E; Akhavan-Tabatabaei, Raha; Duque, Daniel; Medaglia, Andrés L; Torres, María N; Rodríguez, Juan Pablo

    In this work we tackle the problem of planning and scheduling preventive maintenance (PM) of sediment-related sewer blockages in a set of geographically distributed sites that are subject to non-deterministic failures. To solve the problem, we extend a combined maintenance and routing (CMR) optimization approach which is a procedure based on two components: (a) first a maintenance model is used to determine the optimal time to perform PM operations for each site and second (b) a mixed integer program-based split procedure is proposed to route a set of crews (e.g., sewer cleaners, vehicles equipped with winches or rods and dump trucks) in order to perform PM operations at a near-optimal minimum expected cost. We applied the proposed CMR optimization approach to two (out of five) operative zones in the city of Bogotá (Colombia), where more than 100 maintenance operations per zone must be scheduled on a weekly basis. Comparing the CMR against the current maintenance plan, we obtained more than 50% of cost savings in 90% of the sites.

  5. Poster — Thur Eve — 69: Computational Study of DVH-guided Cancer Treatment Planning Optimization Methods

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

    Ghomi, Pooyan Shirvani; Zinchenko, Yuriy

    2014-08-15

    Purpose: To compare methods to incorporate the Dose Volume Histogram (DVH) curves into the treatment planning optimization. Method: The performance of three methods, namely, the conventional Mixed Integer Programming (MIP) model, a convex moment-based constrained optimization approach, and an unconstrained convex moment-based penalty approach, is compared using anonymized data of a prostate cancer patient. Three plans we generated using the corresponding optimization models. Four Organs at Risk (OARs) and one Tumor were involved in the treatment planning. The OARs and Tumor were discretized into total of 50,221 voxels. The number of beamlets was 943. We used commercially available optimization softwaremore » Gurobi and Matlab to solve the models. Plan comparison was done by recording the model runtime followed by visual inspection of the resulting dose volume histograms. Conclusion: We demonstrate the effectiveness of the moment-based approaches to replicate the set of prescribed DVH curves. The unconstrained convex moment-based penalty approach is concluded to have the greatest potential to reduce the computational effort and holds a promise of substantial computational speed up.« less

  6. 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(*) optimal vaccination policy when vaccine supply is limited and (b) a cost-benefit scenario. The class of epidemic models for which this method can be used is described and we present an example formulation for which the resulting problem is a mixed-integer program. A short numerical example based on plausible parameter values and distributions is given to illustrate how including parameter uncertainty improves the robustness of the optimal strategy at the cost of higher coverage of the population. Results derived from a stochastic programming analysis can also help to guide decisions about how much effort and resources to focus on collecting data needed to provide better estimates of key parameters.

  7. Dynamic optimization and its relation to classical and quantum constrained systems

    NASA Astrophysics Data System (ADS)

    Contreras, Mauricio; Pellicer, Rely; Villena, Marcelo

    2017-08-01

    We study the structure of a simple dynamic optimization problem consisting of one state and one control variable, from a physicist's point of view. By using an analogy to a physical model, we study this system in the classical and quantum frameworks. Classically, the dynamic optimization problem is equivalent to a classical mechanics constrained system, so we must use the Dirac method to analyze it in a correct way. We find that there are two second-class constraints in the model: one fix the momenta associated with the control variables, and the other is a reminder of the optimal control law. The dynamic evolution of this constrained system is given by the Dirac's bracket of the canonical variables with the Hamiltonian. This dynamic results to be identical to the unconstrained one given by the Pontryagin equations, which are the correct classical equations of motion for our physical optimization problem. In the same Pontryagin scheme, by imposing a closed-loop λ-strategy, the optimality condition for the action gives a consistency relation, which is associated to the Hamilton-Jacobi-Bellman equation of the dynamic programming method. A similar result is achieved by quantizing the classical model. By setting the wave function Ψ(x , t) =e iS(x , t) in the quantum Schrödinger equation, a non-linear partial equation is obtained for the S function. For the right-hand side quantization, this is the Hamilton-Jacobi-Bellman equation, when S(x , t) is identified with the optimal value function. Thus, the Hamilton-Jacobi-Bellman equation in Bellman's maximum principle, can be interpreted as the quantum approach of the optimization problem.

  8. Dynamic modeling and optimization for space logistics using time-expanded networks

    NASA Astrophysics Data System (ADS)

    Ho, Koki; de Weck, Olivier L.; Hoffman, Jeffrey A.; Shishko, Robert

    2014-12-01

    This research develops a dynamic logistics network formulation for lifecycle optimization of mission sequences as a system-level integrated method to find an optimal combination of technologies to be used at each stage of the campaign. This formulation can find the optimal transportation architecture considering its technology trades over time. The proposed methodologies are inspired by the ground logistics analysis techniques based on linear programming network optimization. Particularly, the time-expanded network and its extension are developed for dynamic space logistics network optimization trading the quality of the solution with the computational load. In this paper, the methodologies are applied to a human Mars exploration architecture design problem. The results reveal multiple dynamic system-level trades over time and give recommendation of the optimal strategy for the human Mars exploration architecture. The considered trades include those between In-Situ Resource Utilization (ISRU) and propulsion technologies as well as the orbit and depot location selections over time. This research serves as a precursor for eventual permanent settlement and colonization of other planets by humans and us becoming a multi-planet species.

  9. Optimal synthesis and design of the number of cycles in the leaching process for surimi production.

    PubMed

    Reinheimer, M Agustina; Scenna, Nicolás J; Mussati, Sergio F

    2016-12-01

    Water consumption required during the leaching stage in the surimi manufacturing process strongly depends on the design and the number and size of stages connected in series for the soluble protein extraction target, and it is considered as the main contributor to the operating costs. Therefore, the optimal synthesis and design of the leaching stage is essential to minimize the total annual cost. In this study, a mathematical optimization model for the optimal design of the leaching operation is presented. Precisely, a detailed Mixed Integer Nonlinear Programming (MINLP) model including operating and geometric constraints was developed based on our previous optimization model (NLP model). Aspects about quality, water consumption and main operating parameters were considered. The minimization of total annual costs, which considered a trade-off between investment and operating costs, led to an optimal solution with lesser number of stages (2 instead of 3 stages) and higher volumes of the leaching tanks comparing with previous results. An analysis was performed in order to investigate how the optimal solution was influenced by the variations of the unitary cost of fresh water, waste treatment and capital investment.

  10. Selecting Tools to Model Integer and Binomial Multiplication

    ERIC Educational Resources Information Center

    Pratt, Sarah Smitherman; Eddy, Colleen M.

    2017-01-01

    Mathematics teachers frequently provide concrete manipulatives to students during instruction; however, the rationale for using certain manipulatives in conjunction with concepts may not be explored. This article focuses on area models that are currently used in classrooms to provide concrete examples of integer and binomial multiplication. The…

  11. Dynamic ADMM for Real-Time Optimal Power Flow

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

    Dall-Anese, Emiliano; Zhang, Yijian; Hong, Mingyi

    This paper considers distribution networks featuring distributed energy resources (DERs), and develops a dynamic optimization method to maximize given operational objectives in real time while adhering to relevant network constraints. The design of the dynamic algorithm is based on suitable linearization of the AC power flow equations, and it leverages the so-called alternating direction method of multipliers (ADMM). The steps of the ADMM, however, are suitably modified to accommodate appropriate measurements from the distribution network and the DERs. With the aid of these measurements, the resultant algorithm can enforce given operational constraints in spite of inaccuracies in the representation ofmore » the AC power flows, and it avoids ubiquitous metering to gather the state of noncontrollable resources. Optimality and convergence of the proposed algorithm are established in terms of tracking of the solution of a convex surrogate of the AC optimal power flow problem.« less

  12. 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.

  13. A new VLSI complex integer multiplier which uses a quadratic-polynomial residue system with Fermat numbers

    NASA Technical Reports Server (NTRS)

    Truong, T. K.; Hsu, I. S.; Chang, J. J.; Shyu, H. C.; Reed, I. S.

    1986-01-01

    A quadratic-polynomial Fermat residue number system (QFNS) has been used to compute complex integer multiplications. The advantage of such a QFNS is that a complex integer multiplication requires only two integer multiplications. In this article, a new type Fermat number multiplier is developed which eliminates the initialization condition of the previous method. It is shown that the new complex multiplier can be implemented on a single VLSI chip. Such a chip is designed and fabricated in CMOS-pw technology.

  14. A new VLSI complex integer multiplier which uses a quadratic-polynomial residue system with Fermat numbers

    NASA Technical Reports Server (NTRS)

    Shyu, H. C.; Reed, I. S.; Truong, T. K.; Hsu, I. S.; Chang, J. J.

    1987-01-01

    A quadratic-polynomial Fermat residue number system (QFNS) has been used to compute complex integer multiplications. The advantage of such a QFNS is that a complex integer multiplication requires only two integer multiplications. In this article, a new type Fermat number multiplier is developed which eliminates the initialization condition of the previous method. It is shown that the new complex multiplier can be implemented on a single VLSI chip. Such a chip is designed and fabricated in CMOS-Pw technology.

  15. C-learning: A new classification framework to estimate optimal dynamic treatment regimes.

    PubMed

    Zhang, Baqun; Zhang, Min

    2017-12-11

    A dynamic treatment regime is a sequence of decision rules, each corresponding to a decision point, that determine that next treatment based on each individual's own available characteristics and treatment history up to that point. We show that identifying the optimal dynamic treatment regime can be recast as a sequential optimization problem and propose a direct sequential optimization method to estimate the optimal treatment regimes. In particular, at each decision point, the optimization is equivalent to sequentially minimizing a weighted expected misclassification error. Based on this classification perspective, we propose a powerful and flexible C-learning algorithm to learn the optimal dynamic treatment regimes backward sequentially from the last stage until the first stage. C-learning is a direct optimization method that directly targets optimizing decision rules by exploiting powerful optimization/classification techniques and it allows incorporation of patient's characteristics and treatment history to improve performance, hence enjoying advantages of both the traditional outcome regression-based methods (Q- and A-learning) and the more recent direct optimization methods. The superior performance and flexibility of the proposed methods are illustrated through extensive simulation studies. © 2017, The International Biometric Society.

  16. Direct Optimal Control of Duffing Dynamics

    NASA Technical Reports Server (NTRS)

    Oz, Hayrani; Ramsey, John K.

    2002-01-01

    The "direct control method" is a novel concept that is an attractive alternative and competitor to the differential-equation-based methods. The direct method is equally well applicable to nonlinear, linear, time-varying, and time-invariant systems. For all such systems, the method yields explicit closed-form control laws based on minimization of a quadratic control performance measure. We present an application of the direct method to the dynamics and optimal control of the Duffing system where the control performance measure is not restricted to a quadratic form and hence may include a quartic energy term. The results we present in this report also constitute further generalizations of our earlier work in "direct optimal control methodology." The approach is demonstrated for the optimal control of the Duffing equation with a softening nonlinear stiffness.

  17. An inverse dynamics approach to trajectory optimization and guidance for an aerospace plane

    NASA Technical Reports Server (NTRS)

    Lu, Ping

    1992-01-01

    The optimal ascent problem for an aerospace planes is formulated as an optimal inverse dynamic problem. Both minimum-fuel and minimax type of performance indices are considered. Some important features of the optimal trajectory and controls are used to construct a nonlinear feedback midcourse controller, which not only greatly simplifies the difficult constrained optimization problem and yields improved solutions, but is also suited for onboard implementation. Robust ascent guidance is obtained by using combination of feedback compensation and onboard generation of control through the inverse dynamics approach. Accurate orbital insertion can be achieved with near-optimal control of the rocket through inverse dynamics even in the presence of disturbances.

  18. Secure Hashing of Dynamic Hand Signatures Using Wavelet-Fourier Compression with BioPhasor Mixing and [InlineEquation not available: see fulltext.] Discretization

    NASA Astrophysics Data System (ADS)

    Wai Kuan, Yip; Teoh, Andrew B. J.; Ngo, David C. L.

    2006-12-01

    We introduce a novel method for secure computation of biometric hash on dynamic hand signatures using BioPhasor mixing and[InlineEquation not available: see fulltext.] discretization. The use of BioPhasor as the mixing process provides a one-way transformation that precludes exact recovery of the biometric vector from compromised hashes and stolen tokens. In addition, our user-specific[InlineEquation not available: see fulltext.] discretization acts both as an error correction step as well as a real-to-binary space converter. We also propose a new method of extracting compressed representation of dynamic hand signatures using discrete wavelet transform (DWT) and discrete fourier transform (DFT). Without the conventional use of dynamic time warping, the proposed method avoids storage of user's hand signature template. This is an important consideration for protecting the privacy of the biometric owner. Our results show that the proposed method could produce stable and distinguishable bit strings with equal error rates (EERs) of[InlineEquation not available: see fulltext.] and[InlineEquation not available: see fulltext.] for random and skilled forgeries for stolen token (worst case) scenario, and[InlineEquation not available: see fulltext.] for both forgeries in the genuine token (optimal) scenario.

  19. Pinning mode of integer quantum Hall Wigner crystal of skyrmions

    NASA Astrophysics Data System (ADS)

    Zhu, Han; Sambandamurthy, G.; Chen, Y. P.; Jiang, P.-H.; Engel, L. W.; Tsui, D. C.; Pfeiffer, L. N.; West, K. W.

    2009-03-01

    Just away from integer Landau level (LL) filling factors ν, the dilute quasi-particles/holes at the partially filled LL form an integer-quantum-Hall Wigner crystal, which exhibits microwave pinning mode resonances [1]. Due to electron-electron interaction, it was predicted that the elementary excitation around ν= 1 is not a single spin flip, but a larger-scale spin texture, known as a skyrmion [2]. We have compared the pinning mode resonances [1] of integer quantum Hall Wigner crystals formed in the partly filled LL just away from ν= 1 and ν= 2, in the presence of an in-plane magnetic field. As an in-plane field is applied, the peak frequencies of the resonances near ν= 1 increase, while the peak frequencies below ν= 2 show neligible dependence on in-plane field. We interpret this observation as due to a skyrmion crystal phase around ν= 1 and a single-hole Wigner crystal phase below ν= 2. The in-plane field increases the Zeeman gap and causes shrinking of the skyrmion size toward single spin flips. [1] Yong P. Chen et al., Phys. Rev. Lett. 91, 016801 (2003). [2] S. L. Sondhi et al., Phys. Rev. B 47, 16 419 (1993); L. Brey et al., Phys. Rev. Lett. 75, 2562 (1995).

  20. Computer Corner: Spreadsheets, Power Series, Generating Functions, and Integers.

    ERIC Educational Resources Information Center

    Snow, Donald R.

    1989-01-01

    Implements a table algorithm on a spreadsheet program and obtains functions for several number sequences such as the Fibonacci and Catalan numbers. Considers other applications of the table algorithm to integers represented in various number bases. (YP)

  1. Discrete Optimization of Electronic Hyperpolarizabilities in a Chemical Subspace

    DTIC Science & Technology

    2009-05-01

    molecular design. Methods for optimization in discrete spaces have been studied extensively and recently reviewed ( 5). Optimization methods include...integer programming, as in branch-and-bound techniques (including dead-end elimination [ 6]), simulated annealing ( 7), and genetic algorithms ( 8...These algorithms have found renewed interest and application in molecular and materials design (9- 12) . Recently, new approaches have been

  2. Fast engineering optimization: A novel highly effective control parameterization approach for industrial dynamic processes.

    PubMed

    Liu, Ping; Li, Guodong; Liu, Xinggao

    2015-09-01

    Control vector parameterization (CVP) is an important approach of the engineering optimization for the industrial dynamic processes. However, its major defect, the low optimization efficiency caused by calculating the relevant differential equations in the generated nonlinear programming (NLP) problem repeatedly, limits its wide application in the engineering optimization for the industrial dynamic processes. A novel highly effective control parameterization approach, fast-CVP, is first proposed to improve the optimization efficiency for industrial dynamic processes, where the costate gradient formulae is employed and a fast approximate scheme is presented to solve the differential equations in dynamic process simulation. Three well-known engineering optimization benchmark problems of the industrial dynamic processes are demonstrated as illustration. The research results show that the proposed fast approach achieves a fine performance that at least 90% of the computation time can be saved in contrast to the traditional CVP method, which reveals the effectiveness of the proposed fast engineering optimization approach for the industrial dynamic processes. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  3. Optimal Strategy for Integrated Dynamic Inventory Control and Supplier Selection in Unknown Environment via Stochastic Dynamic Programming

    NASA Astrophysics Data System (ADS)

    Sutrisno; Widowati; Solikhin

    2016-06-01

    In this paper, we propose a mathematical model in stochastic dynamic optimization form to determine the optimal strategy for an integrated single product inventory control problem and supplier selection problem where the demand and purchasing cost parameters are random. For each time period, by using the proposed model, we decide the optimal supplier and calculate the optimal product volume purchased from the optimal supplier so that the inventory level will be located at some point as close as possible to the reference point with minimal cost. We use stochastic dynamic programming to solve this problem and give several numerical experiments to evaluate the model. From the results, for each time period, the proposed model was generated the optimal supplier and the inventory level was tracked the reference point well.

  4. Optimization of municipal solid waste collection and transportation routes

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

    Das, Swapan, E-mail: swapan2009sajal@gmail.com; Bhattacharyya, Bidyut Kr., E-mail: bidyut53@yahoo.co.in

    2015-09-15

    Graphical abstract: Display Omitted - Highlights: • Profitable integrated solid waste management system. • Optimal municipal waste collection scheme between the sources and waste collection centres. • Optimal path calculation between waste collection centres and transfer stations. • Optimal waste routing between the transfer stations and processing plants. - Abstract: Optimization of municipal solid waste (MSW) collection and transportation through source separation becomes one of the major concerns in the MSW management system design, due to the fact that the existing MSW management systems suffer by the high collection and transportation cost. Generally, in a city different waste sources scattermore » throughout the city in heterogeneous way that increase waste collection and transportation cost in the waste management system. Therefore, a shortest waste collection and transportation strategy can effectively reduce waste collection and transportation cost. In this paper, we propose an optimal MSW collection and transportation scheme that focus on the problem of minimizing the length of each waste collection and transportation route. We first formulize the MSW collection and transportation problem into a mixed integer program. Moreover, we propose a heuristic solution for the waste collection and transportation problem that can provide an optimal way for waste collection and transportation. Extensive simulations and real testbed results show that the proposed solution can significantly improve the MSW performance. Results show that the proposed scheme is able to reduce more than 30% of the total waste collection path length.« less

  5. Fast parallel DNA-based algorithms for molecular computation: quadratic congruence and factoring integers.

    PubMed

    Chang, Weng-Long

    2012-03-01

    Assume that n is a positive integer. If there is an integer such that M (2) ≡ C (mod n), i.e., the congruence has a solution, then C is said to be a quadratic congruence (mod n). If the congruence does not have a solution, then C is said to be a quadratic noncongruence (mod n). The task of solving the problem is central to many important applications, the most obvious being cryptography. In this article, we describe a DNA-based algorithm for solving quadratic congruence and factoring integers. In additional to this novel contribution, we also show the utility of our encoding scheme, and of the algorithm's submodules. We demonstrate how a variety of arithmetic, shifted and comparative operations, namely bitwise and full addition, subtraction, left shifter and comparison perhaps are performed using strands of DNA.

  6. Modeling Ullage Dynamics of Tank Pressure Control Experiment during Jet Mixing in Microgravity

    NASA Technical Reports Server (NTRS)

    Kartuzova, O.; Kassemi, M.

    2016-01-01

    A CFD model for simulating the fluid dynamics of the jet induced mixing process is utilized in this paper to model the pressure control portion of the Tank Pressure Control Experiment (TPCE) in microgravity1. The Volume of Fluid (VOF) method is used for modeling the dynamics of the interface during mixing. The simulations were performed at a range of jet Weber numbers from non-penetrating to fully penetrating. Two different initial ullage positions were considered. The computational results for the jet-ullage interaction are compared with still images from the video of the experiment. A qualitative comparison shows that the CFD model was able to capture the main features of the interfacial dynamics, as well as the jet penetration of the ullage.

  7. Sub-optimal control of fuzzy linear dynamical systems under granular differentiability concept.

    PubMed

    Mazandarani, Mehran; Pariz, Naser

    2018-05-01

    This paper deals with sub-optimal control of a fuzzy linear dynamical system. The aim is to keep the state variables of the fuzzy linear dynamical system close to zero in an optimal manner. In the fuzzy dynamical system, the fuzzy derivative is considered as the granular derivative; and all the coefficients and initial conditions can be uncertain. The criterion for assessing the optimality is regarded as a granular integral whose integrand is a quadratic function of the state variables and control inputs. Using the relative-distance-measure (RDM) fuzzy interval arithmetic and calculus of variations, the optimal control law is presented as the fuzzy state variables feedback. Since the optimal feedback gains are obtained as fuzzy functions, they need to be defuzzified. This will result in the sub-optimal control law. This paper also sheds light on the restrictions imposed by the approaches which are based on fuzzy standard interval arithmetic (FSIA), and use strongly generalized Hukuhara and generalized Hukuhara differentiability concepts for obtaining the optimal control law. The granular eigenvalues notion is also defined. Using an RLC circuit mathematical model, it is shown that, due to their unnatural behavior in the modeling phenomenon, the FSIA-based approaches may obtain some eigenvalues sets that might be different from the inherent eigenvalues set of the fuzzy dynamical system. This is, however, not the case with the approach proposed in this study. The notions of granular controllability and granular stabilizability of the fuzzy linear dynamical system are also presented in this paper. Moreover, a sub-optimal control for regulating a Boeing 747 in longitudinal direction with uncertain initial conditions and parameters is gained. In addition, an uncertain suspension system of one of the four wheels of a bus is regulated using the sub-optimal control introduced in this paper. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

  8. Solving large-scale fixed cost integer linear programming models for grid-based location problems with heuristic techniques

    NASA Astrophysics Data System (ADS)

    Noor-E-Alam, Md.; Doucette, John

    2015-08-01

    Grid-based location problems (GBLPs) can be used to solve location problems in business, engineering, resource exploitation, and even in the field of medical sciences. To solve these decision problems, an integer linear programming (ILP) model is designed and developed to provide the optimal solution for GBLPs considering fixed cost criteria. Preliminary results show that the ILP model is efficient in solving small to moderate-sized problems. However, this ILP model becomes intractable in solving large-scale instances. Therefore, a decomposition heuristic is proposed to solve these large-scale GBLPs, which demonstrates significant reduction of solution runtimes. To benchmark the proposed heuristic, results are compared with the exact solution via ILP. The experimental results show that the proposed method significantly outperforms the exact method in runtime with minimal (and in most cases, no) loss of optimality.

  9. Voronoi Diagram Based Optimization of Dynamic Reactive Power Sources

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

    Huang, Weihong; Sun, Kai; Qi, Junjian

    2015-01-01

    Dynamic var sources can effectively mitigate fault-induced delayed voltage recovery (FIDVR) issues or even voltage collapse. This paper proposes a new approach to optimization of the sizes of dynamic var sources at candidate locations by a Voronoi diagram based algorithm. It first disperses sample points of potential solutions in a searching space, evaluates a cost function at each point by barycentric interpolation for the subspaces around the point, and then constructs a Voronoi diagram about cost function values over the entire space. Accordingly, the final optimal solution can be obtained. Case studies on the WSCC 9-bus system and NPCC 140-busmore » system have validated that the new approach can quickly identify the boundary of feasible solutions in searching space and converge to the global optimal solution.« less

  10. Optimization and physicochemical characterization of a cationic lipid-phosphatidylcholine mixed emulsion formulated as a highly efficient vehicle that facilitates adenoviral gene transfer.

    PubMed

    Kim, Soo-Yeon; Lee, Sang-Jin; Kim, Jin-Ki; Choi, Han-Gon; Lim, Soo-Jeong

    2017-01-01

    Cationic lipid-based nanoparticles enhance viral gene transfer by forming electrostatic complexes with adenoviral vectors. We recently demonstrated the superior complexation capabilities of 1,2-dioleoyl-3-trimethylammonium propane (DOTAP) emulsion compared with a liposomal counterpart but the cytotoxicity of DOTAP emulsions remained a challenge. The present study is aimed at formulating an emulsion capable of acting as a highly effective viral gene transfer vehicle with reduced cytotoxicity and to physicochemically characterize the structures of virus-emulsion complexes in comparison with virus-liposome complexes when the only difference between emulsions and liposomes was the presence or absence of inner oil core. The emulsion formulation was performed by 1) reducing the content of DOTAP while increasing the content of zwitterionic lipid 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC), and 2) optimizing the oil content. The complexation capability of formulated DOTAP:DMPC mixed emulsions was similar to those of emulsions containing DOTAP alone while displaying significantly lower cytotoxicity. The complexation capabilities of the DOTAP:DMPC mixed emulsion were serum-compatible and were monitored in a variety of cell types, whereas its liposomal counterpart was totally ineffective. Characterization by scanning electron microscopy, transmission electron microscopy, atomic force microscopy, and dynamic light scattering studies indicated that the optimized emulsions spontaneously surrounded the virus particles to generate emulsions that encapsulated the viral particles, whereas viral particles merely attached to the surfaces of the counterpart liposomes to form multiviral aggregates. Overall, these studies demonstrated that optimized DOTAP:DMPC mixed emulsions are potentially useful for adenoviral gene delivery due to less cytotoxicity and the unique ability to encapsulate the viral particle, highlighting the importance of nanoparticle formulation.

  11. Optimization and physicochemical characterization of a cationic lipid-phosphatidylcholine mixed emulsion formulated as a highly efficient vehicle that facilitates adenoviral gene transfer

    PubMed Central

    Kim, Soo-Yeon; Lee, Sang-Jin; Kim, Jin-Ki; Choi, Han-Gon; Lim, Soo-Jeong

    2017-01-01

    Cationic lipid-based nanoparticles enhance viral gene transfer by forming electrostatic complexes with adenoviral vectors. We recently demonstrated the superior complexation capabilities of 1,2-dioleoyl-3-trimethylammonium propane (DOTAP) emulsion compared with a liposomal counterpart but the cytotoxicity of DOTAP emulsions remained a challenge. The present study is aimed at formulating an emulsion capable of acting as a highly effective viral gene transfer vehicle with reduced cytotoxicity and to physicochemically characterize the structures of virus-emulsion complexes in comparison with virus–liposome complexes when the only difference between emulsions and liposomes was the presence or absence of inner oil core. The emulsion formulation was performed by 1) reducing the content of DOTAP while increasing the content of zwitterionic lipid 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC), and 2) optimizing the oil content. The complexation capability of formulated DOTAP:DMPC mixed emulsions was similar to those of emulsions containing DOTAP alone while displaying significantly lower cytotoxicity. The complexation capabilities of the DOTAP:DMPC mixed emulsion were serum-compatible and were monitored in a variety of cell types, whereas its liposomal counterpart was totally ineffective. Characterization by scanning electron microscopy, transmission electron microscopy, atomic force microscopy, and dynamic light scattering studies indicated that the optimized emulsions spontaneously surrounded the virus particles to generate emulsions that encapsulated the viral particles, whereas viral particles merely attached to the surfaces of the counterpart liposomes to form multiviral aggregates. Overall, these studies demonstrated that optimized DOTAP:DMPC mixed emulsions are potentially useful for adenoviral gene delivery due to less cytotoxicity and the unique ability to encapsulate the viral particle, highlighting the importance of nanoparticle formulation. PMID:29070949

  12. A CU-Level Rate and Distortion Estimation Scheme for RDO of Hardware-Friendly HEVC Encoders Using Low-Complexity Integer DCTs.

    PubMed

    Lee, Bumshik; Kim, Munchurl

    2016-08-01

    In this paper, a low complexity coding unit (CU)-level rate and distortion estimation scheme is proposed for High Efficiency Video Coding (HEVC) hardware-friendly implementation where a Walsh-Hadamard transform (WHT)-based low-complexity integer discrete cosine transform (DCT) is employed for distortion estimation. Since HEVC adopts quadtree structures of coding blocks with hierarchical coding depths, it becomes more difficult to estimate accurate rate and distortion values without actually performing transform, quantization, inverse transform, de-quantization, and entropy coding. Furthermore, DCT for rate-distortion optimization (RDO) is computationally high, because it requires a number of multiplication and addition operations for various transform block sizes of 4-, 8-, 16-, and 32-orders and requires recursive computations to decide the optimal depths of CU or transform unit. Therefore, full RDO-based encoding is highly complex, especially for low-power implementation of HEVC encoders. In this paper, a rate and distortion estimation scheme is proposed in CU levels based on a low-complexity integer DCT that can be computed in terms of WHT whose coefficients are produced in prediction stages. For rate and distortion estimation in CU levels, two orthogonal matrices of 4×4 and 8×8 , which are applied to WHT that are newly designed in a butterfly structure only with addition and shift operations. By applying the integer DCT based on the WHT and newly designed transforms in each CU block, the texture rate can precisely be estimated after quantization using the number of non-zero quantized coefficients and the distortion can also be precisely estimated in transform domain without de-quantization and inverse transform required. In addition, a non-texture rate estimation is proposed by using a pseudoentropy code to obtain accurate total rate estimates. The proposed rate and the distortion estimation scheme can effectively be used for HW-friendly implementation of

  13. Design optimization of aircraft landing gear assembly under dynamic loading

    NASA Astrophysics Data System (ADS)

    Wong, Jonathan Y. B.

    As development cycles and prototyping iterations begin to decrease in the aerospace industry, it is important to develop and improve practical methodologies to meet all design metrics. This research presents an efficient methodology that applies high-fidelity multi-disciplinary design optimization techniques to commercial landing gear assemblies, for weight reduction, cost savings, and structural performance dynamic loading. Specifically, a slave link subassembly was selected as the candidate to explore the feasibility of this methodology. The design optimization process utilized in this research was sectioned into three main stages: setup, optimization, and redesign. The first stage involved the creation and characterization of the models used throughout this research. The slave link assembly was modelled with a simplified landing gear test, replicating the behavior of the physical system. Through extensive review of the literature and collaboration with Safran Landing Systems, dynamic and structural behavior for the system were characterized and defined mathematically. Once defined, the characterized behaviors for the slave link assembly were then used to conduct a Multi-Body Dynamic (MBD) analysis to determine the dynamic and structural response of the system. These responses were then utilized in a topology optimization through the use of the Equivalent Static Load Method (ESLM). The results of the optimization were interpreted and later used to generate improved designs in terms of weight, cost, and structural performance under dynamic loading in stage three. The optimized designs were then validated using the model created for the MBD analysis of the baseline design. The design generation process employed two different approaches for post-processing the topology results produced. The first approach implemented a close replication of the topology results, resulting in a design with an overall peak stress increase of 74%, weight savings of 67%, and no apparent

  14. An optimal renewable energy mix for Indonesia

    NASA Astrophysics Data System (ADS)

    Leduc, Sylvain; Patrizio, Piera; Yowargana, Ping; Kraxner, Florian

    2016-04-01

    Indonesia has experienced a constant increase of the use of petroleum and coal in the power sector, while the share of renewable sources has remained stable at 6% of the total energy production during the last decade. As its domestic energy demand undeniably continues to grow, Indonesia is committed to increase the production of renewable energy. Mainly to decrease its dependency on fossil fuel-based resources, and to decrease the anthropogenic emissions, the government of Indonesia has established a 23 percent target for renewable energy by 2025, along with a 100 percent electrification target by 2020 (the current rate is 80.4 percent). In that respect, Indonesia has abundant resources to meet these targets, but there is - inter alia - a lack of proper integrated planning, regulatory support, investment, distribution in remote areas of the Archipelago, and missing data to back the planning. To support the government of Indonesia in its sustainable energy system planning, a geographic explicit energy modeling approach is applied. This approach is based on the energy systems optimization model BeWhere, which identifies the optimal location of energy conversion sites based on the minimization of the costs of the supply chain. The model will incorporate the existing fossil fuel-based infrastructures, and evaluate the optimal costs, potentials and locations for the development of renewable energy technologies (i.e., wind, solar, hydro, biomass and geothermal based technologies), as well as the development of biomass co-firing in existing coal plants. With the help of the model, an optimally adapted renewable energy mix - vis-à-vis the competing fossil fuel based resources and applicable policies in order to promote the development of those renewable energy technologies - will be identified. The development of the optimal renewable energy technologies is carried out with special focus on nature protection and cultural heritage areas, where feedstock (e.g., biomass

  15. Study on system dynamics of evolutionary mix-game models

    NASA Astrophysics Data System (ADS)

    Gou, Chengling; Guo, Xiaoqian; Chen, Fang

    2008-11-01

    Mix-game model is ameliorated from an agent-based MG model, which is used to simulate the real financial market. Different from MG, there are two groups of agents in Mix-game: Group 1 plays a majority game and Group 2 plays a minority game. These two groups of agents have different bounded abilities to deal with historical information and to count their own performance. In this paper, we modify Mix-game model by assigning the evolution abilities to agents: if the winning rates of agents are smaller than a threshold, they will copy the best strategies the other agent has; and agents will repeat such evolution at certain time intervals. Through simulations this paper finds: (1) the average winning rates of agents in Group 1 and the mean volatilities increase with the increases of the thresholds of Group 1; (2) the average winning rates of both groups decrease but the mean volatilities of system increase with the increase of the thresholds of Group 2; (3) the thresholds of Group 2 have greater impact on system dynamics than the thresholds of Group 1; (4) the characteristics of system dynamics under different time intervals of strategy change are similar to each other qualitatively, but they are different quantitatively; (5) As the time interval of strategy change increases from 1 to 20, the system behaves more and more stable and the performances of agents in both groups become better also.

  16. Experiments on the enhancement of compressible mixing via streamwise vorticity. II - Vortex strength assessment and seed particle dynamics

    NASA Technical Reports Server (NTRS)

    Naughton, J. W.; Cattafesta, L. N.; Settles, G. S.

    1993-01-01

    The effect of streamwise vorticity on compressible axisymmetric mixing layers is examined using vortex strength assessment and seed particle dynamics analysis. Experimental results indicate that the particles faithfully represent the dynamics of the turbulent swirling flow. A comparison of the previously determined mixing layer growth rates with the present vortex strength data reveals that the increase of turbulent mixing up to 60 percent scales with the degree of swirl. The mixing enhancement appears to be independent of the compressibility level of the mixing layer.

  17. Optimizing Motion Planning for Hyper Dynamic Manipulator

    NASA Astrophysics Data System (ADS)

    Aboura, Souhila; Omari, Abdelhafid; Meguenni, Kadda Zemalache

    2012-01-01

    This paper investigates the optimal motion planning for an hyper dynamic manipulator. As case study, we consider a golf swing robot which is consisting with two actuated joint and a mechanical stoppers. Genetic Algorithm (GA) technique is proposed to solve the optimal golf swing motion which is generated by Fourier series approximation. The objective function for GA approach is to minimizing the intermediate and final state, minimizing the robot's energy consummation and maximizing the robot's speed. Obtained simulation results show the effectiveness of the proposed scheme.

  18. Mixed Integer PDE Constrained Optimization for the Control of a Wildfire Hazard

    DTIC Science & Technology

    2017-01-01

    are nodes suitable for extinguishing the fire. We introduce a discretization of the time horizon [0, T] by the set of time T := {0, At,..., ntZ\\t = T...of the constraints and objective with a discrete counterpart. The PDE is replaced by a linear system obtained from a convergent finite difference...method [5] and the integral is replaced by a quadrature formula. The domain is discretized by replacing 17 with an equidistant grid of length Ax

  19. Numerical integration and optimization of motions for multibody dynamic systems

    NASA Astrophysics Data System (ADS)

    Aguilar Mayans, Joan

    This thesis considers the optimization and simulation of motions involving rigid body systems. It does so in three distinct parts, with the following topics: optimization and analysis of human high-diving motions, efficient numerical integration of rigid body dynamics with contacts, and motion optimization of a two-link robot arm using Finite-Time Lyapunov Analysis. The first part introduces the concept of eigenpostures, which we use to simulate and analyze human high-diving motions. Eigenpostures are used in two different ways: first, to reduce the complexity of the optimal control problem that we solve to obtain such motions, and second, to generate an eigenposture space to which we map existing real world motions to better analyze them. The benefits of using eigenpostures are showcased through different examples. The second part reviews an extensive list of integration algorithms used for the integration of rigid body dynamics. We analyze the accuracy and stability of the different integrators in the three-dimensional space and the rotation space SO(3). Integrators with an accuracy higher than first order perform more efficiently than integrators with first order accuracy, even in the presence of contacts. The third part uses Finite-time Lyapunov Analysis to optimize motions for a two-link robot arm. Finite-Time Lyapunov Analysis diagnoses the presence of time-scale separation in the dynamics of the optimized motion and provides the information and methodology for obtaining an accurate approximation to the optimal solution, avoiding the complications that timescale separation causes for alternative solution methods.

  20. Integrated optimization of planetary rover layout and exploration routes

    NASA Astrophysics Data System (ADS)

    Lee, Dongoo; Ahn, Jaemyung

    2018-01-01

    This article introduces an optimization framework for the integrated design of a planetary surface rover and its exploration route that is applicable to the initial phase of a planetary exploration campaign composed of multiple surface missions. The scientific capability and the mobility of a rover are modelled as functions of the science weight fraction, a key parameter characterizing the rover. The proposed problem is formulated as a mixed-integer nonlinear program that maximizes the sum of profits obtained through a planetary surface exploration mission by simultaneously determining the science weight fraction of the rover, the sites to visit and their visiting sequences under resource consumption constraints imposed on each route and collectively on a mission. A solution procedure for the proposed problem composed of two loops (the outer loop and the inner loop) is developed. The results of test cases demonstrating the effectiveness of the proposed framework are presented.

  1. Reversible integer wavelet transform for blind image hiding method

    PubMed Central

    Bibi, Nargis; Mahmood, Zahid; Akram, Tallha; Naqvi, Syed Rameez

    2017-01-01

    In this article, a blind data hiding reversible methodology to embed the secret data for hiding purpose into cover image is proposed. The key advantage of this research work is to resolve the privacy and secrecy issues raised during the data transmission over the internet. Firstly, data is decomposed into sub-bands using the integer wavelets. For decomposition, the Fresnelet transform is utilized which encrypts the secret data by choosing a unique key parameter to construct a dummy pattern. The dummy pattern is then embedded into an approximated sub-band of the cover image. Our proposed method reveals high-capacity and great imperceptibility of the secret embedded data. With the utilization of family of integer wavelets, the proposed novel approach becomes more efficient for hiding and retrieving process. It retrieved the secret hidden data from the embedded data blindly, without the requirement of original cover image. PMID:28498855

  2. Applying n-bit floating point numbers and integers, and the n-bit filter of HDF5 to reduce file sizes of remote sensing products in memory-sensitive environments

    NASA Astrophysics Data System (ADS)

    Zinke, Stephan

    2017-02-01

    Memory sensitive applications for remote sensing data require memory-optimized data types in remote sensing products. Hierarchical Data Format version 5 (HDF5) offers user defined floating point numbers and integers and the n-bit filter to create data types optimized for memory consumption. The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) applies a compaction scheme to the disseminated products of the Day and Night Band (DNB) data of Suomi National Polar-orbiting Partnership (S-NPP) satellite's instrument Visible Infrared Imager Radiometer Suite (VIIRS) through the EUMETSAT Advanced Retransmission Service, converting the original 32 bits floating point numbers to user defined floating point numbers in combination with the n-bit filter for the radiance dataset of the product. The radiance dataset requires a floating point representation due to the high dynamic range of the DNB. A compression factor of 1.96 is reached by using an automatically determined exponent size and an 8 bits trailing significand and thus reducing the bandwidth requirements for dissemination. It is shown how the parameters needed for user defined floating point numbers are derived or determined automatically based on the data present in a product.

  3. Informing Practice: Making Sense of Integers through Storytelling

    ERIC Educational Resources Information Center

    Wessman-Enzinger, Nicole M.; Mooney, Edward S.

    2014-01-01

    The authors asked fifth-grade and eighth-grade students to pose stories for number sentences involving the addition and subtraction of integers. In this article, the authors look at eight stories from students. Which of these stories works for the given number sentence? What do they reveal about student thinking? When the authors examined these…

  4. Optimization of dynamic soaring maneuvers to enhance endurance of a versatile UAV

    NASA Astrophysics Data System (ADS)

    Mir, Imran; Maqsood, Adnan; Akhtar, Suhail

    2017-06-01

    Dynamic soaring is a process of acquiring energy available in atmospheric wind shears and is commonly exhibited by soaring birds to perform long distance flights. This paper aims to demonstrate a viable algorithm which can be implemented in near real time environment to formulate optimal trajectories for dynamic soaring maneuvers for a small scale Unmanned Aerial Vehicle (UAV). The objective is to harness maximum energy from atmosphere wind shear to improve loiter time for Intelligence, Surveillance and Reconnaissance (ISR) missions. Three-dimensional point-mass UAV equations of motion and linear wind gradient profile are used to model flight dynamics. Utilizing UAV states, controls, operational constraints, initial and terminal conditions that enforce a periodic flight, dynamic soaring problem is formulated as an optimal control problem. Optimized trajectories of the maneuver are subsequently generated employing pseudo spectral techniques against distant UAV performance parameters. The discussion also encompasses the requirement for generation of optimal trajectories for dynamic soaring in real time environment and the ability of the proposed algorithm for speedy solution generation. Coupled with the fact that dynamic soaring is all about immediately utilizing the available energy from the wind shear encountered, the proposed algorithm promises its viability for practical on board implementations requiring computation of trajectories in near real time.

  5. Mixed valent metals

    NASA Astrophysics Data System (ADS)

    Riseborough, P. S.; Lawrence, J. M.

    2016-08-01

    We review the theory of mixed-valent metals and make comparison with experiments. A single-impurity description of the mixed-valent state is discussed alongside the description of the nearly-integer valent or Kondo limit. The degeneracy N of the f-shell plays an important role in the description of the low-temperature Fermi-liquid state. In particular, for large N, there is a rapid cross-over between the mixed-valent and the Kondo limit when the number of f electrons is changed. We discuss the limitations on the application of the single-impurity description to concentrated compounds such as those caused by the saturation of the Kondo effect and those due to the presence of magnetic interactions between the impurities. This discussion is followed by a description of a periodic lattice of mixed-valent ions, including the role of the degeneracy N. The article concludes with a comparison of theory and experiment. Topics covered include the single-impurity Anderson model, Luttinger’s theorem, the Friedel sum rule, the Schrieffer-Wolff transformation, the single-impurity Kondo model, Kondo screening, the Wilson ratio, local Fermi-liquids, Fermi-liquid sum rules, the Noziéres exhaustion principle, Doniach’s diagram, the Anderson lattice model, the Slave-Boson method, etc.

  6. Mixed valent metals

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

    Riseborough, P. S.; Lawrence, Jon M.

    Here, we review the theory of mixed-valent metals and make comparison with experiments. A single-impurity description of the mixed-valent state is discussed alongside the description of the nearly-integer valent or Kondo limit. The degeneracy N of the f-shell plays an important role in the description of the low-temperature Fermi-liquid state. In particular, for large N, there is a rapid cross-over between the mixed-valent and the Kondo limit when the number of f electrons is changed. We discuss the limitations on the application of the single-impurity description to concentrated compounds such as those caused by the saturation of the Kondo effectmore » and those due to the presence of magnetic interactions between the impurities. This discussion is followed by a description of a periodic lattice of mixed-valent ions, including the role of the degeneracy N. The article concludes with a comparison of theory and experiment. Topics covered include the single-impurity Anderson model, Luttinger's theorem, the Friedel sum rule, the Schrieffer–Wolff transformation, the single-impurity Kondo model, Kondo screening, the Wilson ratio, local Fermi-liquids, Fermi-liquid sum rules, the Nozieres exhaustion principle, Doniach's diagram, the Anderson lattice model, the Slave-Boson method, etc.« less

  7. Mixed valent metals

    DOE PAGES

    Riseborough, P. S.; Lawrence, Jon M.

    2016-07-04

    Here, we review the theory of mixed-valent metals and make comparison with experiments. A single-impurity description of the mixed-valent state is discussed alongside the description of the nearly-integer valent or Kondo limit. The degeneracy N of the f-shell plays an important role in the description of the low-temperature Fermi-liquid state. In particular, for large N, there is a rapid cross-over between the mixed-valent and the Kondo limit when the number of f electrons is changed. We discuss the limitations on the application of the single-impurity description to concentrated compounds such as those caused by the saturation of the Kondo effectmore » and those due to the presence of magnetic interactions between the impurities. This discussion is followed by a description of a periodic lattice of mixed-valent ions, including the role of the degeneracy N. The article concludes with a comparison of theory and experiment. Topics covered include the single-impurity Anderson model, Luttinger's theorem, the Friedel sum rule, the Schrieffer–Wolff transformation, the single-impurity Kondo model, Kondo screening, the Wilson ratio, local Fermi-liquids, Fermi-liquid sum rules, the Nozieres exhaustion principle, Doniach's diagram, the Anderson lattice model, the Slave-Boson method, etc.« less

  8. Finite-size scaling and integer-spin Heisenberg chains

    NASA Astrophysics Data System (ADS)

    Bonner, Jill C.; Müller, Gerhard

    1984-03-01

    Finite-size scaling (phenomenological renormalization) techniques are trusted and widely applied in low-dimensional magnetism and, particularly, in lattice gauge field theory. Recently, investigations have begun which subject the theoretical basis to systematic and intensive scrutiny to determine the validity of finite-size scaling in a variety of situations. The 2D ANNNI model is an example of a situation where finite-size scaling methods encounter difficulty, related to the occurrence of a disorder line (one-dimensional line). A second example concerns the behavior of the spin-1/2 antiferromagnetic XXZ model where the T=0 critical behavior is exactly known and features an essential singularity at the isotropic Heisenberg point. Standard finite-size scaling techniques do not convincingly reproduce the exact phase behavior and this is attributable to the essential singularity. The point is relevant in connection with a finite-size scaling analysis of a spin-one antiferromagnetic XXZ model, which claims to support a conjecture by Haldane that the T=0 phase behavior of integer-spin Heisenberg chains is significantly different from that of half-integer-spin Heisenberg chains.

  9. Vlasov Simulation of Mixing in Antihydrogen Formation

    NASA Astrophysics Data System (ADS)

    So, Chukman; Fajans, Joel; Friedland, Lazar; Wurtele, Jonathan; Alpha Collaboration

    2011-10-01

    In the ALPHA apparatus, low temperature antiprotons (p) and positrons (e+) are prepared adjacent to each other in a nested Penning trap. To create trappable antihydrogen (H), the two species must be mixed such that some resultant H atoms have sub-Kelvin kinetic energy. A new simulation has been developed to study and optimize the autoresonant mixing, in ALPHA. The p dynamics are governed by their own self- field, the e+ plasma field, and the external fields. The e+ 's are handled quasi-statically with a Poisson-Boltzmann solver. p 's are handled by multiple time dependent 1D Vlasov-Poisson solvers, each representing a radial slice of the plasma. The 1D simulatiuons couple through the 2D Poisson equation. We neglect radial transport due to the strong solenoidal field. The advantages and disadvantages of different descretization schemes, comparisons of simulation with experiment, and techniques for optimizing mixing, will be presented.

  10. Performance evaluation of the inverse dynamics method for optimal spacecraft reorientation

    NASA Astrophysics Data System (ADS)

    Ventura, Jacopo; Romano, Marcello; Walter, Ulrich

    2015-05-01

    This paper investigates the application of the inverse dynamics in the virtual domain method to Euler angles, quaternions, and modified Rodrigues parameters for rapid optimal attitude trajectory generation for spacecraft reorientation maneuvers. The impact of the virtual domain and attitude representation is numerically investigated for both minimum time and minimum energy problems. Owing to the nature of the inverse dynamics method, it yields sub-optimal solutions for minimum time problems. Furthermore, the virtual domain improves the optimality of the solution, but at the cost of more computational time. The attitude representation also affects solution quality and computational speed. For minimum energy problems, the optimal solution can be obtained without the virtual domain with any considered attitude representation.

  11. The mixed alkali effect in ionically conducting glasses revisited: a study by molecular dynamics simulation.

    PubMed

    Habasaki, Junko; Ngai, Kia L

    2007-09-07

    When more than two kinds of mobile ions are mixed in ionic conducting glasses and crystals, there is a non-linear decrease of the transport coefficients of either type of ion. This phenomenon is known as the mixed mobile ion effect or Mixed Alkali Effect (MAE), and remains an unsolved problem. We use molecular dynamics simulation to study the complex ion dynamics in ionically conducting glasses including the MAE. In the mixed alkali lithium-potassium silicate glasses and related systems, a distinct part of the van Hove functions reveals that jumps from one kind of site to another are suppressed. Although, consensus for the existence of preferential jump paths for each kind of mobile ions seems to have been reached amongst researchers, the role of network formers and the number of unoccupied ion sites remain controversial in explaining the MAE. In principle, these factors when incorporated into a theory can generate the MAE, but in reality they are not essential for a viable explanation of the ion dynamics and the MAE. Instead, dynamical heterogeneity and "cooperativity blockage" originating from ion-ion interaction and correlation are fundamental for the observed ion dynamics and the MAE. Suppression of long range motion with increased back-correlated motions is shown to be a cause of the large decrease of the diffusivity especially in dilute foreign alkali regions. Support for our conclusion also comes from the fact that these features of ion dynamics are common to other ionic conductors, which have no glassy networks, and yet they all exhibit the MAE.

  12. Integrated aerodynamic/dynamic/structural optimization of helicopter rotor blades using multilevel decomposition

    NASA Technical Reports Server (NTRS)

    Walsh, Joanne L.; Young, Katherine C.; Pritchard, Jocelyn I.; Adelman, Howard M.; Mantay, Wayne R.

    1995-01-01

    This paper describes an integrated aerodynamic/dynamic/structural (IADS) optimization procedure for helicopter rotor blades. The procedure combines performance, dynamics, and structural analyses with a general-purpose optimizer using multilevel decomposition techniques. At the upper level, the structure is defined in terms of global quantities (stiffness, mass, and average strains). At the lower level, the structure is defined in terms of local quantities (detailed dimensions of the blade structure and stresses). The IADS procedure provides an optimization technique that is compatible with industrial design practices in which the aerodynamic and dynamic designs are performed at a global level and the structural design is carried out at a detailed level with considerable dialog and compromise among the aerodynamic, dynamic, and structural groups. The IADS procedure is demonstrated for several examples.

  13. Fractional-order in a macroeconomic dynamic model

    NASA Astrophysics Data System (ADS)

    David, S. A.; Quintino, D. D.; Soliani, J.

    2013-10-01

    In this paper, we applied the Riemann-Liouville approach in order to realize the numerical simulations to a set of equations that represent a fractional-order macroeconomic dynamic model. It is a generalization of a dynamic model recently reported in the literature. The aforementioned equations have been simulated for several cases involving integer and non-integer order analysis, with some different values to fractional order. The time histories and the phase diagrams have been plotted to visualize the effect of fractional order approach. The new contribution of this work arises from the fact that the macroeconomic dynamic model proposed here involves the public sector deficit equation, which renders the model more realistic and complete when compared with the ones encountered in the literature. The results reveal that the fractional-order macroeconomic model can exhibit a real reasonable behavior to macroeconomics systems and might offer greater insights towards the understanding of these complex dynamic systems.

  14. Application of optimization technique for flood damage modeling in river system

    NASA Astrophysics Data System (ADS)

    Barman, Sangita Deb; Choudhury, Parthasarathi

    2018-04-01

    A river system is defined as a network of channels that drains different parts of a basin uniting downstream to form a common outflow. An application of various models found in literatures, to a river system having multiple upstream flows is not always straight forward, involves a lengthy procedure; and with non-availability of data sets model calibration and applications may become difficult. In the case of a river system the flow modeling can be simplified to a large extent if the channel network is replaced by an equivalent single channel. In the present work optimization model formulations based on equivalent flow and applications of the mixed integer programming based pre-emptive goal programming model in evaluating flood control alternatives for a real life river system in India are proposed to be covered in the study.

  15. Unique Factorization in Cyclotomic Integers of Degree Seven

    ERIC Educational Resources Information Center

    Duckworth, W. Ethan

    2008-01-01

    This article provides a survey of some basic results in algebraic number theory and applies this material to prove that the cyclotomic integers generated by a seventh root of unity are a unique factorization domain. Part of the proof uses the computer algebra system Maple to find and verify factorizations. The proofs use a combination of historic…

  16. 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…

  17. Integer Flows and Circuit Covers of Graphs and Signed Graphs

    NASA Astrophysics Data System (ADS)

    Cheng, Jian

    The work in Chapter 2 is motivated by Tutte and Jaeger's pioneering work on converting modulo flows into integer-valued flows for ordinary graphs. For a signed graphs (G, sigma), we first prove that for each k ∈ {2, 3}, if (G, sigma) is (k - 1)-edge-connected and contains an even number of negative edges when k = 2, then every modulo k-flow of (G, sigma) can be converted into an integer-valued ( k + 1)-ow with a larger or the same support. We also prove that if (G, sigma) is odd-(2p+1)-edge-connected, then (G, sigma) admits a modulo circular (2 + 1/ p)-flows if and only if it admits an integer-valued circular (2 + 1/p)-flows, which improves all previous result by Xu and Zhang (DM2005), Schubert and Steffen (EJC2015), and Zhu (JCTB2015). Shortest circuit cover conjecture is one of the major open problems in graph theory. It states that every bridgeless graph G contains a set of circuits F such that each edge is contained in at least one member of F and the length of F is at most 7/5∥E(G)∥. This concept was recently generalized to signed graphs by Macajova et al. (JGT2015). In Chapter 3, we improve their upper bound from 11∥E( G)∥ to 14/3 ∥E(G)∥, and if G is 2-edgeconnected and has even negativeness, then it can be further reduced to 11/3 ∥E(G)∥. Tutte's 3-flow conjecture has been studied by many graph theorists in the last several decades. As a new approach to this conjecture, DeVos and Thomassen considered the vectors as ow values and found that there is a close relation between vector S1-flows and integer 3-NZFs. Motivated by their observation, in Chapter 4, we prove that if a graph G admits a vector S1-flow with rank at most two, then G admits an integer 3-NZF. The concept of even factors is highly related to the famous Four Color Theorem. We conclude this dissertation in Chapter 5 with an improvement of a recent result by Chen and Fan (JCTB2016) on the upperbound of even factors. We show that if a graph G contains an even factor, then it

  18. Bidding strategy for microgrid in day-ahead market based on hybrid stochastic/robust optimization

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

    Liu, Guodong; Xu, Yan; Tomsovic, Kevin

    In this paper, we propose an optimal bidding strategy in the day-ahead market of a microgrid consisting of intermittent distributed generation (DG), storage, dispatchable DG and price responsive loads. The microgrid coordinates the energy consumption or production of its components and trades electricity in both the day-ahead and real-time markets to minimize its operating cost as a single entity. The bidding problem is challenging due to a variety of uncertainties, including power output of intermittent DG, load variation, day-ahead and real-time market prices. A hybrid stochastic/robust optimization model is proposed to minimize the expected net cost, i.e., expected total costmore » of operation minus total benefit of demand. This formulation can be solved by mixed integer linear programming. The uncertain output of intermittent DG and day-ahead market price are modeled via scenarios based on forecast results, while a robust optimization is proposed to limit the unbalanced power in real-time market taking account of the uncertainty of real-time market price. Numerical simulations on a microgrid consisting of a wind turbine, a PV panel, a fuel cell, a micro-turbine, a diesel generator, a battery and a responsive load show the advantage of stochastic optimization in addition to robust optimization.« less

  19. Bidding strategy for microgrid in day-ahead market based on hybrid stochastic/robust optimization

    DOE PAGES

    Liu, Guodong; Xu, Yan; Tomsovic, Kevin

    2016-01-01

    In this paper, we propose an optimal bidding strategy in the day-ahead market of a microgrid consisting of intermittent distributed generation (DG), storage, dispatchable DG and price responsive loads. The microgrid coordinates the energy consumption or production of its components and trades electricity in both the day-ahead and real-time markets to minimize its operating cost as a single entity. The bidding problem is challenging due to a variety of uncertainties, including power output of intermittent DG, load variation, day-ahead and real-time market prices. A hybrid stochastic/robust optimization model is proposed to minimize the expected net cost, i.e., expected total costmore » of operation minus total benefit of demand. This formulation can be solved by mixed integer linear programming. The uncertain output of intermittent DG and day-ahead market price are modeled via scenarios based on forecast results, while a robust optimization is proposed to limit the unbalanced power in real-time market taking account of the uncertainty of real-time market price. Numerical simulations on a microgrid consisting of a wind turbine, a PV panel, a fuel cell, a micro-turbine, a diesel generator, a battery and a responsive load show the advantage of stochastic optimization in addition to robust optimization.« less

  20. Multi-objective dynamic aperture optimization for storage rings

    DOE PAGES

    Li, Yongjun; Yang, Lingyun

    2016-11-30

    We report an efficient dynamic aperture (DA) optimization approach using multiobjective genetic algorithm (MOGA), which is driven by nonlinear driving terms computation. It was found that having small low order driving terms is a necessary but insufficient condition of having a decent DA. Then direct DA tracking simulation is implemented among the last generation candidates to select the best solutions. The approach was demonstrated successfully in optimizing NSLS-II storage ring DA.

  1. A hybrid approach to modeling and control of vehicle height for electronically controlled air suspension

    NASA Astrophysics Data System (ADS)

    Sun, Xiaoqiang; Cai, Yingfeng; Wang, Shaohua; Liu, Yanling; Chen, Long

    2016-01-01

    The control problems associated with vehicle height adjustment of electronically controlled air suspension (ECAS) still pose theoretical challenges for researchers, which manifest themselves in the publications on this subject over the last years. This paper deals with modeling and control of a vehicle height adjustment system for ECAS, which is an example of a hybrid dynamical system due to the coexistence and coupling of continuous variables and discrete events. A mixed logical dynamical (MLD) modeling approach is chosen for capturing enough details of the vehicle height adjustment process. The hybrid dynamic model is constructed on the basis of some assumptions and piecewise linear approximation for components nonlinearities. Then, the on-off statuses of solenoid valves and the piecewise approximation process are described by propositional logic, and the hybrid system is transformed into the set of linear mixed-integer equalities and inequalities, denoted as MLD model, automatically by HYSDEL. Using this model, a hybrid model predictive controller (HMPC) is tuned based on online mixed-integer quadratic optimization (MIQP). Two different scenarios are considered in the simulation, whose results verify the height adjustment effectiveness of the proposed approach. Explicit solutions of the controller are computed to control the vehicle height adjustment system in realtime using an offline multi-parametric programming technology (MPT), thus convert the controller into an equivalent explicit piecewise affine form. Finally, bench experiments for vehicle height lifting, holding and lowering procedures are conducted, which demonstrate that the HMPC can adjust the vehicle height by controlling the on-off statuses of solenoid valves directly. This research proposes a new modeling and control method for vehicle height adjustment of ECAS, which leads to a closed-loop system with favorable dynamical properties.

  2. Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, Part I: main content.

    PubMed

    Orellana, Liliana; Rotnitzky, Andrea; Robins, James M

    2010-01-01

    Dynamic treatment regimes are set rules for sequential decision making based on patient covariate history. Observational studies are well suited for the investigation of the effects of dynamic treatment regimes because of the variability in treatment decisions found in them. This variability exists because different physicians make different decisions in the face of similar patient histories. In this article we describe an approach to estimate the optimal dynamic treatment regime among a set of enforceable regimes. This set is comprised by regimes defined by simple rules based on a subset of past information. The regimes in the set are indexed by a Euclidean vector. The optimal regime is the one that maximizes the expected counterfactual utility over all regimes in the set. We discuss assumptions under which it is possible to identify the optimal regime from observational longitudinal data. Murphy et al. (2001) developed efficient augmented inverse probability weighted estimators of the expected utility of one fixed regime. Our methods are based on an extension of the marginal structural mean model of Robins (1998, 1999) which incorporate the estimation ideas of Murphy et al. (2001). Our models, which we call dynamic regime marginal structural mean models, are specially suitable for estimating the optimal treatment regime in a moderately small class of enforceable regimes of interest. We consider both parametric and semiparametric dynamic regime marginal structural models. We discuss locally efficient, double-robust estimation of the model parameters and of the index of the optimal treatment regime in the set. In a companion paper in this issue of the journal we provide proofs of the main results.

  3. Puerto Rico water resources planning model program description

    USGS Publications Warehouse

    Moody, D.W.; Maddock, Thomas; Karlinger, M.R.; Lloyd, J.J.

    1973-01-01

    Because the use of the Mathematical Programming System -Extended (MPSX) to solve large linear and mixed integer programs requires the preparation of many input data cards, a matrix generator program to produce the MPSX input data from a much more limited set of data may expedite the use of the mixed integer programming optimization technique. The Model Definition and Control Program (MODCQP) is intended to assist a planner in preparing MPSX input data for the Puerto Rico Water Resources Planning Model. The model utilizes a mixed-integer mathematical program to identify a minimum present cost set of water resources projects (diversions, reservoirs, ground-water fields, desalinization plants, water treatment plants, and inter-basin transfers of water) which will meet a set of future water demands and to determine their sequence of construction. While MODCOP was specifically written to generate MPSX input data for the planning model described in this report, the program can be easily modified to reflect changes in the model's mathematical structure.

  4. Dynamic ADMM for Real-Time Optimal Power Flow: Preprint

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

    Dall-Anese, Emiliano; Zhang, Yijian; Hong, Mingyi

    This paper considers distribution networks featuring distributed energy resources (DERs), and develops a dynamic optimization method to maximize given operational objectives in real time while adhering to relevant network constraints. The design of the dynamic algorithm is based on suitable linearizations of the AC power flow equations, and it leverages the so-called alternating direction method of multipliers (ADMM). The steps of the ADMM, however, are suitably modified to accommodate appropriate measurements from the distribution network and the DERs. With the aid of these measurements, the resultant algorithm can enforce given operational constraints in spite of inaccuracies in the representation ofmore » the AC power flows, and it avoids ubiquitous metering to gather the state of non-controllable resources. Optimality and convergence of the propose algorithm are established in terms of tracking of the solution of a convex surrogate of the AC optimal power flow problem.« less

  5. Optimal planning for the sustainable utilization of municipal solid waste.

    PubMed

    Santibañez-Aguilar, José Ezequiel; Ponce-Ortega, José María; Betzabe González-Campos, J; Serna-González, Medardo; El-Halwagi, Mahmoud M

    2013-12-01

    The increasing generation of municipal solid waste (MSW) is a major problem particularly for large urban areas with insufficient landfill capacities and inefficient waste management systems. Several options associated to the supply chain for implementing a MSW management system are available, however to determine the optimal solution several technical, economic, environmental and social aspects must be considered. Therefore, this paper proposes a mathematical programming model for the optimal planning of the supply chain associated to the MSW management system to maximize the economic benefit while accounting for technical and environmental issues. The optimization model simultaneously selects the processing technologies and their location, the distribution of wastes from cities as well as the distribution of products to markets. The problem was formulated as a multi-objective mixed-integer linear programing problem to maximize the profit of the supply chain and the amount of recycled wastes, where the results are showed through Pareto curves that tradeoff economic and environmental aspects. The proposed approach is applied to a case study for the west-central part of Mexico to consider the integration of MSW from several cities to yield useful products. The results show that an integrated utilization of MSW can provide economic, environmental and social benefits. Copyright © 2013 Elsevier Ltd. All rights reserved.

  6. On the optimization of a mixed speaker array in an enclosed space using the virtual-speaker weighting method

    NASA Astrophysics Data System (ADS)

    Peng, Bo; Zheng, Sifa; Liao, Xiangning; Lian, Xiaomin

    2018-03-01

    In order to achieve sound field reproduction in a wide frequency band, multiple-type speakers are used. The reproduction accuracy is not only affected by the signals sent to the speakers, but also depends on the position and the number of each type of speaker. The method of optimizing a mixed speaker array is investigated in this paper. A virtual-speaker weighting method is proposed to optimize both the position and the number of each type of speaker. In this method, a virtual-speaker model is proposed to quantify the increment of controllability of the speaker array when the speaker number increases. While optimizing a mixed speaker array, the gain of the virtual-speaker transfer function is used to determine the priority orders of the candidate speaker positions, which optimizes the position of each type of speaker. Then the relative gain of the virtual-speaker transfer function is used to determine whether the speakers are redundant, which optimizes the number of each type of speaker. Finally the virtual-speaker weighting method is verified by reproduction experiments of the interior sound field in a passenger car. The results validate that the optimum mixed speaker array can be obtained using the proposed method.

  7. Analysis of grinding of superalloys and ceramics for off-line process optimization

    NASA Astrophysics Data System (ADS)

    Sathyanarayanan, G.

    The present study has compared the performances of resinoid, vitrified, and electroplated CBN wheels in creep feed grinding of M42 and D2 tool steels. Responses such as a specific energy, normal and tangential forces, and surface roughness were used as measures of performance. It was found that creep feed grinding with resinoid, vitrified, and electroplated CBN wheels has its own advantages, but no single wheel could provide good finish, lower specific energy, and high material removal rates simultaneously. To optimize the CBN grinding with different bonded wheels, a Multiple Criteria Decision Making (MCDM) methodology was used. Creep feed grinding of superalloys, Ti-6Al-4V and Inconel 718, has been modeled by utilizing neural networks to optimize the grinding process. A parallel effort was directed at creep feed grinding of alumina ceramics with diamond wheels to investigate the influence of process variables on responses based on experimental results and statistical analysis. The conflicting influence of variables was observed. This led to the formulation of ceramic grinding process as a multi-objective nonlinear mixed integer problem.

  8. Using Set Model for Learning Addition of Integers

    ERIC Educational Resources Information Center

    Lestari, Umi Puji; Putri, Ratu Ilma Indra; Hartono, Yusuf

    2015-01-01

    This study aims to investigate how set model can help students' understanding of addition of integers in fourth grade. The study has been carried out to 23 students and a teacher of IVC SD Iba Palembang in January 2015. This study is a design research that also promotes PMRI as the underlying design context and activity. Results showed that the…

  9. Optimal blood glucose level control using dynamic programming based on minimal Bergman model

    NASA Astrophysics Data System (ADS)

    Rettian Anggita Sari, Maria; Hartono

    2018-03-01

    The purpose of this article is to simulate the glucose dynamic and the insulin kinetic of diabetic patient. The model used in this research is a non-linear Minimal Bergman model. Optimal control theory is then applied to formulate the problem in order to determine the optimal dose of insulin in the treatment of diabetes mellitus such that the glucose level is in the normal range for some specific time range. The optimization problem is solved using dynamic programming. The result shows that dynamic programming is quite reliable to represent the interaction between glucose and insulin levels in diabetes mellitus patient.

  10. Optimized strategy for the control and prevention of newly emerging influenza revealed by the spread dynamics model.

    PubMed

    Zhang, Wen-Dou; Zu, Zheng-Hu; Xu, Qing; Xu, Zhi-Jing; Liu, Jin-Jie; Zheng, Tao

    2014-01-01

    No matching vaccine is immediately available when a novel influenza strain breaks out. Several nonvaccine-related strategies must be employed to control an influenza epidemic, including antiviral treatment, patient isolation, and immigration detection. This paper presents the development and application of two regional dynamic models of influenza with Pontryagin's Maximum Principle to determine the optimal control strategies for an epidemic and the corresponding minimum antiviral stockpiles. Antiviral treatment was found to be the most effective measure to control new influenza outbreaks. In the case of inadequate antiviral resources, the preferred approach was the centralized use of antiviral resources in the early stage of the epidemic. Immigration detection was the least cost-effective; however, when used in combination with the other measures, it may play a larger role. The reasonable mix of the three control measures could reduce the number of clinical cases substantially, to achieve the optimal control of new influenza.

  11. Optimal investments in digital communication systems in primary exchange area

    NASA Astrophysics Data System (ADS)

    Garcia, R.; Hornung, R.

    1980-11-01

    Integer linear optimization theory, following Gomory's method, was applied to the model planning of telecommunication networks in which all future investments are made in digital systems only. The integer decision variables are the number of digital systems set up on cable or radiorelay links that can be installed. The objective function is the total cost of the extension of the existing line capacity to meet the demand between primary and local exchanges. Traffic volume constraints and flow conservation in transit nodes complete the model. Results indicating computing time and method efficiency are illustrated by an example.

  12. An ultrashort mixing length micromixer: the shear superposition micromixer.

    PubMed

    Bottausci, Frédéric; Cardonne, Caroline; Meinhart, Carl; Mezić, Igor

    2007-03-01

    We report for the first time a laminar high-performance continuous micromixing process of two fluids over a length of 200 microns in under 10 milliseconds achieved by an optimization of the control parameters amplitude and frequency in the mixing device denoted as 'Shear Superposition Micromixer'. We improve mixing time by approximately 5 orders of magnitude over diffusion-limited mixing. The data indicate that rapid mixing is a result of the combined action of Taylor-Aris dispersion in the main and secondary microchannels and unsteady vortex motion that occurs at finite Reynolds number, which occurs above a threshold amplitude and frequency. The mixing performance is quantified using micron-resolution particle image velocimetry (micro-PIV) and computational fluid dynamics (CFD) simulations.

  13. Exploring the Sums of Powers of Consecutive q-Integers

    ERIC Educational Resources Information Center

    Kim, T.; Ryoo, C. S.; Jang, L. C.; Rim, S. H.

    2005-01-01

    The Bernoulli numbers are among the most interesting and important number sequences in mathematics. They first appeared in the posthumous work "Ars Conjectandi" (1713) by Jacob Bernoulli (1654-1705) in connection with sums of powers of consecutive integers (Bernoulli, 1713; or Smith, 1959). Bernoulli numbers are particularly important in number…

  14. Currency Arbitrage Detection Using a Binary Integer Programming Model

    ERIC Educational Resources Information Center

    Soon, Wanmei; Ye, Heng-Qing

    2011-01-01

    In this article, we examine the use of a new binary integer programming (BIP) model to detect arbitrage opportunities in currency exchanges. This model showcases an excellent application of mathematics to the real world. The concepts involved are easily accessible to undergraduate students with basic knowledge in Operations Research. Through this…

  15. Negative Integer Understanding: Characterizing First Graders' Mental Models

    ERIC Educational Resources Information Center

    Bofferding, Laura

    2014-01-01

    This article presents results of a research study. Sixty-one first graders' responses to interview questions about negative integer values and order and directed magnitudes were examined to characterize the students' mental models. The models reveal that initially, students overrelied on various combinations of whole-number principles as…

  16. Final Report on DOE Project entitled Dynamic Optimized Advanced Scheduling of Bandwidth Demands for Large-Scale Science Applications

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

    Ramamurthy, Byravamurthy

    2014-05-05

    In this project, developed scheduling frameworks for dynamic bandwidth demands for large-scale science applications. In particular, we developed scheduling algorithms for dynamic bandwidth demands in this project. Apart from theoretical approaches such as Integer Linear Programming, Tabu Search and Genetic Algorithm heuristics, we have utilized practical data from ESnet OSCARS project (from our DOE lab partners) to conduct realistic simulations of our approaches. We have disseminated our work through conference paper presentations and journal papers and a book chapter. In this project we addressed the problem of scheduling of lightpaths over optical wavelength division multiplexed (WDM) networks. We published severalmore » conference papers and journal papers on this topic. We also addressed the problems of joint allocation of computing, storage and networking resources in Grid/Cloud networks and proposed energy-efficient mechanisms for operatin optical WDM networks.« less

  17. The impact of case mix on timely access to appointments in a primary care group practice.

    PubMed

    Ozen, Asli; Balasubramanian, Hari

    2013-06-01

    At the heart of the practice of primary care is the concept of a physician panel. A panel refers to the set of patients for whose long term, holistic care the physician is responsible. A physician's appointment burden is determined by the size and composition of the panel. Size refers to the number of patients in the panel while composition refers to the case-mix, or the type of patients (older versus younger, healthy versus chronic patients), in the panel. In this paper, we quantify the impact of the size and case-mix on the ability of a multi-provider practice to provide adequate access to its empanelled patients. We use overflow frequency, or the probability that the demand exceeds the capacity, as a measure of access. We formulate problem of minimizing the maximum overflow for a multi-physician practice as a non-linear integer programming problem and establish structural insights that enable us to create simple yet near optimal heuristic strategies to change panels. This optimization framework helps a practice: (1) quantify the imbalances across physicians due to the variation in case mix and panel size, and the resulting effect on access; and (2) determine how panels can be altered in the least disruptive way to improve access. We illustrate our methodology using four test practices created using patient level data from the primary care practice at Mayo Clinic, Rochester, Minnesota. An important advantage of our approach is that it can be implemented in an Excel Spreadsheet and used for aggregate level planning and panel management decisions.

  18. Computer aided analysis and optimization of mechanical system dynamics

    NASA Technical Reports Server (NTRS)

    Haug, E. J.

    1984-01-01

    The purpose is to outline a computational approach to spatial dynamics of mechanical systems that substantially enlarges the scope of consideration to include flexible bodies, feedback control, hydraulics, and related interdisciplinary effects. Design sensitivity analysis and optimization is the ultimate goal. The approach to computer generation and solution of the system dynamic equations and graphical methods for creating animations as output is outlined.

  19. Optimization of Orifice Geometry for Cross-Flow Mixing in a Cylindrical Duct

    NASA Technical Reports Server (NTRS)

    Kroll, J. T.; Sowa, W. A.; Samuelsen, G. S.

    1996-01-01

    Mixing of gaseous jets in a cross-flow has significant applications in engineering, one example of which is the dilution zone of a gas turbine combustor. Despite years of study, the design of the jet injection in combustors is largely based on practical experience. The emergence of NO(x) regulations for stationary gas turbines and the anticipation of aero-engine regulations requires an improved understanding of jet mixing as new combustor concepts are introduced. For example, the success of the staged combustor to reduce the emission of NO(x) is almost entirely dependent upon the rapid and complete dilution of the rich zone products within the mixing section. It is these mixing challenges to which the present study is directed. A series of experiments was undertaken to delineate the optimal mixer orifice geometry. A cross-flow to core-flow momentum-flux ratio of 40 and a mass flow ratio of 2.5 were selected as representative of a conventional design. An experimental test matrix was designed around three variables: the number of orifices, the orifice length-to- width ratio, and the orifice angle. A regression analysis was performed on the data to arrive at an interpolating equation that predicted the mixing performance of orifice geometry combinations within the range of the test matrix parameters. Results indicate that the best mixing orifice geometry tested involves eight orifices with a long-to-short side aspect ratio of 3.5 at a twenty-three degree inclination from the center-line of the mixing section.

  20. A size-composition resolved aerosol model for simulating the dynamics of externally mixed particles: SCRAM (v 1.0)

    NASA Astrophysics Data System (ADS)

    Zhu, S.; Sartelet, K. N.; Seigneur, C.

    2015-06-01

    The Size-Composition Resolved Aerosol Model (SCRAM) for simulating the dynamics of externally mixed atmospheric particles is presented. This new model classifies aerosols by both composition and size, based on a comprehensive combination of all chemical species and their mass-fraction sections. All three main processes involved in aerosol dynamics (coagulation, condensation/evaporation and nucleation) are included. The model is first validated by comparison with a reference solution and with results of simulations using internally mixed particles. The degree of mixing of particles is investigated in a box model simulation using data representative of air pollution in Greater Paris. The relative influence on the mixing state of the different aerosol processes (condensation/evaporation, coagulation) and of the algorithm used to model condensation/evaporation (bulk equilibrium, dynamic) is studied.

  1. Computational fluid dynamics characterization of a novel mixed cell raceway design

    USDA-ARS?s Scientific Manuscript database

    Computational fluid dynamics (CFD) analysis was performed on a new type of mixed cell raceway (MCR) that incorporates longitudinal plug flow using inlet and outlet weirs for the primary fraction of the total flow. As opposed to regular MCR wherein vortices are entirely characterized by the boundary ...

  2. A multi-objective optimization model for hub network design under uncertainty: An inexact rough-interval fuzzy approach

    NASA Astrophysics Data System (ADS)

    Niakan, F.; Vahdani, B.; Mohammadi, M.

    2015-12-01

    This article proposes a multi-objective mixed-integer model to optimize the location of hubs within a hub network design problem under uncertainty. The considered objectives include minimizing the maximum accumulated travel time, minimizing the total costs including transportation, fuel consumption and greenhouse emissions costs, and finally maximizing the minimum service reliability. In the proposed model, it is assumed that for connecting two nodes, there are several types of arc in which their capacity, transportation mode, travel time, and transportation and construction costs are different. Moreover, in this model, determining the capacity of the hubs is part of the decision-making procedure and balancing requirements are imposed on the network. To solve the model, a hybrid solution approach is utilized based on inexact programming, interval-valued fuzzy programming and rough interval programming. Furthermore, a hybrid multi-objective metaheuristic algorithm, namely multi-objective invasive weed optimization (MOIWO), is developed for the given problem. Finally, various computational experiments are carried out to assess the proposed model and solution approaches.

  3. Confronting dynamics and uncertainty in optimal decision making for conservation

    USGS Publications Warehouse

    Williams, Byron K.; Johnson, Fred A.

    2013-01-01

    The effectiveness of conservation efforts ultimately depends on the recognition that decision making, and the systems that it is designed to affect, are inherently dynamic and characterized by multiple sources of uncertainty. To cope with these challenges, conservation planners are increasingly turning to the tools of decision analysis, especially dynamic optimization methods. Here we provide a general framework for optimal, dynamic conservation and then explore its capacity for coping with various sources and degrees of uncertainty. In broadest terms, the dynamic optimization problem in conservation is choosing among a set of decision options at periodic intervals so as to maximize some conservation objective over the planning horizon. Planners must account for immediate objective returns, as well as the effect of current decisions on future resource conditions and, thus, on future decisions. Undermining the effectiveness of such a planning process are uncertainties concerning extant resource conditions (partial observability), the immediate consequences of decision choices (partial controllability), the outcomes of uncontrolled, environmental drivers (environmental variation), and the processes structuring resource dynamics (structural uncertainty). Where outcomes from these sources of uncertainty can be described in terms of probability distributions, a focus on maximizing the expected objective return, while taking state-specific actions, is an effective mechanism for coping with uncertainty. When such probability distributions are unavailable or deemed unreliable, a focus on maximizing robustness is likely to be the preferred approach. Here the idea is to choose an action (or state-dependent policy) that achieves at least some minimum level of performance regardless of the (uncertain) outcomes. We provide some examples of how the dynamic optimization problem can be framed for problems involving management of habitat for an imperiled species, conservation of a

  4. Confronting dynamics and uncertainty in optimal decision making for conservation

    NASA Astrophysics Data System (ADS)

    Williams, Byron K.; Johnson, Fred A.

    2013-06-01

    The effectiveness of conservation efforts ultimately depends on the recognition that decision making, and the systems that it is designed to affect, are inherently dynamic and characterized by multiple sources of uncertainty. To cope with these challenges, conservation planners are increasingly turning to the tools of decision analysis, especially dynamic optimization methods. Here we provide a general framework for optimal, dynamic conservation and then explore its capacity for coping with various sources and degrees of uncertainty. In broadest terms, the dynamic optimization problem in conservation is choosing among a set of decision options at periodic intervals so as to maximize some conservation objective over the planning horizon. Planners must account for immediate objective returns, as well as the effect of current decisions on future resource conditions and, thus, on future decisions. Undermining the effectiveness of such a planning process are uncertainties concerning extant resource conditions (partial observability), the immediate consequences of decision choices (partial controllability), the outcomes of uncontrolled, environmental drivers (environmental variation), and the processes structuring resource dynamics (structural uncertainty). Where outcomes from these sources of uncertainty can be described in terms of probability distributions, a focus on maximizing the expected objective return, while taking state-specific actions, is an effective mechanism for coping with uncertainty. When such probability distributions are unavailable or deemed unreliable, a focus on maximizing robustness is likely to be the preferred approach. Here the idea is to choose an action (or state-dependent policy) that achieves at least some minimum level of performance regardless of the (uncertain) outcomes. We provide some examples of how the dynamic optimization problem can be framed for problems involving management of habitat for an imperiled species, conservation of a

  5. Detailed design of a lattice composite fuselage structure by a mixed optimization method

    NASA Astrophysics Data System (ADS)

    Liu, D.; Lohse-Busch, H.; Toropov, V.; Hühne, C.; Armani, U.

    2016-10-01

    In this article, a procedure for designing a lattice fuselage barrel is developed. It comprises three stages: first, topology optimization of an aircraft fuselage barrel is performed with respect to weight and structural performance to obtain the conceptual design. The interpretation of the optimal result is given to demonstrate the development of this new lattice airframe concept for the fuselage barrel. Subsequently, parametric optimization of the lattice aircraft fuselage barrel is carried out using genetic algorithms on metamodels generated with genetic programming from a 101-point optimal Latin hypercube design of experiments. The optimal design is achieved in terms of weight savings subject to stability, global stiffness and strain requirements, and then verified by the fine mesh finite element simulation of the lattice fuselage barrel. Finally, a practical design of the composite skin complying with the aircraft industry lay-up rules is presented. It is concluded that the mixed optimization method, combining topology optimization with the global metamodel-based approach, allows the problem to be solved with sufficient accuracy and provides the designers with a wealth of information on the structural behaviour of the novel anisogrid composite fuselage design.

  6. Multi-Item Multiperiodic Inventory Control Problem with Variable Demand and Discounts: A Particle Swarm Optimization Algorithm

    PubMed Central

    Mousavi, Seyed Mohsen; Niaki, S. T. A.; Bahreininejad, Ardeshir; Musa, Siti Nurmaya

    2014-01-01

    A multi-item multiperiod inventory control model is developed for known-deterministic variable demands under limited available budget. Assuming the order quantity is more than the shortage quantity in each period, the shortage in combination of backorder and lost sale is considered. The orders are placed in batch sizes and the decision variables are assumed integer. Moreover, all unit discounts for a number of products and incremental quantity discount for some other items are considered. While the objectives are to minimize both the total inventory cost and the required storage space, the model is formulated into a fuzzy multicriteria decision making (FMCDM) framework and is shown to be a mixed integer nonlinear programming type. In order to solve the model, a multiobjective particle swarm optimization (MOPSO) approach is applied. A set of compromise solution including optimum and near optimum ones via MOPSO has been derived for some numerical illustration, where the results are compared with those obtained using a weighting approach. To assess the efficiency of the proposed MOPSO, the model is solved using multi-objective genetic algorithm (MOGA) as well. A large number of numerical examples are generated at the end, where graphical and statistical approaches show more efficiency of MOPSO compared with MOGA. PMID:25093195

  7. Multilevel decomposition approach to integrated aerodynamic/dynamic/structural optimization of helicopter rotor blades

    NASA Technical Reports Server (NTRS)

    Walsh, Joanne L.; Young, Katherine C.; Pritchard, Jocelyn I.; Adelman, Howard M.; Mantay, Wayne R.

    1994-01-01

    This paper describes an integrated aerodynamic, dynamic, and structural (IADS) optimization procedure for helicopter rotor blades. The procedure combines performance, dynamics, and structural analyses with a general purpose optimizer using multilevel decomposition techniques. At the upper level, the structure is defined in terms of local quantities (stiffnesses, mass, and average strains). At the lower level, the structure is defined in terms of local quantities (detailed dimensions of the blade structure and stresses). The IADS procedure provides an optimization technique that is compatible with industrial design practices in which the aerodynamic and dynamic design is performed at a global level and the structural design is carried out at a detailed level with considerable dialogue and compromise among the aerodynamic, dynamic, and structural groups. The IADS procedure is demonstrated for several cases.

  8. Lagrangian Approach to Jet Mixing and Optimization of the Reactor for Production of Carbon Nanotubes

    NASA Technical Reports Server (NTRS)

    Povitsky, Alex; Salas, Manuel D.

    2001-01-01

    This study was motivated by an attempt to optimize the High Pressure carbon oxide (HiPco) process for the production of carbon nanotubes from gaseous carbon oxide, The goal is to achieve rapid and uniform heating of catalyst particles by an optimal arrangement of jets. A mixed Eulerian and Lagrangian approach is implemented to track the temperature of catalyst particles along their trajectories as a function of time. The FLUENT CFD software with second-order upwind approximation of convective terms and an algebraic multigrid-based solver is used. The poor performance of the original reactor configuration is explained in terms of features of particle trajectories. The trajectories most exposed to the hot jets appear to be the most problematic for heating because they either bend towards the cold jet interior or rotate upwind of the mixing zone. To reduce undesirable slow and/or oscillatory heating of catalyst particles, a reactor configuration with three central jets is proposed and the optimal location of the central and peripheral nozzles is determined.

  9. Bayesian Optimization Under Mixed Constraints with A Slack-Variable Augmented Lagrangian

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

    Picheny, Victor; Gramacy, Robert B.; Wild, Stefan M.

    An augmented Lagrangian (AL) can convert a constrained optimization problem into a sequence of simpler (e.g., unconstrained) problems, which are then usually solved with local solvers. Recently, surrogate-based Bayesian optimization (BO) sub-solvers have been successfully deployed in the AL framework for a more global search in the presence of inequality constraints; however, a drawback was that expected improvement (EI) evaluations relied on Monte Carlo. Here we introduce an alternative slack variable AL, and show that in this formulation the EI may be evaluated with library routines. The slack variables furthermore facilitate equality as well as inequality constraints, and mixtures thereof.more » We show our new slack “ALBO” compares favorably to the original. Its superiority over conventional alternatives is reinforced on several mixed constraint examples.« less

  10. Optimal interdependence enhances the dynamical robustness of complex systems.

    PubMed

    Singh, Rishu Kumar; Sinha, Sitabhra

    2017-08-01

    Although interdependent systems have usually been associated with increased fragility, we show that strengthening the interdependence between dynamical processes on different networks can make them more likely to survive over long times. By coupling the dynamics of networks that in isolation exhibit catastrophic collapse with extinction of nodal activity, we demonstrate system-wide persistence of activity for an optimal range of interdependence between the networks. This is related to the appearance of attractors of the global dynamics comprising disjoint sets ("islands") of stable activity.

  11. Optimal interdependence enhances the dynamical robustness of complex systems

    NASA Astrophysics Data System (ADS)

    Singh, Rishu Kumar; Sinha, Sitabhra

    2017-08-01

    Although interdependent systems have usually been associated with increased fragility, we show that strengthening the interdependence between dynamical processes on different networks can make them more likely to survive over long times. By coupling the dynamics of networks that in isolation exhibit catastrophic collapse with extinction of nodal activity, we demonstrate system-wide persistence of activity for an optimal range of interdependence between the networks. This is related to the appearance of attractors of the global dynamics comprising disjoint sets ("islands") of stable activity.

  12. Curcumin-loaded mixed micelles: preparation, optimization, physicochemical properties and cytotoxicity in vitro.

    PubMed

    Duan, Yuwei; Wang, Juan; Yang, Xiaoye; Du, Hongliang; Xi, Yanwei; Zhai, Guangxi

    2015-01-01

    Although curcumin (CUR) can inhibit proliferation and induce apoptosis of tumors, the poor water solubility restricted its clinical application. The aim of this study was to improve the aqueous solubility of CUR and make more favorable changes to bioactivity by preparing curcumin-loaded phospholipid-sodium deoxycholate-mixed micelles (CUR-PC-SDC-MMs). CUR-PC-SDC-MMs were prepared by the thin-film dispersion method. Based on the results of single factor exploration, the preparation technology was optimized using the central composite design-response surface methodology with drug loading and entrapment efficiency (EE%) as indicators. The images of transmission electron microscopy showed that the optimized CUR-PC-SDC-MMs were spherical and well dispersed. The average size of the mixed micelles was 66.5 nm, the zeta potential was about -26.96 mV and critical micelle concentration was 0.0087 g/l. CUR was encapsulated in PC-SDC-MMs with loading capacity of 13.12%, EE% of 87.58%, and the solubility of CUR in water was 3.14 mg/ml. The release results in vitro showed that the mixed micelles presented sustained release behavior compared to the propylene glycol solution of CUR. The IC50 values of CUR-loaded micelles and free drug in human breast carcinoma cell lines were 4.10 μg/ml and 6.93 µg/ml, respectively. It could be concluded from the above results that the CUR-PC-SDC-MMs system might serve as a promising nanocarrier to improve the solubility and bioactivity of CUR.

  13. Hybrid Nested Partitions and Math Programming Framework for Large-scale Combinatorial Optimization

    DTIC Science & Technology

    2010-03-31

    optimization problems: 1) exact algorithms and 2) metaheuristic algorithms . This project will integrate concepts from these two technologies to develop...optimal solutions within an acceptable amount of computation time, and 2) metaheuristic algorithms such as genetic algorithms , tabu search, and the...integer programming decomposition approaches, such as Dantzig Wolfe decomposition and Lagrangian relaxation, and metaheuristics such as the Nested

  14. Review of dynamic optimization methods in renewable natural resource management

    USGS Publications Warehouse

    Williams, B.K.

    1989-01-01

    In recent years, the applications of dynamic optimization procedures in natural resource management have proliferated. A systematic review of these applications is given in terms of a number of optimization methodologies and natural resource systems. The applicability of the methods to renewable natural resource systems are compared in terms of system complexity, system size, and precision of the optimal solutions. Recommendations are made concerning the appropriate methods for certain kinds of biological resource problems.

  15. Scheduling algorithms for rapid imaging using agile Cubesat constellations

    NASA Astrophysics Data System (ADS)

    Nag, Sreeja; Li, Alan S.; Merrick, James H.

    2018-02-01

    Distributed Space Missions such as formation flight and constellations, are being recognized as important Earth Observation solutions to increase measurement samples over space and time. Cubesats are increasing in size (27U, ∼40 kg in development) with increasing capabilities to host imager payloads. Given the precise attitude control systems emerging in the commercial market, Cubesats now have the ability to slew and capture images within short notice. We propose a modular framework that combines orbital mechanics, attitude control and scheduling optimization to plan the time-varying, full-body orientation of agile Cubesats in a constellation such that they maximize the number of observed images and observation time, within the constraints of Cubesat hardware specifications. The attitude control strategy combines bang-bang and PD control, with constraints such as power consumption, response time, and stability factored into the optimality computations and a possible extension to PID control to account for disturbances. Schedule optimization is performed using dynamic programming with two levels of heuristics, verified and improved upon using mixed integer linear programming. The automated scheduler is expected to run on ground station resources and the resultant schedules uplinked to the satellites for execution, however it can be adapted for onboard scheduling, contingent on Cubesat hardware and software upgrades. The framework is generalizable over small steerable spacecraft, sensor specifications, imaging objectives and regions of interest, and is demonstrated using multiple 20 kg satellites in Low Earth Orbit for two case studies - rapid imaging of Landsat's land and coastal images and extended imaging of global, warm water coral reefs. The proposed algorithm captures up to 161% more Landsat images than nadir-pointing sensors with the same field of view, on a 2-satellite constellation over a 12-h simulation. Integer programming was able to verify that

  16. Plume and wake dynamics, mixing, and chemistry behind an HSCT aircraft

    NASA Technical Reports Server (NTRS)

    Miake-Lye, R. C.; Martinez-Sanchez, M.; Brown, R. C.; Kolb, C. E.

    1991-01-01

    The chemical evolution and mixing and vortical motion of a High Speed Civil Transport's engine exhausts must be analyzed in order to track the gas and its speciation as emissions are mixed to atmospheric scales. Attention is presently given to an analytic model of the wake dynamical processes which accounts for the roll-up of the trailing vorticity, its breakup due to the Crow instability, and the subsequent evolution and motion of the reconnected vorticity. The concentrated vorticity is noted to wrap up the buoyant exhaust and suppress its continued mixing and dilution. The species tracked encompass those which could be heterogeneously reactive on the surfaces of the condensed ice particles, and those capable of reacting with exhaust soot particle surfaces to form active contrail and/or cloud condensation nuclei.

  17. Selection of actuator locations for static shape control of large space structures by heuristic integer programing

    NASA Technical Reports Server (NTRS)

    Haftka, R. T.; Adelman, H. M.

    1984-01-01

    Orbiting spacecraft such as large space antennas have to maintain a highly accurate space to operate satisfactorily. Such structures require active and passive controls to mantain an accurate shape under a variety of disturbances. Methods for the optimum placement of control actuators for correcting static deformations are described. In particular, attention is focused on the case were control locations have to be selected from a large set of available sites, so that integer programing methods are called for. The effectiveness of three heuristic techniques for obtaining a near-optimal site selection is compared. In addition, efficient reanalysis techniques for the rapid assessment of control effectiveness are presented. Two examples are used to demonstrate the methods: a simple beam structure and a 55m space-truss-parabolic antenna.

  18. Direct comparison of fractional and integer quantized Hall resistance

    NASA Astrophysics Data System (ADS)

    Ahlers, Franz J.; Götz, Martin; Pierz, Klaus

    2017-08-01

    We present precision measurements of the fractional quantized Hall effect, where the quantized resistance {{R}≤ft[ 1/3 \\right]} in the fractional quantum Hall state at filling factor 1/3 was compared with a quantized resistance {{R}[2]} , represented by an integer quantum Hall state at filling factor 2. A cryogenic current comparator bridge capable of currents down to the nanoampere range was used to directly compare two resistance values of two GaAs-based devices located in two cryostats. A value of 1-(5.3  ±  6.3) 10-8 (95% confidence level) was obtained for the ratio ({{R}≤ft[ 1/3 \\right]}/6{{R}[2]} ). This constitutes the most precise comparison of integer resistance quantization (in terms of h/e 2) in single-particle systems and of fractional quantization in fractionally charged quasi-particle systems. While not relevant for practical metrology, such a test of the validity of the underlying physics is of significance in the context of the upcoming revision of the SI.

  19. Accurate construction of consensus genetic maps via integer linear programming.

    PubMed

    Wu, Yonghui; Close, Timothy J; Lonardi, Stefano

    2011-01-01

    We study the problem of merging genetic maps, when the individual genetic maps are given as directed acyclic graphs. The computational problem is to build a consensus map, which is a directed graph that includes and is consistent with all (or, the vast majority of) the markers in the input maps. However, when markers in the individual maps have ordering conflicts, the resulting consensus map will contain cycles. Here, we formulate the problem of resolving cycles in the context of a parsimonious paradigm that takes into account two types of errors that may be present in the input maps, namely, local reshuffles and global displacements. The resulting combinatorial optimization problem is, in turn, expressed as an integer linear program. A fast approximation algorithm is proposed, and an additional speedup heuristic is developed. Our algorithms were implemented in a software tool named MERGEMAP which is freely available for academic use. An extensive set of experiments shows that MERGEMAP consistently outperforms JOINMAP, which is the most popular tool currently available for this task, both in terms of accuracy and running time. MERGEMAP is available for download at http://www.cs.ucr.edu/~yonghui/mgmap.html.

  20. Discovery of Boolean metabolic networks: integer linear programming based approach.

    PubMed

    Qiu, Yushan; Jiang, Hao; Ching, Wai-Ki; Cheng, Xiaoqing

    2018-04-11

    Traditional drug discovery methods focused on the efficacy of drugs rather than their toxicity. However, toxicity and/or lack of efficacy are produced when unintended targets are affected in metabolic networks. Thus, identification of biological targets which can be manipulated to produce the desired effect with minimum side-effects has become an important and challenging topic. Efficient computational methods are required to identify the drug targets while incurring minimal side-effects. In this paper, we propose a graph-based computational damage model that summarizes the impact of enzymes on compounds in metabolic networks. An efficient method based on Integer Linear Programming formalism is then developed to identify the optimal enzyme-combination so as to minimize the side-effects. The identified target enzymes for known successful drugs are then verified by comparing the results with those in the existing literature. Side-effects reduction plays a crucial role in the study of drug development. A graph-based computational damage model is proposed and the theoretical analysis states the captured problem is NP-completeness. The proposed approaches can therefore contribute to the discovery of drug targets. Our developed software is available at " http://hkumath.hku.hk/~wkc/APBC2018-metabolic-network.zip ".

  1. Improving the Dynamic Characteristics of Body-in-White Structure Using Structural Optimization

    PubMed Central

    Yahaya Rashid, Aizzat S.; Mohamed Haris, Sallehuddin; Alias, Anuar

    2014-01-01

    The dynamic behavior of a body-in-white (BIW) structure has significant influence on the noise, vibration, and harshness (NVH) and crashworthiness of a car. Therefore, by improving the dynamic characteristics of BIW, problems and failures associated with resonance and fatigue can be prevented. The design objectives attempt to improve the existing torsion and bending modes by using structural optimization subjected to dynamic load without compromising other factors such as mass and stiffness of the structure. The natural frequency of the design was modified by identifying and reinforcing the structure at critical locations. These crucial points are first identified by topology optimization using mass and natural frequencies as the design variables. The individual components obtained from the analysis go through a size optimization step to find their target thickness of the structure. The thickness of affected regions of the components will be modified according to the analysis. The results of both optimization steps suggest several design modifications to achieve the target vibration specifications without compromising the stiffness of the structure. A method of combining both optimization approaches is proposed to improve the design modification process. PMID:25101312

  2. Radiograph and passive data analysis using mixed variable optimization

    DOEpatents

    Temple, Brian A.; Armstrong, Jerawan C.; Buescher, Kevin L.; Favorite, Jeffrey A.

    2015-06-02

    Disclosed herein are representative embodiments of methods, apparatus, and systems for performing radiography analysis. For example, certain embodiments perform radiographic analysis using mixed variable computation techniques. One exemplary system comprises a radiation source, a two-dimensional detector for detecting radiation transmitted through a object between the radiation source and detector, and a computer. In this embodiment, the computer is configured to input the radiographic image data from the two-dimensional detector and to determine one or more materials that form the object by using an iterative analysis technique that selects the one or more materials from hierarchically arranged solution spaces of discrete material possibilities and selects the layer interfaces from the optimization of the continuous interface data.

  3. A COMPARISON OF STATIC AND DYNAMIC OPTIMIZATION MUSCLE FORCE PREDICTIONS DURING WHEELCHAIR PROPULSION

    PubMed Central

    Morrow, Melissa M.; Rankin, Jeffery W.; Neptune, Richard R.; Kaufman, Kenton R.

    2014-01-01

    The primary purpose of this study was to compare static and dynamic optimization muscle force and work predictions during the push phase of wheelchair propulsion. A secondary purpose was to compare the differences in predicted shoulder and elbow kinetics and kinematics and handrim forces. The forward dynamics simulation minimized differences between simulated and experimental data (obtained from 10 manual wheelchair users) and muscle co-contraction. For direct comparison between models, the shoulder and elbow muscle moment arms and net joint moments from the dynamic optimization were used as inputs into the static optimization routine. RMS errors between model predictions were calculated to quantify model agreement. There was a wide range of individual muscle force agreement that spanned from poor (26.4 % Fmax error in the middle deltoid) to good (6.4 % Fmax error in the anterior deltoid) in the prime movers of the shoulder. The predicted muscle forces from the static optimization were sufficient to create the appropriate motion and joint moments at the shoulder for the push phase of wheelchair propulsion, but showed deviations in the elbow moment, pronation-supination motion and hand rim forces. These results suggest the static approach does not produce results similar enough to be a replacement for forward dynamics simulations, and care should be taken in choosing the appropriate method for a specific task and set of constraints. Dynamic optimization modeling approaches may be required for motions that are greatly influenced by muscle activation dynamics or that require significant co-contraction. PMID:25282075

  4. The Retrofit Puzzle Extended: Optimal Fleet Owner Behavior over Multiple Time Periods

    DOT National Transportation Integrated Search

    2009-08-04

    In "The Retrofit Puzzle: Optimal Fleet Owner Behavior in the Context of Diesel Retrofit Incentive Programs" (1) an integer program was developed to model profit-maximizing diesel fleet owner behavior when selecting pollution reduction retrofits. Flee...

  5. Optimization of long circulating mixed polymeric micelles containing vinpocetine using simple lattice mixture design, in vitro and in vivo characterization.

    PubMed

    El-Dahmy, Rania Moataz; Elsayed, Ibrahim; Elshafeey, Ahmed Hassen; Gawad, Nabaweya Abdelaziz Abd El; El-Gazayerly, Omaima Naim

    2014-12-30

    The aim of this study was to increase the in vivo mean residence time of vinpocetine after IV injection utilizing long circulating mixed micellar systems. Mixed micelles were prepared using Pluronics L121, P123 and F127. The systems were characterized by testing their entrapment efficiency, particle size, polydispersity index, zeta potential, transmission electron microscopy and in vitro drug release. Simple lattice mixture design was planned for the optimization using Design-Expert(®) software. The optimized formula was lyophilized, sterilized and imaged by scanning electron microscope. Moreover, the in vivo behavior of the optimized formula was evaluated after IV injection in rabbits. The optimized formula, containing 68% w/w Pluronic L121 and 32% w/w Pluronic F127, had the highest desirability value (0.621). Entrapment efficiency, particle size, polydispersity index and zeta potential of the optimized formula were 50.74 ± 3.26%, 161.50 ± 7.39 nm, 0.21 ± 0.03 and -22.42 ± 1.72 mV, respectively. Lyophilization and sterilization did not affect the characteristics of the optimized formula. Upon in vivo investigation in rabbits, the optimized formula showed a significantly higher elimination half-life and mean residence time than the market product. Finally, mixed micelles could be considered as a promising long circulating nanocarrier for lipophilic drugs. Copyright © 2014 Elsevier B.V. All rights reserved.

  6. Modeling Temporal Behavior in Large Networks: A Dynamic Mixed-Membership Model

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

    Rossi, R; Gallagher, B; Neville, J

    Given a large time-evolving network, how can we model and characterize the temporal behaviors of individual nodes (and network states)? How can we model the behavioral transition patterns of nodes? We propose a temporal behavior model that captures the 'roles' of nodes in the graph and how they evolve over time. The proposed dynamic behavioral mixed-membership model (DBMM) is scalable, fully automatic (no user-defined parameters), non-parametric/data-driven (no specific functional form or parameterization), interpretable (identifies explainable patterns), and flexible (applicable to dynamic and streaming networks). Moreover, the interpretable behavioral roles are generalizable, computationally efficient, and natively supports attributes. We applied ourmore » model for (a) identifying patterns and trends of nodes and network states based on the temporal behavior, (b) predicting future structural changes, and (c) detecting unusual temporal behavior transitions. We use eight large real-world datasets from different time-evolving settings (dynamic and streaming). In particular, we model the evolving mixed-memberships and the corresponding behavioral transitions of Twitter, Facebook, IP-Traces, Email (University), Internet AS, Enron, Reality, and IMDB. The experiments demonstrate the scalability, flexibility, and effectiveness of our model for identifying interesting patterns, detecting unusual structural transitions, and predicting the future structural changes of the network and individual nodes.« less

  7. Efficient computation of optimal actions.

    PubMed

    Todorov, Emanuel

    2009-07-14

    Optimal choice of actions is a fundamental problem relevant to fields as diverse as neuroscience, psychology, economics, computer science, and control engineering. Despite this broad relevance the abstract setting is similar: we have an agent choosing actions over time, an uncertain dynamical system whose state is affected by those actions, and a performance criterion that the agent seeks to optimize. Solving problems of this kind remains hard, in part, because of overly generic formulations. Here, we propose a more structured formulation that greatly simplifies the construction of optimal control laws in both discrete and continuous domains. An exhaustive search over actions is avoided and the problem becomes linear. This yields algorithms that outperform Dynamic Programming and Reinforcement Learning, and thereby solve traditional problems more efficiently. Our framework also enables computations that were not possible before: composing optimal control laws by mixing primitives, applying deterministic methods to stochastic systems, quantifying the benefits of error tolerance, and inferring goals from behavioral data via convex optimization. Development of a general class of easily solvable problems tends to accelerate progress--as linear systems theory has done, for example. Our framework may have similar impact in fields where optimal choice of actions is relevant.

  8. Insights on chaotic dynamics: mixing experiments between natural silicate melts from Vulcano island (Aeolian Islands, Italy)

    NASA Astrophysics Data System (ADS)

    Rossi, Stefano; Morgavi, Daniele; Vetere, Francesco; Petrelli, Maurizio; Perugini, Diego

    2017-04-01

    keywords: Magma mixing, chaotic dynamics, time series experiments Magma mixing is a petrologic phenomenon which is recognized as potential trigger of highly explosive eruptions and its evidence is commonly observable in natural rocks. Here we tried to replicate the dynamic conditions of mixing performing a set of chaotic mixing experiments between shoshonitic and rhyolitic magmas from Vulcano island. Vulcano is the southernmost island of the Aeolian Archipelago (Aeolian Islands, Italy); it is completely built by volcanic rocks with variable degree of evolution ranging from basalt to rhyolite (e.g. Keller 1980; Ellam et al. 1988; De Astis 1995; De Astis et al. 2013) and its magmatic activity dates back to about 120 ky. Last eruption occurred in 1888-1890. The chaotic mixing experiments were performed by using the new ChaOtic Magma Mixing Apparatus (COMMA), held at the Department of Physics and Geology, University of Perugia. This new experimental device allows to track the evolution of the mixing process and the associated modulation of chemical composition between different magmas. Experiments were performed at 1200°C and atmospheric pressure with a viscosity ratio higher than three orders of magnitude. The experimental protocol was chosen to ensure the occurrence of chaotic dynamics in the system and the run duration was progressively increased (e.g. 10.5 h, 21 h, 42 h). The products of each experiment are crystal-free glasses in which the variation of major elements was investigated along different profiles using electron microprobe (EMPA) at Institute für Mineralogie, Leibniz Universität of Hannover (Germany). The efficiency of the mixing process is estimated by calculating the decrease of concentration variance in time and it is shown that the variance of major elements exponentially decays. Our results confirm and quantify how different chemical elements homogenize in the melt at differing rates. It is also observable that the mixing structures generated

  9. Optimized Strategy for the Control and Prevention of Newly Emerging Influenza Revealed by the Spread Dynamics Model

    PubMed Central

    Zhang, Wen-Dou; Zu, Zheng-Hu; Xu, Qing; Xu, Zhi-Jing; Liu, Jin-Jie; Zheng, Tao

    2014-01-01

    No matching vaccine is immediately available when a novel influenza strain breaks out. Several nonvaccine-related strategies must be employed to control an influenza epidemic, including antiviral treatment, patient isolation, and immigration detection. This paper presents the development and application of two regional dynamic models of influenza with Pontryagin’s Maximum Principle to determine the optimal control strategies for an epidemic and the corresponding minimum antiviral stockpiles. Antiviral treatment was found to be the most effective measure to control new influenza outbreaks. In the case of inadequate antiviral resources, the preferred approach was the centralized use of antiviral resources in the early stage of the epidemic. Immigration detection was the least cost-effective; however, when used in combination with the other measures, it may play a larger role. The reasonable mix of the three control measures could reduce the number of clinical cases substantially, to achieve the optimal control of new influenza. PMID:24392151

  10. New Approaches for Very Large-Scale Integer Programming

    DTIC Science & Technology

    2016-06-24

    existing algorithms. This research has been presented at several conferences and has and will appear in archival journals. 15. SUBJECT TERMS integer...This research has been presented at several conferences and has and will appear in archival journals. Distribution Statement This is block 12 on the...pdf Upload a Report Document, if any. The maximum file size for the Report Document is 50MB. Archival Publications (published) during reporting

  11. Simultaneous optimization of photons and electrons for mixed beam radiotherapy

    NASA Astrophysics Data System (ADS)

    Mueller, S.; Fix, M. K.; Joosten, A.; Henzen, D.; Frei, D.; Volken, W.; Kueng, R.; Aebersold, D. M.; Stampanoni, M. F. M.; Manser, P.

    2017-07-01

    The aim of this work is to develop and investigate an inverse treatment planning process (TPP) for mixed beam radiotherapy (MBRT) capable of performing simultaneous optimization of photon and electron apertures. A simulated annealing based direct aperture optimization (DAO) is implemented to perform simultaneous optimization of photon and electron apertures, both shaped with the photon multileaf collimator (pMLC). Validated beam models are used as input for Monte Carlo dose calculations. Consideration of photon pMLC transmission during DAO and a weight re-optimization of the apertures after deliverable dose calculation are utilized to efficiently reduce the differences between optimized and deliverable dose distributions. The TPP for MBRT is evaluated for an academic situation with a superficial and an enlarged PTV in the depth, a left chest wall case including the internal mammary chain and a squamous cell carcinoma case. Deliverable dose distributions of MBRT plans are compared to those of modulated electron radiotherapy (MERT), photon IMRT and if available to those of clinical VMAT plans. The generated MBRT plans dosimetrically outperform the MERT, photon IMRT and VMAT plans for all investigated situations. For the clinical cases of the left chest wall and the squamous cell carcinoma, the MBRT plans cover the PTV similarly or more homogeneously than the VMAT plans, while OARs are spared considerably better with average reductions of the mean dose to parallel OARs and D 2% to serial OARs by 54% and 26%, respectively. Moreover, the low dose bath expressed as V 10% to normal tissue is substantially reduced by up to 45% compared to the VMAT plans. A TPP for MBRT including simultaneous optimization is successfully implemented and the dosimetric superiority of MBRT plans over MERT, photon IMRT and VMAT plans is demonstrated for academic and clinical situations including superficial targets with and without deep-seated part.

  12. Simultaneous optimization of photons and electrons for mixed beam radiotherapy.

    PubMed

    Mueller, S; Fix, M K; Joosten, A; Henzen, D; Frei, D; Volken, W; Kueng, R; Aebersold, D M; Stampanoni, M F M; Manser, P

    2017-06-26

    The aim of this work is to develop and investigate an inverse treatment planning process (TPP) for mixed beam radiotherapy (MBRT) capable of performing simultaneous optimization of photon and electron apertures. A simulated annealing based direct aperture optimization (DAO) is implemented to perform simultaneous optimization of photon and electron apertures, both shaped with the photon multileaf collimator (pMLC). Validated beam models are used as input for Monte Carlo dose calculations. Consideration of photon pMLC transmission during DAO and a weight re-optimization of the apertures after deliverable dose calculation are utilized to efficiently reduce the differences between optimized and deliverable dose distributions. The TPP for MBRT is evaluated for an academic situation with a superficial and an enlarged PTV in the depth, a left chest wall case including the internal mammary chain and a squamous cell carcinoma case. Deliverable dose distributions of MBRT plans are compared to those of modulated electron radiotherapy (MERT), photon IMRT and if available to those of clinical VMAT plans. The generated MBRT plans dosimetrically outperform the MERT, photon IMRT and VMAT plans for all investigated situations. For the clinical cases of the left chest wall and the squamous cell carcinoma, the MBRT plans cover the PTV similarly or more homogeneously than the VMAT plans, while OARs are spared considerably better with average reductions of the mean dose to parallel OARs and D 2% to serial OARs by 54% and 26%, respectively. Moreover, the low dose bath expressed as V 10% to normal tissue is substantially reduced by up to 45% compared to the VMAT plans. A TPP for MBRT including simultaneous optimization is successfully implemented and the dosimetric superiority of MBRT plans over MERT, photon IMRT and VMAT plans is demonstrated for academic and clinical situations including superficial targets with and without deep-seated part.

  13. Dynamic modeling and optimal joint torque coordination of advanced robotic systems

    NASA Astrophysics Data System (ADS)

    Kang, Hee-Jun

    The development is documented of an efficient dynamic modeling algorithm and the subsequent optimal joint input load coordination of advanced robotic systems for industrial application. A closed-form dynamic modeling algorithm for the general closed-chain robotic linkage systems is presented. The algorithm is based on the transfer of system dependence from a set of open chain Lagrangian coordinates to any desired system generalized coordinate set of the closed-chain. Three different techniques for evaluation of the kinematic closed chain constraints allow the representation of the dynamic modeling parameters in terms of system generalized coordinates and have no restriction with regard to kinematic redundancy. The total computational requirement of the closed-chain system model is largely dependent on the computation required for the dynamic model of an open kinematic chain. In order to improve computational efficiency, modification of an existing open-chain KIC based dynamic formulation is made by the introduction of the generalized augmented body concept. This algorithm allows a 44 pct. computational saving over the current optimized one (O(N4), 5995 when N = 6). As means of resolving redundancies in advanced robotic systems, local joint torque optimization is applied for effectively using actuator power while avoiding joint torque limits. The stability problem in local joint torque optimization schemes is eliminated by using fictitious dissipating forces which act in the necessary null space. The performance index representing the global torque norm is shown to be satisfactory. In addition, the resulting joint motion trajectory becomes conservative, after a transient stage, for repetitive cyclic end-effector trajectories. The effectiveness of the null space damping method is shown. The modular robot, which is built of well defined structural modules from a finite-size inventory and is controlled by one general computer system, is another class of evolving

  14. Optimal Dynamics of Intermittent Water Supply

    NASA Astrophysics Data System (ADS)

    Lieb, Anna; Wilkening, Jon; Rycroft, Chris

    2014-11-01

    In many urban areas of the developing world, piped water is supplied only intermittently, as valves direct water to different parts of the water distribution system at different times. The flow is transient, and may transition between free-surface and pressurized, resulting in complex dynamical features with important consequences for water suppliers and users. These consequences include degradation of distribution system components, compromised water quality, and inequitable water availability. The goal of this work is to model the important dynamics and identify operating conditions that mitigate certain negative effects of intermittent water supply. Specifically, we will look at valve parameters occurring as boundary conditions in a network model of transient, transition flow through closed pipes. Optimization will be used to find boundary values to minimize pressure gradients and ensure equitable water availability.

  15. Analysis and Optimization of Pulse Dynamics for Magnetic Stimulation

    PubMed Central

    Goetz, Stefan M.; Truong, Cong Nam; Gerhofer, Manuel G.; Peterchev, Angel V.; Herzog, Hans-Georg; Weyh, Thomas

    2013-01-01

    Magnetic stimulation is a standard tool in brain research and has found important clinical applications in neurology, psychiatry, and rehabilitation. Whereas coil designs and the spatial field properties have been intensively studied in the literature, the temporal dynamics of the field has received less attention. Typically, the magnetic field waveform is determined by available device circuit topologies rather than by consideration of what is optimal for neural stimulation. This paper analyzes and optimizes the waveform dynamics using a nonlinear model of a mammalian axon. The optimization objective was to minimize the pulse energy loss. The energy loss drives power consumption and heating, which are the dominating limitations of magnetic stimulation. The optimization approach is based on a hybrid global-local method. Different coordinate systems for describing the continuous waveforms in a limited parameter space are defined for numerical stability. The optimization results suggest that there are waveforms with substantially higher efficiency than that of traditional pulse shapes. One class of optimal pulses is analyzed further. Although the coil voltage profile of these waveforms is almost rectangular, the corresponding current shape presents distinctive characteristics, such as a slow low-amplitude first phase which precedes the main pulse and reduces the losses. Representatives of this class of waveforms corresponding to different maximum voltages are linked by a nonlinear transformation. The main phase, however, scales with time only. As with conventional magnetic stimulation pulses, briefer pulses result in lower energy loss but require higher coil voltage than longer pulses. PMID:23469168

  16. Optimal perturbations of a finite-width mixing layer near the trailing edge

    NASA Astrophysics Data System (ADS)

    Gumbart, James C.; Rabchuk, James

    2002-03-01

    The trailing edge of a surface separating two fluid flows can act as an efficient receptor for acoustic or other disturbances. The incident wave energy is converted by a linear mechanism into incipient flow instabilities which lead further downstream to the transition to turbulence. Understanding this process is essential for analyzing feedback loops and other resonances which can cause unwanted structural vibrations in the surface material or directed acoustic emissions from the mixing region. Previously, the modes of instability in a finite-width mixing layer near the trailing edge were studied as a function of frequency by assuming that vorticity was continually being introduced into the flow at the trailing edge by the forcing field. It was found that the initial amplitude of the growing instability mode was a sharply decreasing function of forcing frequency, and that the initial amplitude was a minimum for the frequency at which the rate of instability growth was a maximum^1. This result has led to a study of the adjoint equation for the perturbation stream function, whose eigensolutions are known to be associated with the optimal perturbation field for the frequency of forcing leading to the greatest instability growth downstream. We have obtained these solutions for a piecewise linear velocity profile near the trailing edge using group-theoretic techniques and have shown that they are indeed optimal. We have also analyzed the nature of the physical forcing field that might produce these optimal perturbations. ^1 Rabchuk, J.A., July 2000, Physics of Fluids.

  17. Focusing light through dynamical samples using fast continuous wavefront optimization.

    PubMed

    Blochet, B; Bourdieu, L; Gigan, S

    2017-12-01

    We describe a fast continuous optimization wavefront shaping system able to focus light through dynamic scattering media. A micro-electro-mechanical system-based spatial light modulator, a fast photodetector, and field programmable gate array electronics are combined to implement a continuous optimization of a wavefront with a single-mode optimization rate of 4.1 kHz. The system performances are demonstrated by focusing light through colloidal solutions of TiO 2 particles in glycerol with tunable temporal stability.

  18. Optimal sensor placement for leak location in water distribution networks using genetic algorithms.

    PubMed

    Casillas, Myrna V; Puig, Vicenç; Garza-Castañón, Luis E; Rosich, Albert

    2013-11-04

    This paper proposes a new sensor placement approach for leak location in water distribution networks (WDNs). The sensor placement problem is formulated as an integer optimization problem. The optimization criterion consists in minimizing the number of non-isolable leaks according to the isolability criteria introduced. Because of the large size and non-linear integer nature of the resulting optimization problem, genetic algorithms (GAs) are used as the solution approach. The obtained results are compared with a semi-exhaustive search method with higher computational effort, proving that GA allows one to find near-optimal solutions with less computational load. Moreover, three ways of increasing the robustness of the GA-based sensor placement method have been proposed using a time horizon analysis, a distance-based scoring and considering different leaks sizes. A great advantage of the proposed methodology is that it does not depend on the isolation method chosen by the user, as long as it is based on leak sensitivity analysis. Experiments in two networks allow us to evaluate the performance of the proposed approach.

  19. Optimal Sensor Placement for Leak Location in Water Distribution Networks Using Genetic Algorithms

    PubMed Central

    Casillas, Myrna V.; Puig, Vicenç; Garza-Castañón, Luis E.; Rosich, Albert

    2013-01-01

    This paper proposes a new sensor placement approach for leak location in water distribution networks (WDNs). The sensor placement problem is formulated as an integer optimization problem. The optimization criterion consists in minimizing the number of non-isolable leaks according to the isolability criteria introduced. Because of the large size and non-linear integer nature of the resulting optimization problem, genetic algorithms (GAs) are used as the solution approach. The obtained results are compared with a semi-exhaustive search method with higher computational effort, proving that GA allows one to find near-optimal solutions with less computational load. Moreover, three ways of increasing the robustness of the GA-based sensor placement method have been proposed using a time horizon analysis, a distance-based scoring and considering different leaks sizes. A great advantage of the proposed methodology is that it does not depend on the isolation method chosen by the user, as long as it is based on leak sensitivity analysis. Experiments in two networks allow us to evaluate the performance of the proposed approach. PMID:24193099

  20. A mixed integer linear programming model to reconstruct phylogenies from single nucleotide polymorphism haplotypes under the maximum parsimony criterion

    PubMed Central

    2013-01-01

    Background Phylogeny estimation from aligned haplotype sequences has attracted more and more attention in the recent years due to its importance in analysis of many fine-scale genetic data. Its application fields range from medical research, to drug discovery, to epidemiology, to population dynamics. The literature on molecular phylogenetics proposes a number of criteria for selecting a phylogeny from among plausible alternatives. Usually, such criteria can be expressed by means of objective functions, and the phylogenies that optimize them are referred to as optimal. One of the most important estimation criteria is the parsimony which states that the optimal phylogeny T∗for a set H of n haplotype sequences over a common set of variable loci is the one that satisfies the following requirements: (i) it has the shortest length and (ii) it is such that, for each pair of distinct haplotypes hi,hj∈H, the sum of the edge weights belonging to the path from hi to hj in T∗ is not smaller than the observed number of changes between hi and hj. Finding the most parsimonious phylogeny for H involves solving an optimization problem, called the Most Parsimonious Phylogeny Estimation Problem (MPPEP), which is NP-hard in many of its versions. Results In this article we investigate a recent version of the MPPEP that arises when input data consist of single nucleotide polymorphism haplotypes extracted from a population of individuals on a common genomic region. Specifically, we explore the prospects for improving on the implicit enumeration strategy of implicit enumeration strategy used in previous work using a novel problem formulation and a series of strengthening valid inequalities and preliminary symmetry breaking constraints to more precisely bound the solution space and accelerate implicit enumeration of possible optimal phylogenies. We present the basic formulation and then introduce a series of provable valid constraints to reduce the solution space. We then prove