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1

New algorithms for mixed-integer dynamic optimization

Mixed-integer dynamic optimization (MIDO) problems arise in chemical engineering whenever discrete and continuous decisions are to be made for a system described by a transient model. Areas of application include integrated design and control, synthesis of reactor networks, reduction of kinetic mechanisms and optimization of hybrid systems. This article presents new formulations and algorithms for solving MIDO problems. The algorithms

Vikrant Bansal; Vassilis Sakizlis; Roderick Ross; John D. Perkins; Efstratios N. Pistikopoulos

2003-01-01

2

Optimizing nuclear power plant refueling with mixed-integer programming

The problem addressed here is scheduling the shutdown for refueling and maintenance of nuclear power plants. The models have up to four reactors requiring of the order of five shutdowns each over a five-year time horizon. The resulting mixed-integer program is large and complex with interesting structure. We show good results using a mixed-integer optimizer taking advantage of a strong

Fabrice Fourcade; Ellis Johnson; Mourad Bara; Philippe Cortey-Dumont

1997-01-01

3

The lack of available techniques prompted the development of a mixed integer model to optimize the scheduling of equipment and the distribution of overburden in a typical mountaintop removal operation. Using this format, a (0-1) integer model and transportation model were constructed to determine the optimal equipment schedule and optimal overburden distribution, respectively. To solve this mixed integer program, the

G. V. R

1987-01-01

4

The lack of available techniques prompted the development of a mixed integer model to optimize the scheduling of equipment and the distribution of overburden in a typical mountaintop removal operation. Using this format, a (0-1) integer model and transportation model were constructed to determine the optimal equipment schedule and optimal overburden distribution, respectively. To solve this mixed integer program, the model was partitioned into its binary and real-valued components. Each problem was successively solved and their values added to form estimates of the value of the mixed integer program. Optimal convergence was indicated when the difference between two successive estimates satisfied some pre-specific accuracy value. The performance of the mixed integer model was tested against actual field data to determine its practical applications. To provide the necessary input information, production data was obtained from a single seam, mountaintop removal operation located in the Appalachian coal field. As a means of analyzing the resultant equipment schedule, the total idle time was calculated for each machine type and each lift location. Also, the final overburden assignments were analyzed by determining the distribution of spoil material for various overburden removal productivities. Subsequent validation of the mixed integer model was conducted in two distinct areas. The first dealt with changes in algorithmic data and their effects on the optimality of the model. The second area concerned variations in problem structure, specifically those dealing with changes in problem size and other user-inputed values such as equipment productivities or required reclamation.

Goodman, G.V.R.

1987-01-01

5

Constrained optimal power flow by mixed-integer particle swarm optimization

This paper presents an efficient mixed-integer particle swarm optimization (MIPSO) for solving the constrained optimal power flow (OPF) with a mixture of continuous and discrete control variables and discontinuous fuel cost functions. In the MIPSO-based method, the individual that contains the real-value mixture of continuous and discrete control variables is defined, two mutation schemes are proposed to deal with the

Zwe-lee Gaing

2005-01-01

6

Optimal Placement of SVC Based on Line Flow Base Equation Using Mixed Integer Nonlinear Programming

In this paper a novel approach to determine optimal location, number and initial compensation level of the SVC in a power system is presented. Mixed Integer Nonlinear Programming (MINLP) is used for this purpose as a useful technique for combinatorial optimization over integers and variables. This technique can provide a fast approach as well as high computational efficiency even in

Reza Etemad; Reza Navabi; H. A. Shayanfar

2010-01-01

7

An important advantage of genetic algorithms (GAs) are their ease of use, their wide applicability, and their good performance for a wide range of different problems. GAs are able to find good solutions for many problems even if the problem is complicated and its properties are not well known. In contrast, classical optimization approaches like linear programming or mixed integer

Thomas Butter; Franz Rothlauf; Jorn Grahl; Tobias Hildenbrand; Jens Arndt

8

A coupled gradient network approach for static and temporal mixed integer optimization

The purpose of this work is to utilize the ideas of artificial neural networks to propose new solution methods for a class of constrained mixed-integer optimization problems. These new solution methods are more suitable to parallel implementation than the usual sequential methods of mathematical programming. Another attractive feature of the proposed approach is that some mechanisms of global search may

Paul Benedict Watta

1994-01-01

9

A novel optimization methodology is proposed for the design of transmission line grounding systems, taking into account technical and economical considerations. The grounding systems design of transmission lines is stated as a mixed-integer linear programming problem, in terms of the construction characteristics, and the particular requirements of the tower grounding schemes at the supports of each different line sections in

H. M. Khodr

2009-01-01

10

Hybrid System Analysis and Control via Mixed Integer Optimization

The paper discusses a framework for modeling, analyzing and controlling systems whose behavior is governed by interde- pendent physical laws, logic rules, and operating constraints, denoted as Mixed Logical Dynamical (MLD) systems. They are described by linear dynamic equations subject to linear inequalities involving real and integer variables. MLD models are equivalent to various other system descriptions like Piece Wise

Manfred Morari

11

Mixed integer programming approach to optimal short-term unit commitment for hydropower systems

Unit commitment problem is a complex decision-making process which involves the scheduling of generators over a set of time\\u000a periods to satisfy system load demand, water demand, system reliability, operational, and security constraints. Mathematically,\\u000a this is a nonlinear, nonconvex, high dimensional, and large-scale optimization problem over mixed integer variables. Additionally,\\u000a for a short-term unit commitment problem such as hourly or

Jaeeung Yi

1998-01-01

12

Optimal VAr planning by approximation method for recursive mixed-integer linear programming

The authors propose an algorithm for solving reactive power planning problems. The optimization approach is based on a recursive mixed-integer programming technique using an approximation method. A fundamental feature of this algorithm is that the number of capacitor or reactor units can be treated as a discrete variable in solving large-scale VAr (volt-ampere reactive) planning problems. Numerical results have verified

K. Aoki; M. Fan; A. Nishikori

1988-01-01

13

Security-constrained optimal power flow by mixed-integer genetic algorithm with arithmetic operators

This paper presents an efficient real-coded mixed-integer genetic algorithm (MIGA) for solving non-convex optimal power flow (OPF) problems with considering transmission security and bus voltage constraints for practical application. In the MIGA method, the individual is the real-coded representation that contains a mixture of continuous and discrete control variables, and two arithmetic crossover and mutation schemes are proposed to deal

Zwe-Lee Gaing; Rung-Fang Chang

2006-01-01

14

Real-coded mixed-integer genetic algorithm for constrained optimal power flow

This paper presents an efficient real-coded mixed-integer genetic algorithm (MIGA) for solving non-convex optimal power flow (OPF) problems. In the MIGA method, the individual is the real-coded representation that contains a mixture of continuous and discrete control variables, and two arithmetic mutation schemes are proposed to deaf with continuous\\/discrete control variables, respectively. Simultaneously, because the length of the individual is

Zwe-Lee Gaing; Hou-Sheng Huang

2004-01-01

15

Optimization of a wood dryer kiln using the mixed integer programming technique: A case study

When wood is to be utilized as a raw material for furniture, buildings, etc., it must be dried from approximately 100% to 6% moisture content. This is achieved at least partly in a drying kiln. Heat for this purpose is provided by electrical means, or by steam from boilers fired with wood chips or oil. By making a close examination of monitored values from an actual drying kiln it has been possible to optimize the use of steam and electricity using the so called mixed integer programming technique. Owing to the operating schedule for the drying kiln it has been necessary to divide the drying process in very short time intervals, i.e., a number of minutes. Since a drying cycle takes about two or three weeks, a considerable mathematical problem is presented and this has to be solved.

Gustafsson, S.I. [Inst. of Tech., Linkoeping (Sweden). IKP Wood Science Technology and Energy Systems

1999-07-01

16

NASA Astrophysics Data System (ADS)

In this paper thermo-chemical simulation of the pultrusion process of a composite rod is first used as a validation case to ensure that the utilized numerical scheme is stable and converges to results given in literature. Following this validation case, a cylindrical die block with heaters is added to the pultrusion domain of a composite part and thermal contact resistance (TCR) regions at the die-part interface are defined. Two optimization case studies are performed on this new configuration. In the first one, optimal die radius and TCR values are found by using a hybrid genetic algorithm based on a sequential combination of a genetic algorithm (GA) and a local search technique to fit the centerline temperature of the composite with the one calculated in the validation case. In the second optimization study, the productivity of the process is improved by using a mixed integer genetic algorithm (MIGA) such that the total number of heaters is minimized while satisfying the constraints for the maximum composite temperature, the mean of the cure degree at the die exit and the pulling speed.

Baran, Ismet; Tutum, Cem C.; Hattel, Jesper H.

2013-08-01

17

Current inverse treatment planning methods that optimize both catheter positions and dwell times in prostate HDR brachytherapy use surrogate linear or quadratic objective functions that have no direct interpretation in terms of dose-volume histogram (DVH) criteria, do not result in an optimum or have long solution times. We decrease the solution time of the existing linear and quadratic dose-based programming models (LP and QP, respectively) to allow optimizing over potential catheter positions using mixed integer programming. An additional average speed-up of 75% can be obtained by stopping the solver at an early stage, without deterioration of the plan quality. For a fixed catheter configuration, the dwell time optimization model LP solves to optimality in less than 15 s, which confirms earlier results. We propose an iterative procedure for QP that allows us to prescribe the target dose as an interval, while retaining independence between the solution time and the number of dose calculation points. This iterative procedure is comparable in speed to the LP model and produces better plans than the non-iterative QP. We formulate a new dose-volume-based model that maximizes V(100%) while satisfying pre-set DVH criteria. This model optimizes both catheter positions and dwell times within a few minutes depending on prostate volume and number of catheters, optimizes dwell times within 35 s and gives better DVH statistics than dose-based models. The solutions suggest that the correlation between the objective value and the clinical plan quality is weak in the existing dose-based models. PMID:23363622

Gorissen, Bram L; den Hertog, Dick; Hoffmann, Aswin L

2013-01-31

18

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

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 to draw up the requirements of a public tender. The authors advocate this approach to address similar problems within the field of Health Services to improve the efficiency and the effectiveness of the processes involved. Problems such as those in operation rooms or emergency rooms, where the availability of a large amount of material is critical are eligible to be dealt with in a simmilar manner.

2009-01-01

19

This paper presents a new framework for traffic flow control based on an integrated model description by means of a hybrid dynamical system. The geometrical information on the traffic network is characterized by a hybrid Petri net (HPN). Then, the algebraic behavior of the traffic flow is transformed into a mixed logical dynamical system (MLDS) form to introduce an optimization

Youngwoo Kim; Tatsuya Kato; Shigeru Okuma; Tatsuo Narikiyo

2008-01-01

20

Forestry production and logistics planning: an analysis using mixed-integer programming

This article presents a mathematical model for the problem of production and logistics in the forest industry. Specifically, a dynamic model of mixed-integer programming was formulated to solve three common problems in the forest sector: forest production, forest facilities location and forest freight distribution. The implemented mathematical model allows the strategic selection of the optimal location and size of a

Juan J. Troncoso; Rodrigo A. Garrido

2005-01-01

21

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

National Technical Information Service (NTIS)

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, ...

J. V. Montoya S. Rathinam W. A. Malik Z. P. Wood

2010-01-01

22

Mixed-integer multiperiod model for the planning of oilfield production

In this paper we describe three mixed integer multiperiod optimization models of varying complexity for the oil production planning in the wells of an oil reservoir. The problem considers fixed topology and is concerned with the decisions involving the oil production profiles and operation\\/shut in times of the wells in each time period. We assume nonlinear behavior for the well

A. Ortõ ´ z-Gomez; V. Rico-Ramirez; S. Hernandez-Castro

23

Mixed-integer multiperiod model for the planning of oilfield production

In this paper we describe three mixed integer multiperiod optimization models of varying complexity for the oil production planning in the wells of an oil reservoir. The problem considers fixed topology and is concerned with the decisions involving the oil production profiles and operation\\/shut in times of the wells in each time period. We assume nonlinear behavior for the well

A. Ort??z-Gómez; V. Rico-Ramirez; S. Hernández-Castro

2002-01-01

24

A new mixed integer programming formulation for facility layout design using flexible bays

Abstract This paper presents a mixed-integer programming,formulation to find optimal solutions for the block layout problem with unequal departmental areas arranged in flexible bays. The nonlinear department area constraints are modeled,in a continuous plane without using any surrogate constraints. The formulation is extensively tested on problems,from the literature. © 2005 Elsevier B.V. All rights reserved. Keywords: Facility design; Facility layout;

Abdullah Konak; Sadan Kulturel-konak; Bryan A. Norman; Alice E. Smith

2006-01-01

25

A dynamic optimization approach for nonrenewable energy resources management under uncertainty

This paper introduces an integrated dynamic optimization approach for nonrenewable energy (NRE) resources management under uncertainty. A hybrid inexact chance-constrained mixed-integer linear programming (ICCMILP) method is proposed, with an objective of maximizing economic return under constraints of resources availability and environmental regulations. In its solution process, the ICCMILP is transformed into two deterministic submodels, which correspond to the upper and

L Liu; G. H Huang; G. A Fuller; A Chakma; H. C Guo

2000-01-01

26

Optimization models for the dynamic facility location and allocation problem

The design of logistic distribution systems is one of the most critical and strategic issues in industrial facility management. The aim of this study is to develop and apply innovative mixed integer programming optimization models to design and manage dynamic (i.e. multi-period) multi-stage and multi-commodity location allocation problems (LAP). LAP belong to the NP-hard complexity class of decision problems, and

Riccardo Manzini; Elisa Gebennini

2008-01-01

27

Parametric Mixed Integer Programming: An Application to Solid Waste Management

A method is developed for carrying out parametric analysis on a mixed integer linear program (MILP) as either objective function coefficients or right-hand-side values of the constraints are varied continuously. The method involves solving MILPs at point values of the parameters of variation and joining the results by LP parametric analysis. The procedure for parametric analysis on the objective function

Larry Jenkins

1982-01-01

28

Integer and Mixed-Integer Programming Models: General Properties.

National Technical Information Service (NTIS)

It is well-known that mixed-integer formulations can be used to model important classes of non-convex functions such as fixed-charge functions and linear economy-of-scale cost functions. The purpose of the paper is to formulate a rigorous definition of a ...

R. R. Meyer

1973-01-01

29

We consider a version of the total flow time single machine scheduling problem where uncertainty about processing times is\\u000a taken into account. Namely an interval of equally possible processing times is considered for each job, and optimization is\\u000a carried out according to a robustness criterion. We propose the first mixed integer linear programming formulation for the\\u000a resulting optimization problem and

Roberto Montemanni

2007-01-01

30

PySP : modeling and solving stochastic mixed-integer programs in Python.

Although stochastic programming is a powerful tool for modeling decision-making under uncertainty, various impediments have historically prevented its widespread use. One key factor involves the ability of non-specialists to easily express stochastic programming problems as extensions of deterministic models, which are often formulated first. A second key factor relates to the difficulty of solving stochastic programming models, particularly the general mixed-integer, multi-stage case. Intricate, configurable, and parallel decomposition strategies are frequently required to achieve tractable run-times. We simultaneously address both of these factors in our PySP software package, which is part of the COIN-OR Coopr open-source Python project for optimization. To formulate a stochastic program in PySP, the user specifies both the deterministic base model and the scenario tree with associated uncertain parameters in the Pyomo open-source algebraic modeling language. Given these two models, PySP provides two paths for solution of the corresponding stochastic program. The first alternative involves writing the extensive form and invoking a standard deterministic (mixed-integer) solver. For more complex stochastic programs, we provide an implementation of Rockafellar and Wets Progressive Hedging algorithm. Our particular focus is on the use of Progressive Hedging as an effective heuristic for approximating general multi-stage, mixed-integer stochastic programs. By leveraging the combination of a high-level programming language (Python) and the embedding of the base deterministic model in that language (Pyomo), we are able to provide completely generic and highly configurable solver implementations. PySP has been used by a number of research groups, including our own, to rapidly prototype and solve difficult stochastic programming problems.

Woodruff, David L. (University of California, Davis); Watson, Jean-Paul

2010-08-01

31

NASA Astrophysics Data System (ADS)

An algorithm for the computation of mass conservative dynamically equivalent chemical reaction network structures is proposed in this paper. The algorithm is formulated in an optimization-based framework as a mixed-integer linear programming problem.

Rudan, János; Szederkényi, Gábor; Hangos, Katalin M.

2013-10-01

32

The reliability-redundancy allocation problem can be approached as a mixed-integer programming problem. It has been solved by using optimization techniques such as dynamic programming, integer programming, and mixed-integer nonlinear programming. On the other hand, a broad class of meta-heuristics has been developed for reliability-redundancy optimization. Recently, a new meta-heuristics called harmony search (HS) algorithm has emerged. HS was conceptualized using

Leandro dos Santos Coelho; Diego Luis de Andrade Bernert; Viviana Cocco Mariani

2009-01-01

33

Integrated control and process design during optimal polymer grade transition operations

In this work we address the simultaneous process control and design problem of polymerization reactors during dynamic grade transition operation. The problem is cast as a Mixed-Integer Dynamic Optimization (MIDO) formulation and, by using the full discretization approach for solving dynamic optimization problems [Kameswaran, S., & Biegler, L. T. (2006). Simultaneous dynamic optimization strategies: Recent advances and challenges. Computers &

Antonio Flores-tlacuahuac; Lorenz T. Biegler

2008-01-01

34

Learning Oncogenetic Networks by Reducing to Mixed Integer Linear Programming

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.

Shahrabi Farahani, Hossein; Lagergren, Jens

2013-01-01

35

A mixed integer linear programming model for dynamic route guidance

One of the major challenges facing ITS (Intelligent Transportation Systems) today is to offer route guidance to vehicular traffic so as to reduce trip time experienced. In a cooperative route guidance system, the problem becomes one of assigning routes to vehicles departing at given times from a set of origins to a set of destinations so as to minimize the

David E. Kaufman; Jason Nonis; Robert L. Smith

1998-01-01

36

An interval-parameter fuzzy-stochastic semi-infinite mixed-integer linear programming (IFSSIP) method is developed for waste\\u000a management under uncertainties. The IFSSIP method integrates the fuzzy programming, chance-constrained programming, integer\\u000a programming and interval semi-infinite programming within a general optimization framework. The model is applied to a waste\\u000a management system with three disposal facilities, three municipalities, and three periods. Compared with the previous methods,\\u000a IFSSIP

P. Guo; G. H. Huang; L. He; H. L. Li

2009-01-01

37

A Mixed-Integer LP Procedure for the Analysis of Electric Grid Security Under Disruptive Threat

This paper presents a solution procedure for the mixed-integer bilevel programming model of the electric grid security under disruptive threat problem, here concisely denoted by (ST-MIBLP), that was recently reported. Using results from linear programming theory and some basic linearization of products of binary–binary or binary–continuous variables, we recast (ST-MIBLP) into a standard (one-level) mixed-integer linear program (ST-MILP) with no

Alexis L. Motto; José M. Arroyo; Francisco D. Galiana

2005-01-01

38

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. PMID:24032353

Baran, Richard; Northen, Trent R

2013-09-27

39

In this contribution, we consider mixed-integer nonlinear programming problems subject to differential-algebraic constraints. This class of problems arises frequently in process design, and the particular case of integrated process and control system design is considered. Since these problems are frequently non-convex, local optimization techniques usually fail to locate the global solution. Here, we propose a global optimization algorithm, based on

Oliver Exler; Luis T. Antelo; Jose A. Egea; Antonio A. Alonso; Julio R. Banga

2008-01-01

40

In this study, an interval-parameter two-stage mixed integer linear programming (ITMILP) model is developed for supporting long-term planning of waste management activities in the City of Regina. In the ITMILP, both two-stage stochastic programming and interval linear programming are introduced into a general mixed integer linear programming framework. Uncertainties expressed as not only probability density functions but also discrete intervals can be reflected. The model can help tackle the dynamic, interactive and uncertain characteristics of the solid waste management system in the City, and can address issues concerning plans for cost-effective waste diversion and landfill prolongation. Three scenarios are considered based on different waste management policies. The results indicate that reasonable solutions have been generated. They are valuable for supporting the adjustment or justification of the existing waste flow allocation patterns, the long-term capacity planning of the City's waste management system, and the formulation of local policies and regulations regarding waste generation and management. PMID:16678336

Li, Y P; Huang, G H

2006-05-05

41

We proposed a mixed-integer program to model the management of C. trachomatis infections in women visiting publicly funded family planning clinics. We intended to maximize the number of infected women cured of C. trachomatis infections. The model incorporated screening, re-screening, and treatment options for three age groups with respective age-specific C. trachomatis infection and re-infection rates, two possible test assays, and two possible treatments. Our results showed the total budget had a great impact on the optimal strategy incorporating screening coverage, test selection, and treatment. At any budget level, the strategy that used a relatively small per-patient budget increase to re-screen all women who tested positive 6 months earlier always resulted in curing more infected women and more cost-saving than the strategy that was optimal under the condition of not including a re-screening option. PMID:15152978

Tao, Guoyu; Abban, Bartholomew K; Gift, Thomas L; Chen, Guantao; Irwin, Kathleen L

2004-05-01

42

A Mixed-Integer Programming Model for Pollution Trading

Pollution abatement-related decisions are increasingly important for industry. Pollution trading is an approach to environmental protection that uses market based mechanisms to efficiently allocate emission or pollutant reductions among different sources. This work describes an optimization model intended to provide industries with a guide for making optimal environmental decisions under the flexibility added by a trading strategy. To assess the

Vicente Rico-Ramirez; Francisco Lopez-Villarreal; Salvador Hernandez-Castro; Urmila M. Diwekar

2011-01-01

43

Safety of a decentralized scheme for free-flight ATMS using mixed integer linear programming

In this paper we consider policies for free-flight management of air traffic. We consider instantaneous and bounded heading angle deviation as conflict avoidance maneuvers. The corresponding model, resulting in a mixed integer linear programming (MILP) problem allow to solve both conflict detection and conflict resolution problems. The developed algorithm proved successful in a centralized implementation with a large number of

Lucia Pallottino; Antonio Bicchi; Stefania Pancanti

2002-01-01

44

Mixed Integer Linear Programming in Process Scheduling: Modeling, Algorithms, and Applications

This paper reviews the advances of mixed-integer linear programming (MILP) based approaches for the scheduling of chemical\\u000a processing systems. We focus on the short-term scheduling of general network represented processes. First, the various mathematical\\u000a models that have been proposed in the literature are classified mainly based on the time representation. Discrete-time and\\u000a continuous-time models are presented along with their strengths

Christodoulos A. Floudas; Xiaoxia Lin

2005-01-01

45

This brief addresses the problem of the optimal control and scheduling of networked control systems over limited bandwidth deterministic networks. Multivariable linear systems subject to communication constraints are modeled in the mixed logical dynamical (MLD) framework. The translation of the MLD model into the mixed integer quadratic programming (MIQP) formulation is described. This formulation allows the solving of the optimal

M. E. M. B. Gaid; A. Cela; Y. Hamam

2006-01-01

46

Adaptive hybrid predictive control for a combined cycle power plant optimization

SUMMARY The design and development of an adaptive hybrid predictive controller for the optimization of a real combined cycle power plant (CCPP) are presented. The real plant is modeled as a hybrid system, i.e. logical conditions and dynamic behavior are used in one single modeling framework. Start modes, minimum up\\/down times and other logical features are represented using mixed integer

A. Cipriano; R. Zúñiga

2008-01-01

47

This work presents a method of finding near global optima to minimum-time trajectory generation problem for systems that would be linear if it were not for the presence of Coloumb friction. The required final state of the system is assumed to be maintainable by the system, and the input bounds are assumed to be large enough so that they can overcome the maximum static Coloumb friction force. Other than the previous work for generating minimum-time trajectories for non redundant robotic manipulators for which the path in joint space is already specified, this work represents, to the best of the authors' knowledge, the first approach for generating near global optima for minimum-time problems involving a nonlinear class of dynamic systems. The reason the optima generated are near global optima instead of exactly global optima is due to a discrete-time approximation of the system (which is usually used anyway to simulate such a system numerically). The method closely resembles previous methods for generating minimum-time trajectories for linear systems, where the core operation is the solution of a Phase I linear programming problem. For the nonlinear systems considered herein, the core operation is instead the solution of a mixed integer linear programming problem.

DRIESSEN,BRIAN; SADEGH,NADER

2000-04-25

48

An Application of Parametric Mixed-Integer Linear Programming to Hydropower Development

NASA Astrophysics Data System (ADS)

The problem consists in selecting the sites on the river where reservoirs and hydroelectric power plants are to be built and then determining the type and size of the projected installations. The solution obviously depends on the amount of money the utility is willing to invest, which itself is a function of what the new installations will produce. It is therefore necessary to solve the problem for all possible amounts of firm energy produced, since it is not known at the outset which production level the utility will select. This is done in the paper by a parametric mixed-integer linear programming (MILP) method whose efficiency derives from the fact that the branch-and-bound algorithm for selecting the sites to be developed (and consuming most of the computer time) is solved a minimum number of times. Between the points where the MILP problem is solved, LP parametric analysis is applied.

Turgeon, André

1987-03-01

49

The last few years have been a thrilling time for the commercial application of mixed integer programming. The technology has gone through an inflection point. Just a few years ago, MIP was viewed as a temptingly powerful modeling paradigm that would consistently disappoint in practice. In constrast, in the last few years MIP has become a vital capability that powers

Robert Bixby; Edward Rothberg

2007-01-01

50

National Technical Information Service (NTIS)

The Hybrid Projected Gradient-Evolutionary Search Algorithm (HPGES) algorithm uses a specially designed evolutionary-based global search strategy to efficiently create candidate solutions in the solution space. A local projection-based gradient search alg...

A. Homaifar A. Esterline B. Kimiaghalam

2005-01-01

51

OPTIMIZATION OF A WOOD DRYER KILN USING THE MIXED INTEGER PROGRAMMING TECHNIQUE: A CASE STUDY

When wood is to be utilized as a raw material for furniture, buildings etc., it must be dried from approximately 100% to 6% moisture content. This is achieved at least partly in a drying kiln. Heat for this purpose is provided by electrical means, or by steam from boilers fired with wood chips or oil. By making a close examination

Stig-Inge Gustafsson

1999-01-01

52

Optimization of sensor parameters in programmable logic controller via mixed integer programming

Programmable logic controller (PLC) has been widely used in the industrial control as the controller for the manufacturing system, process control, and so on. This class of control system, in comparison with the standard continuous valued control system, is characterized by its use of low-resolution (typically ON\\/OFF) actuators and sensors. Although several languages for the PLC have been developed, the

Eiji KONAKA; Tatsuya SUZUKI; Shigeru OKUMA

2004-01-01

53

This article proposes a two-stage approach, based on the application of an analytic network process-mixed integer multi-objective programming (ANP-MIMOP) model, to solve the problem of partner selection in agile supply chains (ASCs). A key requirement of an ASC is that its constituents (suppliers, producers, distributors, etc.) can combine and react to fast-changing customer demand as efficiently and effectively as possible.

Chong Wu; David Barnes; Duska Rosenberg; Xinxing Luo

2009-01-01

54

Logic-based solution methods for optimal control of hybrid systems

Combinatorial optimization over continuous and integer variables is a useful tool for solving complex optimal control problems of hybrid dynamical systems formulated in discrete-time. Current approaches are based on mixed-integer linear (or quadratic) programming (MIP), which provides the solution after solving a sequence of relaxed linear (or quadratic) programs. MIP formulations require the translation of the discrete\\/logic part of the

Alberto Bemporad; Nicolò Giorgetti

2006-01-01

55

Mixed-integer nonlinear optimisation approach to coarse-graining biochemical networks

Quantitative modelling and analysis of biochemical networks is challenging because of the inherent complexities and nonlinearities of the system and the limited availability of parameter values. Even if a mathematical model of the network can be developed, the lack of large-scale good-quality data makes accurate estimation of a large number of parameters impossible. Hence, coarse-grained models (CGMs) consisting of essential biochemical mechanisms are more suitable for computational analysis and for studying important systemic functions. The central question in constructing a CGM is which mechanisms should be deemed ‘essential’ and which can be ignored. Also, how should parameter values be defined when data are sparse? A mixed-integer nonlinear-programming (MINLP) based optimisation approach to coarse-graining is presented. Starting with a detailed biochemical model with associated computational details (reaction network and mathematical description) and data on the biochemical system, the structure and the parameters of a CGM can be determined simultaneously. In this optimisation problem, the authors use a genetic algorithm to simultaneously identify parameter values and remove unimportant reactions. The methodology is exemplified by developing two CGMs for the GTPase-cycle module of M1 muscarinic acetylcholine receptor, Gq, and regulator of G protein signalling 4 [RGS4, a GTPase-activating protein (GAP)] starting from a detailed model of 48 reactions. Both the CGMs have only 17 reactions, fit experimental data well and predict, as does the detailed model, four limiting signalling regimes (LSRs) corresponding to the extremes of receptor and GAP concentration. The authors demonstrate that coarse-graining, in addition to resulting in a reduced-order model, also provides insights into the mechanisms in the network. The best CGM obtained for the GTPase cycle also contains an unconventional mechanism and its predictions explain an old problem in pharmacology, the biphasic (bell-shaped) response to certain drugs. The MINLP methodology is broadly applicable to larger and complex (dense) biochemical modules.

Maurya, M.R.; Bornheimer, S.J.; Venkatasubramanian, V.; Subramaniam, S.

2009-01-01

56

For the Department of Veterans Affairs (VA), traumatic brain injury (TBI) is a significant problem facing active duty military personnel, veterans, their families, and caregivers. The VA has designated TBI treatment as one of its physical medicine and rehabilitation special emphasis programs, thereby providing a comprehensive array of treatment services to those military personnel and veterans with TBI. Timely treatment of TBI is critical in achieving maximal recovery, and being in geographical proximity to a medical center with specialized TBI treatment services is a major determinant of whether such treatment is utilized. We present a mixed integer programming model for locating TBI treatment units in the VA. This model was developed for the VA Rehabilitation Strategic Healthcare Group to assist in locating new TBI treatment units. The optimization model assigns TBI treatment units to existing VA medical centers while minimizing the sum of patient treatment costs, patient lodging and travel costs, and the penalty costs associated with foregone treatment revenue and excess capacity utilization. We demonstrate our model with VA TBI admission data from one of the VA's integrated service networks, and discuss the expected service and cost implications for a range of TBI treatment unit location options. PMID:17695136

Côté, Murray J; Syam, Siddhartha S; Vogel, W Bruce; Cowper, Diane C

2007-09-01

57

Greenhouse gas (GHG) emissions from municipal solid waste (MSW) management facilities have become a serious environmental issue. In MSW management, not only economic objectives but also environmental objectives should be considered simultaneously. In this study, a dynamic stochastic possibilistic multiobjective programming (DSPMP) model is developed for supporting MSW management and associated GHG emission control. The DSPMP model improves upon the existing waste management optimization methods through incorporation of fuzzy possibilistic programming and chance-constrained programming into a general mixed-integer multiobjective linear programming (MOP) framework where various uncertainties expressed as fuzzy possibility distributions and probability distributions can be effectively reflected. Two conflicting objectives are integrally considered, including minimization of total system cost and minimization of total GHG emissions from waste management facilities. Three planning scenarios are analyzed and compared, representing different preferences of the decision makers for economic development and environmental-impact (i.e. GHG-emission) issues in integrated MSW management. Optimal decision schemes under three scenarios and different p(i) levels (representing the probability that the constraints would be violated) are generated for planning waste flow allocation and facility capacity expansions as well as GHG emission control. The results indicate that economic and environmental tradeoffs can be effectively reflected through the proposed DSPMP model. The generated decision variables can help the decision makers justify and/or adjust their waste management strategies based on their implicit knowledge and preferences. PMID:23313898

Zhang, Xiaodong; Huang, Gordon

2012-12-28

58

Optimal dynamical characterization of entanglement.

We show that, for experimentally relevant systems, there is an optimal measurement strategy to monitor the time evolution of entanglement under open system dynamics. This suggests an efficient, dynamical characterization of the entanglement of composite, open quantum systems. PMID:17677610

Carvalho, André R R; Busse, Marc; Brodier, Olivier; Viviescas, Carlos; Buchleitner, Andreas

2007-05-07

59

Planning Electric Power Generation: A Nonlinear Mixed Integer Model Employing Benders Decomposition

This paper describes the development and application of an optimization program that is used to help electric utilities plan investments for power generation. For each year over a planning horizon the program determines what types and sizes of generating plants should be constructed, so as to minimize total discounted cost while meeting reliably the system's forecasted demands for electricity. The

F. Noonan; R. J. Giglio

1977-01-01

60

The goal of adaptive sampling in the ocean is to predict the types and locations of additional ocean measurements that would be most useful to collect. Quantitatively, what is most useful is defined by an objective function and the goal is then to optimize this objective under the constraints of the available observing network. Examples of objectives are better oceanic

Namik Kemal Yilmaz; Constantinos Evangelinos; Pierre F. J. Lermusiaux; Nicholas M. Patrikalakis

2008-01-01

61

In road construction, earthwork operations account for about 25% of the construction costs. Existing linear programming models for earthwork optimization are designed to minimize the hauling costs and to balance the earth across the construction site. However, these models do not consider the removal of physical blocks that may influence the earthwork process. As such, current models may result in

Warren L. Hare; Valentin R. Koch; Yves Lucet

2011-01-01

62

This paper describes a new approach for reducing the number of the fitness and constraint function evaluations required by a genetic algorithm (GA) for optimization problems with mixed continuous and discrete design variables. The proposed additions to the GA make the search more eective and rapidly improve the fitness value from generation to generation. The additions involve memory as a

Vladimir B. Gantovnik; Christine Anderson-Cook; Zafer Gurdal; Layne Watson

2005-01-01

63

Test scheduling for core-based systems using mixed-integer linearprogramming

We present optimal solutions to the test schedulingproblem for core-based systems. Given a set of tasks (test sets forthe cores), a set of test resources (e.g., test buses, BIST hardware)and a test access architecture, we determine start times for thetasks such that the total test application time is minimized. Weshow that the test scheduling decision problem is equivalent tothe-processor open

Krishnendu Chakrabarty

2000-01-01

64

Optimization using Extremal Dynamics

NASA Astrophysics Data System (ADS)

We explore a new heuristic for finding high-quality solutions to NP-hard optimization problems which we have recently introduced [see ``Nature's Way of Optimizing," Artificial Intelligence 119, 275-286 (2000) and cond-mat/0010337]. The method, called extremal optimization, is inspired by self-organized criticality, a concept introduced to describe emergent complexity in physical systems. Extremal optimization successively replaces extremely undesirable elements of a single sub-optimal solution with new, random ones. Large fluctuations ensue that efficiently explore many local optima. With only one adjustable parameter, its performance has proved competitive with more elaborate methods, especially near phase transitions which are believed to contain the hardest instances. In particular, extremal optimization is superior to simulated annealing in the partitioning of sparse graphs, it finds the overlap of all ground-states at the phase transition of the 3-coloring problem, and it provides independent confirmation for the ground-state energy of spin glasses, previously obtained with elaborate genetic algorithms.

Boettcher, Stefan

2001-03-01

65

Adaptive critics for dynamic optimization.

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. PMID:20223635

Kulkarni, Raghavendra V; Venayagamoorthy, Ganesh Kumar

2010-02-24

66

Background The estimation of parameter values for mathematical models of biological systems is an optimization problem that is particularly challenging due to the nonlinearities involved. One major difficulty is the existence of multiple minima in which standard optimization methods may fall during the search. Deterministic global optimization methods overcome this limitation, ensuring convergence to the global optimum within a desired tolerance. Global optimization techniques are usually classified into stochastic and deterministic. The former typically lead to lower CPU times but offer no guarantee of convergence to the global minimum in a finite number of iterations. In contrast, deterministic methods provide solutions of a given quality (i.e., optimality gap), but tend to lead to large computational burdens. Results This work presents a deterministic outer approximation-based algorithm for the global optimization of dynamic problems arising in the parameter estimation of models of biological systems. Our approach, which offers a theoretical guarantee of convergence to global minimum, is based on reformulating the set of ordinary differential equations into an equivalent set of algebraic equations through the use of orthogonal collocation methods, giving rise to a nonconvex nonlinear programming (NLP) problem. This nonconvex NLP is decomposed into two hierarchical levels: a master mixed-integer linear programming problem (MILP) that provides a rigorous lower bound on the optimal solution, and a reduced-space slave NLP that yields an upper bound. The algorithm iterates between these two levels until a termination criterion is satisfied. Conclusion The capabilities of our approach were tested in two benchmark problems, in which the performance of our algorithm was compared with that of the commercial global optimization package BARON. The proposed strategy produced near optimal solutions (i.e., within a desired tolerance) in a fraction of the CPU time required by BARON.

2012-01-01

67

Existing algorithms for GPS ambiguity determination can be classified into three categories, i.e. ambiguity resolution in\\u000a the measurement domain, the coordinate domain and the ambiguity domain. There are many techniques available for searching\\u000a the ambiguity domain, such as FARA (Frei and Beutler in Manuscr Geod 15(4):325–356, 1990), LSAST (Hatch in Proceedings of KIS’90, Banff, Canada, pp 299–308, 1990), the modified

Jianqing Cai; Erik W. Grafarend; Congwei Hu

2009-01-01

68

Optimal dynamic detection of explosives

The detection of explosives is a notoriously difficult problem, especially at stand-off distances, due to their (generally) low vapor pressure, environmental and matrix interferences, and packaging. We are exploring optimal dynamic detection to exploit the best capabilities of recent advances in laser technology and recent discoveries in optimal shaping of laser pulses for control of molecular processes to significantly enhance the standoff detection of explosives. The core of the ODD-Ex technique is the introduction of optimally shaped laser pulses to simultaneously enhance sensitivity of explosives signatures while reducing the influence of noise and the signals from background interferents in the field (increase selectivity). These goals are being addressed by operating in an optimal nonlinear fashion, typically with a single shaped laser pulse inherently containing within it coherently locked control and probe sub-pulses. With sufficient bandwidth, the technique is capable of intrinsically providing orthogonal broad spectral information for data fusion, all from a single optimal pulse.

Moore, David Steven [Los Alamos National Laboratory; Mcgrane, Shawn D [Los Alamos National Laboratory; Greenfield, Margo T [Los Alamos National Laboratory; Scharff, R J [Los Alamos National Laboratory; Rabitz, Herschel A [PRINCETON UNIV; Roslund, J [PRINCETON UNIV

2009-01-01

69

An infrastructure for adaptive dynamic optimization

Dynamic optimization is emerging as a promising approach to overcome many of the obstacles of traditional static compilation. But while there are a number of compiler infrastructures for developing static optimizations, there are very few for developing dynamic optimizations. We present a framework for implementing dynamic analyses and optimizations. We provide an interface for building external modules, or clients, for

Derek Bruening; Timothy Garnett; Saman P. Amarasinghe

2003-01-01

70

Trading: An Optimization Approach

Euent trading to manage water pollution holds considerable potential for industries and policy makers alike. This paper proposes an optimization based ap- proach to assist decision making in pollutant trading which is beyond heuristics. The optimization model, formulated as an Mixed Integer Linear Programming (MILP) problem, allows decision makers to incorporate watershed and technology specific information in regulation development. The

Y. Shastri; U. Diwekar; S. Mehrotra

71

Using Annotations to Reduce Dynamic Optimization Time

Dynamic compilation and optimization are widely used in heterogenous computing environments, in which an inter- mediate form of the code is compiled to native code during execution. An important tradeoff exists between the amount of time spent dynamically optimizing the program and the running time of the program. The time to perform dynamic optimizations can cause significant delays during execution

Chandra Krintz; Brad Calder

2000-01-01

72

Code Cache Management Schemes for Dynamic Optimizers

A dynamic optimizer is a software-based system that performs code modifications at runtime, and several such systems have been proposed over the past several years. These systems typically perform optimization on the level of an instruction trace, and most use caching mechanisms to store recently optimized portions of code. Since the dynamic optimizers produce variable-length code traces that are modified

Kim M. Hazelwood; Michael D. Smith

2002-01-01

73

Multiobjective optimization using dynamic neighborhood particle swarm optimization

This paper presents a particle swarm optimization (PSO) algorithm for multiobjective optimization problems. PSO is modified by using a dynamic neighborhood strategy, new particle memory updating, and one-dimension optimization to deal with multiple objectives. Several benchmark cases were tested and showed that PSO could efficiently find multiple Pareto optimal solutions

Xiaohui Hu; Russell C. Eberhart

2002-01-01

74

Multiobjective optimization of dynamic aperture

NASA Astrophysics Data System (ADS)

Dynamic aperture (DA) is one of the key nonlinear properties for a storage ring. Although there have been both analytical and numerical methods to find the aperture, the reverse problem of how to optimize it is still a challenging problem. A general and flexible way of optimizing the DA is highly demanded in accelerator design and operation. In this paper, we discuss the use of multiobjective optimization for DA. First we consider using objective functions based only on numerical tracking results. Data mining of these results demonstrated a correlation between DA and low-order nonlinear driving terms. Next we considered using objective functions which included both numerical tracking results and analytical estimates of low-order nonlinear driving terms. This resulted in faster convergence. The National Synchrotron Light Source II (NSLS-II) lattice was taken as an example to illustrate this method. This multiobjective approach is not limited by particular linear or nonlinear lattice settings, and can also be applied for optimizing other properties of a storage ring.

Yang, Lingyun; Li, Yongjun; Guo, Weiming; Krinsky, Samuel

2011-05-01

75

Static and Dynamic Optimization Models in Agriculture

NSDL National Science Digital Library

Charles Moss, Associate Professor of Food and Resource Economics at the University of Florida developed this web site for his course on optimization models. The course aims to introduce students to classical optimization models, particularly mathematical programming and using optimal control theory to solve dynamic optimization models. The site provides lecture notes, slides from the lectures, assignments and solutions, and computer programs.

76

This work describes a stochastic approach for the optimal placement of sensors in municipal water networks to detect maliciously injected con- taminants. The model minimizes the expected fraction of the population at risk and the cost of the sensors. Our work explicitly includes uncertainties in the attack risk and population density, so that the resulting problem involves optimization under uncertainty.

Vicente Rico-ramírez; Sergio Frausto-hernández; Urmila M. Diwekar; Salvador Hernández-castro

2007-01-01

77

This work describes a stochastic approach for the optimal placement of sensors in municipal water networks to detect maliciously injected contaminants. The model minimizes the expected fraction of the population at risk. Our work explicity includes uncertainties in attack risks and population density, so that the resulting problem involves optimization under uncertainty. In our formulation, we include the number of

Sergio Frausto-Hernández; Urmila M. Diwekar; Salvador Hernández-Castro; Vicente Rico-Ramírez

2005-01-01

78

A software environment for simultaneous dynamic optimization

We describe a software environment for dynamic optimization using the simultaneous approach. Over the past few years, there has been significant development in the formulation of simultaneous dynamic optimization problems, using collocation on finite elements, and in the solution of the resulting large-scale nonlinear programming problem, using powerful barrier NLP solvers. Here we describe the background of the simultaneous approach

Y.-D. Lang; L. T. Biegler

2007-01-01

79

TRACKING CODE DEVELOPMENT FOR BEAM DYNAMICS OPTIMIZATION

Dynamic aperture (DA) optimization with direct particle tracking is a straight forward approach when the computing power is permitted. It can have various realistic errors included and is more close than theoretical estimations. In this approach, a fast and parallel tracking code could be very helpful. In this presentation, we describe an implementation of storage ring particle tracking code TESLA for beam dynamics optimization. It supports MPI based parallel computing and is robust as DA calculation engine. This code has been used in the NSLS-II dynamics optimizations and obtained promising performance.

Yang, L.

2011-03-28

80

Dynamic optimization of artificial lighting in greenhouses

A principle for dynamic optimization of artificial lighting in greenhouses is presented, where the optimization criterion is maximization of the term

E. Heuvelink; H. Challa

1989-01-01

81

Two Characterizations of Optimality in Dynamic Programming

It holds in great generality that a plan is optimal for a dynamic programming problem, if and only if it is 'thrifty' and 'equalizing.' An alternative characterization of an optimal plan, that applies in many economic models, is that the plan must satisfy an appropriate Euler equation and a transversality condition. Here we explore the connections between these two characterizations.

Karatzas, Ioannis, E-mail: ik@math.columbia.ed [Columbia University, Department of Mathematics (United States); Sudderth, William D., E-mail: bill@stat.umn.ed [University of Minnesota, School of Statistics (United States)

2010-06-15

82

Optimizations for Dynamic Inverted Index Maintenance

For free-text search over rapidly evolving corpora, dynamic update of inverted indices is a basic requirement. B-trees are an effective tool in implementing such indices. The Zipfian distribution of postings suggests space and time optimizations unique to this task. In particular, we present two novel optimizations, merge update, which performs better than straight forward block update, and pulsing which significantly

Douglas R. Cutting; Jan O. Pedersen

1990-01-01

83

Simultaneous beam geometry and intensity map optimization in intensity-modulated radiation therapy

Purpose: In current intensity-modulated radiation therapy (IMRT) plan optimization, the focus is on either finding optimal beam angles (or other beam delivery parameters such as field segments, couch angles, gantry angles) or optimal beam intensities. In this article we offer a mixed integer programming (MIP) approach for simultaneously determining an optimal intensity map and optimal beam angles for IMRT delivery.

Eva K.. Lee; Tim Fox; Ian Crocker

2006-01-01

84

Synthesis of nonequilibrium reactive distillation processes by MINLP optimization

A mixed integer nonlinear programming (MINLP) model is presented for synthesizing reactive distillation columns when chemical reaction equilibrium cannot be assured. The MINLP minimizes the total annual cost subject to a rigorous tray-by-tray model. The solution of this MINLP yields the optimal number of trays, the optimal feed rates, and the optimal feed tray locations. The liquid holdup per tray,

Amy R. Ciric; Deyao Gu

1994-01-01

85

Optimal facility layout design

The facility layout problem (FLP) is a fundamental optimization problem encountered in many manufacturing and service organizations. Montreuil introduced a mixed integer programming (MIP) model for FLP that has been used as the basis for several rounding heuristics. However, no further attempt has been made to solve this MIP optimally. In fact, though this MIP only has 2n(n?1) 0–1 variables,

Russell D. Meller; Venkat Narayanan; Pamela H. Vance

1998-01-01

86

Zone-based facilities layout optimization

Combining the best features of both discrete and continuous space representations, and exploiting the notion of space structuring as done in new-generation heuristics, this paper introduces a novel zone-based optimization model for facilities layout design. Preliminary empirical results are presented, with the model solved directly using a commercial mixed integer linear programming solver for selected well known cases. The results

Benoit Montreuil; Edith Brotherton; Suzanne Marcotte

87

Mean field theory for optimal power flow

We present a method based on mean field theory to cope with the mixed nonlinear integer programming, especially with optimal power flow problems involving both continuous and discrete variables, in a more exact manner. That is, we first formulate OPF as a mixed integer programming, and then derive its mean field equations as well as the annealing algorithm, by taking

Luonan Chen; Hideki Suzuki; Kazuo Katou

1997-01-01

88

Nonlinear dynamic response structural optimization using equivalent static loads

It is well known that nonlinear dynamic response optimization using a conventional optimization algorithm is fairly difficult and expensive for the gradient or non-gradient based optimization methods because many nonlinear dynamic analyses are required. Therefore, it is quite difficult to find practical large scale examples with many design variables and constraints for nonlinear dynamic response structural optimization. The equivalent static

Yong-Il Kim; Gyung-Jin Park

2010-01-01

89

Dynamic economic dispatch: feasible and optimal solutions

Dynamic economic dispatch is an extension of the conventional economic dispatch problem that takes into consideration the limits on the ramp rate of the generating units. This paper examines the factors that affect the feasibility and optimality of solutions to this problem. It proposes two new solution methods. The first is guaranteed to find a feasible solution even when the

X. S. Han; H. B. Gooi; Daniel S. Kirschen

2001-01-01

90

Role of controllability in optimizing quantum dynamics

NASA Astrophysics Data System (ADS)

This paper reveals an important role that controllability plays in the complexity of optimizing quantum control dynamics. We show that the loss of controllability generally leads to multiple locally suboptimal controls when gate fidelity in a quantum control system is maximized, which does not happen if the system is controllable. Such local suboptimal controls may attract an optimization algorithm into a local trap when a global optimal solution is sought, even if the target gate can be perfectly realized. This conclusion results from an analysis of the critical topology of the corresponding quantum control landscape, which refers to the gate fidelity objective as a functional of the control fields. For uncontrollable systems, due to SU(2) and SU(3) dynamical symmetries, the control landscape corresponding to an implementable target gate is proven to possess multiple locally optimal critical points, and its ruggedness can be further increased if the target gate is not realizable. These results imply that the optimization of quantum dynamics can be seriously impeded when operating with local search algorithms under these conditions, and thus full controllability is demanded.

Wu, Re-Bing; Hsieh, Michael A.; Rabitz, Herschel

2011-06-01

91

Role of controllability in optimizing quantum dynamics

This paper reveals an important role that controllability plays in the complexity of optimizing quantum control dynamics. We show that the loss of controllability generally leads to multiple locally suboptimal controls when gate fidelity in a quantum control system is maximized, which does not happen if the system is controllable. Such local suboptimal controls may attract an optimization algorithm into a local trap when a global optimal solution is sought, even if the target gate can be perfectly realized. This conclusion results from an analysis of the critical topology of the corresponding quantum control landscape, which refers to the gate fidelity objective as a functional of the control fields. For uncontrollable systems, due to SU(2) and SU(3) dynamical symmetries, the control landscape corresponding to an implementable target gate is proven to possess multiple locally optimal critical points, and its ruggedness can be further increased if the target gate is not realizable. These results imply that the optimization of quantum dynamics can be seriously impeded when operating with local search algorithms under these conditions, and thus full controllability is demanded.

Wu Rebing; Hsieh, Michael A.; Rabitz, Herschel [Department of Automation, Tsinghua University, Beijing, 100084, China and Center for Quantum Information Science and Technology, TNList, Beijing, 100084 (China); Department of Chemistry and Center for Quantum Information Science and Technology, University of Southern California, Los Angeles, California 90025 (United States); Department of Chemistry, Princeton University, Princeton, New Jersey 08544 (United States)

2011-06-15

92

A Dynamic Optimization Approach for Power Generation Planning under Uncertainty

In this study, an integrated fuzzy possibilistic-joint probabilistic mixed-integer programming (FPJPMIP) model is developed and applied to the expansion planning of power generation under uncertainty. As an extension of existing fuzzy possibilistic programming and joint probabilistic programming, the FPJPMIP addresses system uncertainties in the model's left- and right-hand sides (with the expression of possibilistic and probabilistic distributions). Its applicability has

Z. F. Liu; G. H. Huang; N. Li

2008-01-01

93

Advances in simultaneous strategies for dynamic process optimization

Following on the popularity of dynamic simulation for process systems, dynamic optimization has been identified as an important task for key process applications. In this study, we present an improved algorithm for simultaneous strategies for dynamic optimization. This approach addresses two important issues for dynamic optimization. First, an improved nonlinear programming strategy is developed based on interior point methods. This

Lorenz T. Biegler; Arturo M. Cervantes; Andreas Wächter

2002-01-01

94

Optimizing Motion Planning for Hyper Dynamic Manipulator

NASA Astrophysics Data System (ADS)

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.

Aboura, Souhila; Omari, Abdelhafid; Meguenni, Kadda Zemalache

2012-01-01

95

Application of optimal prediction to molecular dynamics

Optimal prediction is a general system reduction technique for large sets of differential equations. In this method, which was devised by Chorin, Hald, Kast, Kupferman, and Levy, a projection operator formalism is used to construct a smaller system of equations governing the dynamics of a subset of the original degrees of freedom. This reduced system consists of an effective Hamiltonian dynamics, augmented by an integral memory term and a random noise term. Molecular dynamics is a method for simulating large systems of interacting fluid particles. In this thesis, I construct a formalism for applying optimal prediction to molecular dynamics, producing reduced systems from which the properties of the original system can be recovered. These reduced systems require significantly less computational time than the original system. I initially consider first-order optimal prediction, in which the memory and noise terms are neglected. I construct a pair approximation to the renormalized potential, and ignore three-particle and higher interactions. This produces a reduced system that correctly reproduces static properties of the original system, such as energy and pressure, at low-to-moderate densities. However, it fails to capture dynamical quantities, such as autocorrelation functions. I next derive a short-memory approximation, in which the memory term is represented as a linear frictional force with configuration-dependent coefficients. This allows the use of a Fokker-Planck equation to show that, in this regime, the noise is {delta}-correlated in time. This linear friction model reproduces not only the static properties of the original system, but also the autocorrelation functions of dynamical variables.

Barber IV, John Letherman

2004-12-01

96

The Construction of Dynamic Multi-objective Optimization Test Functions

Dynamic Multi-objective Optimization Problems (DMOPs) gradually become a difficult and hot topic in Multi-objective Optimization\\u000a area. However, there is lack of standard test functions for Dynamic Multi-objective Optimization Algorithms now. Firstly this\\u000a paper proves the existence of Pareto optimal set of a class of a special non-dynamic two-objective optimization problem theoretically.\\u000a Based on this result, we present one method of

Min Tang; Zhangcan Huang; Guangxi Chen

2007-01-01

97

A dynamic programming based Gas Pipeline Optimizer

A dynamic programming based Gas Pipeline Optimizer (GPO) has been developed at Scientific Software-Intercomp for the HBJ gas\\u000a transmission pipeline system in India. Used as an operating and planning tool, the GPO will determine the discharge pressures\\u000a at the compressor stations and the number of compressor trains to operate at each compressor station so that fuel consumption\\u000a and start-up\\/shut-down costs

Hemant S. Lall; Peter Percell

98

New approximate optimization method for distribution system planning

An algorithm to obtain an approximate optimal solution to the problem of large-scale radial distribution system planning is proposed. The distribution planning problem is formulated as a MIP (mixed integer programming) problem. The set of constraints is reduced to a set of continuous variable linear equations by using the fact that the basis of the simplex tableau consists of the

K. Aoki; K. Nara; T. Satoh; M. Kitagawa; K. Yamanaka

1990-01-01

99

Optimizing the natural gas supply mix of local distribution utilities

A large mixed-integer linear program (MILP) and a much smaller nonlinear programming (NLP) approximation of the MILP, involving simulation and response surface estimation via regression analysis, are proposed to solve the problem of the optimal selection of natural gas supply contracts by local gas distribution utilities. Each potential supply source is characterized by several price and nonprice parameters. Weather variability

Jean-Michel Guldmann; Fahui Wang

1999-01-01

100

Energy Optimized Topologies for Distributed Averaging in Wireless Sensor Networks

We study the energy efficient implementation of aver- aging\\/consensus algorithms in wireless sensor networks. For static, time-invariant topologies we start from the recent result that a bidirectional spanning tree is preferable in terms of convergence time. We formulate the combinatorial optimization problem of selecting such a minimal energy tree as a mixed integer linear pro- gramming problem. Since the problem

Ioannis Ch. Paschalidis; Binbin Li

2011-01-01

101

Three-phase distribution OPF in smart grids: Optimality versus computational burden

Existing Mixed Integer Non-linear Programming (MINLP) solution methods and commercially available solvers lack computational efficiency and robustness in solving three-phase Distribution Optimal Power Flow (DOPF) programs, given the large number of continuous and integer variables encountered in practical sized systems. A heuristic approach to solve this problem was proposed by the authors, in which a compromise is made on optimality

Sumit Paudyaly; Claudio A. Canizares; Kankar Bhattacharya

2011-01-01

102

Summary form only given, as follows. This paper presents a particle swarm optimization (PSO) for reactive power and voltage control (volt\\/VAr control: VVC) considering voltage security assessment (VSA). VVC can be formulated as a mixed-integer nonlinear optimization problem (MINLP). The proposed method expands the original PSO to handle a MINLP and determines an online VVC strategy with continuous and discrete

H. Yoshida; K. Kawata; Y. Fukuyama; S. Takayama; Y. Nakanishi

2001-01-01

103

MINLP Models for the Synthesis of Optimal Peptide Tags and Downstream Protein Processing

The development of systematic methods for the synthesis of downstream protein processing operations has seen growing interest in recent years, as purification is often the most complex and costly stage in biochemical production plants. The objective of the work presented here is to develop mathematical models based on mixed integer optimization techniques, which integrate the selection of optimal peptide purification

Evangelos Simeonidis; Jose M. Pinto; M. Elena Lienqueo; Sophia Tsoka; Lazaros G. Papageorgiou

2005-01-01

104

We introduce a mixed-integer programming formulation for finding optimal cyclic schedules for printed circuit board lines with multiple hoists on a shared track, where the processing sequence may be different than the location sequence of the tanks. Computational results on some benchmark problems indicate that optimal cyclic schedules for problems of realistic size can be found in a reasonable time.

Janny Leung; Guoqing Zhang

2003-01-01

105

Purpose – The purpose of this paper is to present the application of an adaptive bacterial foraging (BF) algorithm for the design optimization of an energy efficient induction motor. Design\\/methodology\\/approach – The induction motor design problem is formulated as a mixed integer nonlinear optimization problem. A set of nine independent variables is selected, and to make the machine feasible and

V. P. Sakthivel; R. Bhuvaneswari; S. Subramanian

2010-01-01

106

DPSIM Modelling: Dynamic Optimization in Large Scale Simulation Models

Although it is well established that dynamically optimal policies should be “closed loop” so that policies take into account\\u000a changing conditions of a system, it is rare for such optimization to actually be carried out in large-scale simulation models.\\u000a Computational limitations remain a major barrier to the study of dynamically optimal policies. Since the size of dynamic optimization\\u000a problems grows

Richard T. Woodward; Wade L. Griffin; Yong-Suhk Wui

107

Integrated DFM Framework for Dynamic Yield Optimization

NSDL National Science Digital Library

This website includes an abstract of the following article. Users may request access to the full article via the website, and a direct link will be emailed to them. We present a new methodology for a balanced yield optimization and a new DFM (design for manufacturability) framework which implements it. Our approach allows designers to dynamically balance multiple factors contributing to yield loss and select optimal combination of DFM enhancements based on the current information about the IC layout, the manufacturing process, and known causes of failures. We bring together the information gained from layout analysis, layout aware circuit analysis, resolution enhancement and optical proximity correction tools, parasitics extraction, timing estimates, and other tools, to suggest the DFM solution which is optimized within the existing constraints on design time and available data. The framework allows us to integrate all available sources of yield information, characterize and compare proposed DFM solutions, quickly adjust them when new data or new analysis tools become available, fine tune DFM optimization for a particular design and process and provide the IC designer with a customized solution which characterizes the manufacturability of the design, identifies and classifies areas with the most opportunities for improvement, and suggests DFM improvements. The proposed methodology replaces the ad hoc approach to DFM which targets one yield loss cause at a time at the expense of other factors with a comprehensive analysis of competing DFM techniques and trade offs between them.

2010-07-19

108

Particle Swarm Optimization with Dynamic Step Length

NASA Astrophysics Data System (ADS)

Particle swarm optimization (PSO) is a robust swarm intelligent technique inspired from birds flocking and fish schooling. Though many effective improvements have been proposed, however, the premature convergence is still its main problem. Because each particle's movement is a continuous process and can be modelled with differential equation groups, a new variant, particle swarm optimization with dynamic step length (PSO-DSL), with additional control coefficient- step length, is introduced. Then the absolute stability theory is introduced to analyze the stability character of the standard PSO, the theoretical result indicates the PSO with constant step length can not always be stable, this may be one of the reason for premature convergence. Simulation results show the PSO-DSL is effective.

Cui, Zhihua; Cai, Xingjuan; Zeng, Jianchao; Sun, Guoji

109

Mobile Robotic Systems: Dynamics, Control, and Optimization

NASA Astrophysics Data System (ADS)

Several classes of mobile robotic systems are discussed that are based on certain non-conventional principles of motion and can move along different surfaces and inside various media. Namely, we consider wall-climbing robots equipped with pneumatic grippers and able to move along vertical walls; snake-like multilink mechanisms with actuators installed at their joints; and vibro-robots moving in resistive media and containing movable masses. Kinematics and dynamics of these types of robots are discussed. Optimal geometrical and mechanical parameters as well as optimal periodic motions of robots are determined that correspond to the maximal average speed of locomotion. Results of experiments with prototypes of robots as well as results of computer simulation are presented. The locomotion principles analyzed are applicable to robots that can move in a complicated and hazardous environment, along different surfaces, and inside tubes.

Chernousko, F. L.

2009-08-01

110

Optimization of dynamic systems using collocation methods

NASA Astrophysics Data System (ADS)

The time-based simulation is an important tool for the engineer. Often a time-domain simulation is the most expedient to construct, the most capable of handling complex modeling issues, or the most understandable with an engineer's physical intuition. Aeroelastic systems, for example, are often most easily solved with a nonlinear time-based approach to allow the use of high fidelity models. Simulations of automatic flight control systems can also be easier to model in the time domain, especially when nonlinearities are present. Collocation is an optimization method for systems that incorporate a time-domain simulation. Instead of integrating the equations of motion for each design iteration, the optimizer iteratively solves the simulation as it finds the optimal design. This forms a smooth, well-posed, sparse optimization problem, transforming the numerical integration's sequential calculation into a set of constraints that can be evaluated in any order, or even in parallel. The collocation method used in this thesis has been improved from existing techniques in several ways, in particular with a very simple and computationally inexpensive method of applying dynamic constraints, such as damping, that are more traditionally calculated with linear models in the frequency domain. This thesis applies the collocation method to a range of aircraft design problems, from minimizing the weight of a wing with a flutter constraint, to gain-scheduling the stability augmentation system of a small-scale flight control testbed, to aeroservoelastic design of a large aircraft concept. Collocation methods have not been applied to aeroelastic simulations in the past, although the combination of nonlinear aerodynamic analyses with structural dynamics and stability constraints is well-suited to collocation. The results prove the collocation method's worth as a tool for aircraft design, particularly when applied to the multidisciplinary numerical models used today.

Holden, Michael Eric

111

Optimal bolt preload for dynamic loading

A simple spring-mass model is developed for closure bolting systems, including the effects of bolt prestress. An analytical solution is developed for the case of an initially peaked, exponentially decaying internal pressure pulse acting on the closure. The dependence of peak bolt stresses and deflections on bolt prestress level is investigated and an optimal prestress that minimizes peak bolt stress is found in certain cases. Vulnerability curves are developed for bolted-closure systems to provide rapid evaluation of the dynamic capacity of designs for a range in bolt prestress.

Duffey, T.A.

1992-01-01

112

Optimal bolt preload for dynamic loading

A simple spring-mass model is developed for closure bolting systems, including the effects of bolt prestress. An analytical solution is developed for the case of an initially peaked, exponentially decaying internal pressure pulse acting on the closure. The dependence of peak bolt stresses and deflections on bolt prestress level is investigated and an optimal prestress that minimizes peak bolt stress is found in certain cases. Vulnerability curves are developed for bolted-closure systems to provide rapid evaluation of the dynamic capacity of designs for a range in bolt prestress.

Duffey, T.A.

1992-08-01

113

Optimizing compressor operation with dynamic programming

Fuel consumption in natural gas compressor stations can be minimized using dynamic programming. Appropriate for installations with either reciprocating or centrifugal compressors in parallel or tandem configurations, this approach yields minimum fuel usage consistent with constraints on total throughput and operating limits on such individual parameters as speed, torque, and surge flow. The flow shares indicated by the optimization procedure may be achieved by adjusting pressure, temperature, or flow setpoints, depending on pipeline and compressor station configuration. Control may be exerted by changing compressor speed; reciprocating units can also be controlled by opening or closing the pockets.

Baqui, A.

1982-09-01

114

Optimal control of HIV-virus dynamics.

In this paper we consider a mathematical model of HIV-virus dynamics and propose an efficient control strategy to keep the number of HIV virons under a pre-specified level and to reduce the total amount of medications that patients receive. The model considered is a nonlinear third-order model. The third-order model describes dynamics of three most dominant variables: number of healthy white blood cells (T-cells), number of infected T-cells, and number of virus particles. There are two control variables in this model corresponding to two categories of antiviral drugs: reverse transcriptase inhibitors (RTI) and protease inhibitors (PI). The proposed strategy is based on linearization of the nonlinear model at the equilibrium point (steady state). The corresponding controller has two components: the first one that keeps the system state variables at the desired equilibrium (set-point controller) and the second-one that reduces in an optimal way deviations of the system state variables from their desired equilibrium values. The second controller is based on minimization of the square of the error between the actual and desired (equilibrium) values for the linearized system (linear-quadratic optimal controller). The obtained control strategy recommends to HIV researchers and experimentalists that the constant dosages of drugs have to be administrated at all times (set point controller, open-loop controller) and that the variable dosages of drugs have to be administrated on a daily basis (closed-loop controller, feedback controller). PMID:19294513

Radisavljevic-Gajic, Verica

2009-03-18

115

An Optimal Dynamic Threat Evaluation and Weapon Scheduling Technique

NASA Astrophysics Data System (ADS)

Real time scheduling problems demand high level of flexibility and robustness under complex dynamic scenarios. Threat Evaluation (TE) and Weapon Assignment (WA), together TEWA is one such complex dynamic system having optimal or near optimal utilization of scarce defensive resources of supreme priority. Several static solutions of TEWA have been proposed. This paper discusses an optimal dynamic multi-air threat evaluation and weapon allocation algorithm using a variant of Stable Marriage Algorithm (SMA). WA uses a new dynamic weapon scheduling algorithm, allowing multiple engagements using shoot-look-shoot strategy, to compute near-optimal solution. Testing part of this paper shows feasibility of this approach for a range of scenarios.

Naeem, H.; Masood, A.

116

Theory and Methodology Optimizing the natural gas supply mix of local distribution utilities 1

A large mixed-integer linear program (MILP) and a much smaller nonlinear programming (NLP) approximation of the MILP, involving simulation and response surface estimation via regression analysis, are proposed to solve the problem of the optimal selection of natural gas supply contracts by local gas distribution utilities. Each potential supply source is characterized by several price and nonprice parameters. Weather variability

Jean-Michel Guldmann; Fahui Wang

117

This paper describes a methodology developed for designing an optimal configuration for system transmission planning with carbon emissions costs. The power transmission network planning problem is modeled by the mixed integer programming model, a GA, and SA. At this moment environmental issues have the most serious problem to be concerned within every part of the world. Global warming, which is

A. Sadegheih

2010-01-01

118

Optimal flow rates and well locations for soil vapor extraction design

A mixed-integer programming model to determine the optimum number of wells, their locations and pumping rates for soil vapor extraction (SVE) is developed by coupling an air flow simulation model (AIR3D) to the GAMS optimization software. The model was tested for sensitivity of the vertical discretization of the domain, the number of potential well locations, the number of constraints, and

Charles S. Sawyer; Madhavi Kamakoti

1998-01-01

119

An optimal rescheduling for online train traffic control in disturbed situations

A practical method for generating optimal schedules for online train traffic control in disturbed situations is proposed. This scheduling problem is formulated to a 0-1 mixed integer programming problem. The method of solution proposed here is mainly divided into two parts: the first part generates a suboptimal solution by a heuristic method based upon \\

S. Araya; K. Abe; K. Fukumori

1983-01-01

120

A model is proposed for optimizing the net benefits of removing multiple dams in U.S. watersheds of Lake Erie by quantifying impacts upon social, ecological, and economic objectives of importance to managers and stakeholders. Explicit consideration is given to the linkages between newly accessible tributary habitat and the lake's ecosystem. The model is a mixed integer linear program (MILP) that

Pearl Q. Zheng; Benjamin F. Hobbs; Joseph F. Koonce

2009-01-01

121

Optimizing Circular Economy Planning and Risk Analysis Using System Dynamics

This article develops a systems dynamics and multi-objective programming model (SDMOP) for planning a regional circular economy. Various risk analyses are conducted using the technique of sensitivity analysis. This SDMOP model includes two modules: the MOP module used to derive optimized parameters as inputs to the systems dynamics model, and the systems dynamics module used to plan the regional circular

Jiuping Xu; Xiaofei Li; Desheng Dash Wu

2009-01-01

122

Dynamic systems of regional economy management optimization

NASA Astrophysics Data System (ADS)

One of the most actual problems of the Russian economic life is a regional economic systems formation. The hierarchy of economic and branch priorities should follow from the general idea of an industrial policy. The matter is that the concept of an industrial policy is defined by the system of priorities mainly incorporated in it. The problem of priorities is not solved yet neither on federal, nor at a regional level. It is necessary to recognize, that a substantiation of this or that variant of priorities - objectively a challenge. Such substantiation can be received with the help of dynamic structural modeling and management technology. At formation of the regional industrial policy program the special attention is given to creation of modern type commercial structures. In regions there are headquarters and branches of many largest corporations, holdings and banks. Besides it, many regional enterprises already became inter-regional or even the transnational companies. In this connection an assistance of transformation of the industrial enterprises and their groups in vertically integrated companies and modern type holdings can become a prominent aspect of an industrial policy. Regional economic structures should be reconstructed gradually on the general model of the world class competitive companies. Assistance to creation of new corporational control systems, the organization of headquarters and the central services work - all this can be included into the sphere of regional administration industrial policy. The special attention should be turned on necessity of development of own system of the corporate structures, capable to provide to the region an independent participation in use of the natural resources and industrial-technological potential, at the stage of a regional industrial policy program formation. Transformation of the industrial enterprises and their groups into modern type vertically-integrated companies and holdings can become one of the major directions of an industrial policy of region. The situational-analytical centers (SAC) of regional administration The major component of SAC is dynamic modeling, analysis, forecasting and optimization systems, based on modern intellectual information technologies. Spheres of SAC are not only financial streams management and investments optimization, but also strategic forecasting functions, which provide an optimum choice, "aiming", search of optimum ways of regional development and corresponding investments. It is expedient to consider an opportunity of formation of the uniform organizational-methodical center of an industrial policy of region. This organization can be directly connected to the scheduled-analytical services of the largest economic structures, local authorities, the ministries and departments. Such "direct communication" is capable to provide an effective regional development strategic management. Anyway, the output on foreign markets demands concentration of resources and support of authorities. Offered measures are capable to provide a necessary coordination of efforts of a various level economic structures. For maintenance of a regional industrial policy an attraction of all newest methods of strategic planning and management is necessary. Their activity should be constructed on the basis of modern approaches of economic systems management, cause the essence of an industrial policy is finally reduced to an effective regional and corporate economic activities control centers formation. Opportunities of optimum regional economy planning and management as uniform system Approaches to planning regional economic systems can be different. We will consider some most effective methods of planning and control over a regional facilities condition. All of them are compact and evident, that allows to put them into the group of average complexity technologies. At the decision of problems of a regional resource management is rather perspective the so-called "topographical" approach, which is used by intellectual information tec

Trofimov, S.; Kudzh, S.

123

Evolutionary Optimization of Dynamic Multi-objective Test Functions

Multi-objective as well as dynamic characteristics appear in many real-world problems. In order to use multi-objective evolutionary optimization algorithms (MOEA) efficiently, a systematic analysis of the algorithms' behavior in dynamic environments by means of dynamic test functions is necessary. These functions can be classified into problems with changing Pareto sets and\\/or Pareto fronts with different dynamic criteria. Thus, a test

Jorn Mehnen; Tobias Wagner; Gunter Rudolph

124

Optimization of reactive distillation processes with simulated annealing

A simulated annealing-based algorithm (MSIMPSA) suitable for the optimization of mixed integer non-linear programming (MINLP) problems was applied to the synthesis of a non-equilibrium reactive distillation column. A simulation model based on an extension of conventional distillation is proposed for the simulation step of the optimization problem. In the case of ideal vapor–liquid equilibrium, the simulation results are similar to

M. F. Cardoso; R. L. Salcedo; S. Feyo de Azevedo; D. Barbosa

2000-01-01

125

Dynamic optimization of constrained chemical engineering problems using dynamic programming

In many chemical engineering process control applications, one frequently encounters differential-algebraic optimization problems. Such optimal control problems are difficult to solve, in general, because of the presence of singular arcs for systems whose Hamiltonian is linear with respect to the control variable. We propose the use of absolute error penalty functions (AEPF) in handling constrained optimal control problems in chemical

S. A. Dadebo; K. B. Mcauley

1995-01-01

126

Vehicle dynamics applications of optimal control theory

The aim of the paper is to survey the various forms of optimal-control theory which have been applied to automotive problems and to present illustrative examples of applications studies, with assessments of the state of the art and of the contributions made through the use of optimal-control ideas. After a short introduction to the topic mentioning several questions to which

R. S. Sharp; Huei Peng

2011-01-01

127

Dynamic optimization identifies optimal programmes for pathway regulation in prokaryotes.

To survive in fluctuating environmental conditions, microorganisms must be able to quickly react to environmental challenges by upregulating the expression of genes encoding metabolic pathways. Here we show that protein abundance and protein synthesis capacity are key factors that determine the optimal strategy for the activation of a metabolic pathway. If protein abundance relative to protein synthesis capacity increases, the strategies shift from the simultaneous activation of all enzymes to the sequential activation of groups of enzymes and finally to a sequential activation of individual enzymes along the pathway. In the case of pathways with large differences in protein abundance, even more complex pathway activation strategies with a delayed activation of low abundance enzymes and an accelerated activation of high abundance enzymes are optimal. We confirm the existence of these pathway activation strategies as well as their dependence on our proposed constraints for a large number of metabolic pathways in several hundred prokaryotes. PMID:23979724

Bartl, Martin; Kötzing, Martin; Schuster, Stefan; Li, Pu; Kaleta, Christoph

2013-08-27

128

Dynamics systems vs. optimal control--a unifying view.

In the past, computational motor control has been approached from at least two major frameworks: the dynamic systems approach and the viewpoint of optimal control. The dynamic system approach emphasizes motor control as a process of self-organization between an animal and its environment. Nonlinear differential equations that can model entrainment and synchronization behavior are among the most favorable tools of dynamic systems modelers. In contrast, optimal control approaches view motor control as the evolutionary or development result of a nervous system that tries to optimize rather general organizational principles, e.g., energy consumption or accurate task achievement. Optimal control theory is usually employed to develop appropriate theories. Interestingly, there is rather little interaction between dynamic systems and optimal control modelers as the two approaches follow rather different philosophies and are often viewed as diametrically opposing. In this paper, we develop a computational approach to motor control that offers a unifying modeling framework for both dynamic systems and optimal control approaches. In discussions of several behavioral experiments and some theoretical and robotics studies, we demonstrate how our computational ideas allow both the representation of self-organizing processes and the optimization of movement based on reward criteria. Our modeling framework is rather simple and general, and opens opportunities to revisit many previous modeling results from this novel unifying view. PMID:17925262

Schaal, Stefan; Mohajerian, Peyman; Ijspeert, Auke

2007-01-01

129

An ant colony algorithm aimed at dynamic continuous optimization

The introduction of the concept of swarm intelligence into ant colony optimization (ACO) algorithms has shown the rich possibilities of self-organization when dealing with difficult optimization. Indeed, the inherent flexibility and efficiency of ACO algorithms proved to be advantageous for difficult dynamic discrete problems, e.g. routing in telecommunication networks. Moreover, we believe that ant colony algorithms can be efficient for

J. Dréo; P. Siarry

2006-01-01

130

Aerodynamic design optimization using sensitivity analysis and computational fluid dynamics

NASA Astrophysics Data System (ADS)

An efficient aerodynamic shape optimization method based on a computational fluid dynamics/sensitivity analysis algorithm has been developed which determines automatically the geometrical definition of an optimal surface starting from any initial arbitrary geometry. This method is not limited to any number of design variables or to any class of surfaces for shape definition.

Baysal, Oktay; Eleshaky, Mohamad E.

1992-01-01

131

Aerodynamic design optimization using sensitivity analysis and computational fluid dynamics

An efficient aerodynamic shape optimization method based on a computational fluid dynamics\\/sensitivity analysis algorithm has been developed which determines automatically the geometrical definition of an optimal surface starting from any initial arbitrary geometry. This method is not limited to any number of design variables or to any class of surfaces for shape definition.

Oktay Baysal; Mohamad E. Eleshaky

1992-01-01

132

Towards Dynamic Data-Driven Optimization of Oil Well Placement

The adequate location of wells in oil and environmental applications has a significant economical impact on reservoir management. However, the determination of optimal well locations is both challenging and computationally expensive. The overall goal of this research is to use the emerging Grid infra- structure to realize an autonomic dynamic data-driven self-optimizing reservoir framework. In this paper, we present the

Manish Parashar; Vincent Matossian; Wolfgang Bangerth; Hector Klie; Benjamin Rutt; Tahsin M. Kurç; Ümit V. Çatalyürek; Joel H. Saltz; Mary F. Wheeler

2005-01-01

133

Simple Distributed Particle Swarm Optimization for Dynamic and Noisy Environments.

National Technical Information Service (NTIS)

In this paper, we present a Simple Distributed Particle Swarm Optimization (SDPSO) algorithm that can be used to track the optimal solution in a dynamic and noisy environment. The classic PSO algorithm lacks the ability to track changing optimum in a dyna...

J. St. Charles T. E. Potok X. Cui

2009-01-01

134

Optimal Dynamic Pricing for Perishable Assets with Nonhomogeneous Demand

We consider a dynamic pricing model for selling a given stock of a perishable product over a finite time horizon. Customers, whose reservation price distribution changes over time, arrive according to a nonhomogeneous Poisson process. We show that at any given time, the optimal price decreases with inventory. We also identify a sufficient condition under which the optimal price decreases

Wen Zhao; Yu-Sheng Zheng

2000-01-01

135

Dynamic Optimal Control Models in Advertising: Recent Developments

This paper presents a review of recent developments that have taken place in the area of dynamic optimal control models in advertising subsequent to the comprehensive survey of the literature by Sethi in 1977. The basic problem underlying these models is that of determining optimal advertising expenditures and possibly other variables of interest over time for a firm or a

Gustav Feichtinger; Richard F. Hartl; Suresh P. Sethi

1994-01-01

136

Dynamical optimal learning for FNN and its applications

This work presents a new dynamical optimal learning (DOL) algorithm for three-layer linear neural networks and investigates its generalization ability. The optimal learning rates can be fully determined during the training process. The mean squared error is guaranteed to be stably decreased and the learning is less sensitive to initial parameter settings. The simulation results illustrate that the proposed DOL

H. J. Tang; K. C. Tan; T. H. Lee

2004-01-01

137

New dynamical optimal learning for linear multilayer FNN

This letter presents a new dynamical optimal learning (DOL) algorithm for three-layer linear neural networks and investigates its generalization ability. The optimal learning rates can be fully determined during the training process. The mean squared error (mse) is guaranteed to be stably decreased and the learning is less sensitive to initial parameter settings. The simulation results illustrate that the proposed

K. C. Tan; H. J. Tang

2004-01-01

138

Dynamic programming approach to a minimum distance optimal control problem

An optimal control problem with minimum-type (non-additive) functional is considered. Such problem has several applications, including air collision avoidance problem for two aircraft. It is known that the Bellman optimality principle is not fulfilled globally for this problem, so that the dynamic programming technique works only in a part of the problem's phase space. The boundary of this part is

Arik Melikyan; N. Hovakimyan; Y. Ikeda

2003-01-01

139

Review of dynamic optimization methods in renewable natural resource management

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.

Williams, B.K.

1989-01-01

140

Identification and Optimization of Aircraft Dynamics.

National Technical Information Service (NTIS)

A technique is described for the design of an adaptive controller for multivariable systems and is based on recently developed methods for identification and optimization. An application of the method to a helicopter system with time-varying parameters is...

K. S. Narendra S. S. Tripathi

1972-01-01

141

Optimization-based Dynamic Human Lifting Prediction.

National Technical Information Service (NTIS)

In this study, an optimization-based approach for simulating the lifting motion of a three dimensional digital human model is presented. Lifting motion is generated by minimizing a performance measure subjected to basic physical and kinematical constraint...

H. Chung J. Kim R. Bhatt S. Rahmatalla Y. Xiang

2008-01-01

142

Dynamic security constrained optimal power flow\\/VAr planning

Traditionally security constrained optimal power flow and VAr planning methods consider static security observing voltage profile and flow constraints under normal and post contingency conditions. Ideally, these formulations should be extended to consider dynamic security. This paper reports on a BC Hydro\\/CEPEL joint effort establishing a dynamic security constrained OPF\\/VAr planning tool which considers simultaneously static constraints as well as

Ebrahim Vaahedi; Yakout Mansour; Chris Fuchs; Sergio Granville; Maria de Lujan Latore; Hamid Hamadanizadeh

2001-01-01

143

Bridging developmental systems theory and evolutionary psychology using dynamic optimization.

Interactions between evolutionary psychologists and developmental systems theorists have been largely antagonistic. This is unfortunate because potential synergies between the two approaches remain unexplored. This article presents a method that may help to bridge the divide, and that has proven fruitful in biology: dynamic optimization. Dynamic optimization integrates developmental systems theorists' focus on dynamics and contingency with the 'design stance' of evolutionary psychology. It provides a theoretical framework as well as a set of tools for exploring the properties of developmental systems that natural selection might favor, given particular evolutionary ecologies. We also discuss limitations of the approach. PMID:23786476

Frankenhuis, Willem E; Panchanathan, Karthik; Clark Barrett, H

2013-03-18

144

Solving Optimal Control Problems by Exploiting Inherent Dynamical Systems Structures

NASA Astrophysics Data System (ADS)

Computing globally efficient solutions is a major challenge in optimal control of nonlinear dynamical systems. This work proposes a method combining local optimization and motion planning techniques based on exploiting inherent dynamical systems structures, such as symmetries and invariant manifolds. Prior to the optimal control, the dynamical system is analyzed for structural properties that can be used to compute pieces of trajectories that are stored in a motion planning library. In the context of mechanical systems, these motion planning candidates, termed primitives, are given by relative equilibria induced by symmetries and motions on stable or unstable manifolds of e.g. fixed points in the natural dynamics. The existence of controlled relative equilibria is studied through Lagrangian mechanics and symmetry reduction techniques. The proposed framework can be used to solve boundary value problems by performing a search in the space of sequences of motion primitives connected using optimized maneuvers. The optimal sequence can be used as an admissible initial guess for a post-optimization. The approach is illustrated by two numerical examples, the single and the double spherical pendula, which demonstrates its benefit compared to standard local optimization techniques.

Flaßkamp, Kathrin; Ober-Blöbaum, Sina; Kobilarov, Marin

2012-08-01

145

Dynamic Network Formation Using Ant Colony Optimization.

National Technical Information Service (NTIS)

This research presents three contributions for solving highly dynamic (i.e. drastic change within the network) Multi-commodity Capacitated Network Design Problems (MCNDPs) resulting in a distributed multi-agent network design algorithm. The first contribu...

S. C. Oimoen

2009-01-01

146

Combining optimal control theory and molecular dynamics for protein folding.

A new method to develop low-energy folding routes for proteins is presented. The novel aspect of the proposed approach is the synergistic use of optimal control theory with Molecular Dynamics (MD). In the first step of the method, optimal control theory is employed to compute the force field and the optimal folding trajectory for the C? atoms of a Coarse-Grained (CG) protein model. The solution of this CG optimization provides an harmonic approximation of the true potential energy surface around the native state. In the next step CG optimization guides the MD simulation by specifying the optimal target positions for the C? atoms. In turn, MD simulation provides an all-atom conformation whose C? positions match closely the reference target positions determined by CG optimization. This is accomplished by Targeted Molecular Dynamics (TMD) which uses a bias potential or harmonic restraint in addition to the usual MD potential. Folding is a dynamical process and as such residues make different contacts during the course of folding. Therefore CG optimization has to be reinitialized and repeated over time to accomodate these important changes. At each sampled folding time, the active contacts among the residues are recalculated based on the all-atom conformation obtained from MD. Using the new set of contacts, the CG potential is updated and the CG optimal trajectory for the C? atoms is recomputed. This is followed by MD. Implementation of this repetitive CG optimization-MD simulation cycle generates the folding trajectory. Simulations on a model protein Villin demonstrate the utility of the method. Since the method is founded on the general tools of optimal control theory and MD without any restrictions, it is widely applicable to other systems. It can be easily implemented with available MD software packages. PMID:22238629

Arkun, Yaman; Gur, Mert

2012-01-06

147

Policy Optimization for Dynamic Power Management

Dynamic power management schemes (also called policies) reduce the power consumption of complex electronic sys- tems by trading off performance for power in a controlled fash- ion, taking system workload into account. In a power-managed system it is possible to set components into different states, each characterized by performance and power consumption levels. The main function of a power management

Luca Benini; Alessandro Bogliolo; Giuseppe A. Paleologo; GiovanniDe Micheli

1999-01-01

148

Optimal dynamical decoupling sequence for the Ohmic spectrum

The optimal dynamical decoupling sequence for a qubit coupled to an Ohmic environment is investigated. By analytically computing the derivatives of the decoherence function, the optimal pulse locations are found to satisfy a set of nonlinear equations which can be easily solved. These equations incorporate the environment information such as the high-energy (UV) cutoff frequency {omega}{sub c}, giving a complete description of the decoupling process. The solutions explain previous experimental and theoretical results of locally optimized dynamical decoupling sequence in high-frequency-dominated environment, which were obtained by purely numerical computation and experimental feedback. As shown in numerical comparison, these solutions outperform the Uhrig dynamical decoupling sequence by one or more orders of magnitude in the Ohmic case.

Pan Yu; Cui Wei [Key Laboratory of Systems and Control, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190 (China); Graduate University of Chinese Academy of Sciences, Beijing 100039 (China); Xi Zairong [Key Laboratory of Systems and Control, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190 (China)

2010-02-15

149

Optimized reduction of uncertainty in bursty human dynamics

NASA Astrophysics Data System (ADS)

Human dynamics is known to be inhomogeneous and bursty but the detailed understanding of the role of human factors in bursty dynamics is still lacking. In order to investigate their role we devise an agent-based model, where an agent in an uncertain situation tries to reduce the uncertainty by communicating with information providers while having to wait time for responses. Here the waiting time can be considered as cost. We show that the optimal choice of the waiting time under uncertainty gives rise to the bursty dynamics, characterized by the heavy tailed distribution of optimal waiting time. We find that in all cases the efficiency for communication is relevant to the scaling behavior of the optimal waiting time distribution. On the other hand, the cost turns out in some cases to be irrelevant depending on the degree of uncertainty and efficiency.

Jo, Hang-Hyun; Moon, Eunyoung; Kaski, Kimmo

2012-01-01

150

Dynamic cellular manufacturing systems design—a comprehensive model

This paper addresses the dynamic cell formation problem (DCF). In dynamic environment, the product demand and mix changes\\u000a in each period of a multiperiod planning horizon. It causes need of reconfiguration of cells to respond to the product demand\\u000a and mix change in each period. This paper proposes a mixed-integer nonlinear programming model to design the dynamic cellular\\u000a manufacturing systems

Lokesh Kumar Saxena; Promod Kumar Jain

2011-01-01

151

Dynamically mapping screen real estate optimality

This research paper brings together the fields of systems engineering and media studies to investigate the cinema\\/television\\/computer\\/mobile device screen as a dynamic interface through which points of engagement or how the aesthetics and narrative structures presented on the screen engage the user and create meaning. The co-authors work towards the development of a “screen real estate grammar” or ontology by

Luigi Benedicenti; Sheila Petty

2010-01-01

152

Optimal Verification of Operations on Dynamic Sets

\\u000a We study the design of protocols for set-operation verification, namely the problem of cryptographically checking the correctness of outsourced set operations performed by an untrusted\\u000a server over a dynamic collection of sets that are owned (and updated) by a trusted source. We present new authenticated data\\u000a structures that allow any entity to publicly verify a proof attesting the correctness of

Charalampos Papamanthou; Roberto Tamassia; Nikos Triandopoulos

153

Optimal control of stochastic magnetization dynamics by spin current

NASA Astrophysics Data System (ADS)

Fluctuation-induced stochastic magnetization dynamics plays an important role in spintronics devices. Here we propose that it can be optimally controlled by spin currents to minimize or maximize the Freidlin-Wentzell action functional of the system hence to increase or decrease the probability of the large fluctuations. We apply this method to study the thermally activated magnetization switching problem and to demonstrate the merits of the optimal control strategy.

Wang, Yong; Zhang, Fu-Chun

2013-05-01

154

MILP model for emergy optimization in EIP water networks

The eco-industrial park (EIP) concept provides a framework in which several plants can cooperate with each other and exchange\\u000a their wastewater to minimize total freshwater consumption. Emergy analysis is a methodology that considers the total, cumulative\\u000a energy which has been consumed within a system; thus, by minimizing emergy, an environmentally optimal EIP can be designed.\\u000a This article presents a mixed-integer

Mohammad Sadegh Taskhiri; Raymond R. Tan; Anthony S. F. Chiu

155

Application of field and dynamics code to LEBT optimization

NASA Astrophysics Data System (ADS)

A code for computer simulation and optimization of beam dynamics in 3D or rotationally symmetric electrostatic fields is considered. It is based on a physical model that takes into account the beam space charge. The theoretical framework used for both formulation of the model and interpretation of the results of numerical experiments is a formalism of the charged particle dynamics in phase space. The code can be used as an effective tool for computer-aided design and optimization of electrostatic accelerating and focusing systems. The operation of the code is illustrated with a typical example.

Kozynchenko, S. A.; Svistunov, Yu. A.

2006-03-01

156

Speeding up critical system dynamics through optimized evolution

The number of defects which are generated upon crossing a quantum phase transition can be minimized by choosing properly designed time-dependent pulses. In this work we determine what are the ultimate limits of this optimization. We discuss under which conditions the production of defects across the phase transition is vanishing small. Furthermore we show that the minimum time required to enter this regime is T{approx}{pi}/{Delta}, where {Delta} is the minimum spectral gap, unveiling an intimate connection between an optimized unitary dynamics and the intrinsic measure of the Hilbert space for pure states. Surprisingly, the dynamics is nonadiabatic; this result can be understood by assuming a simple two-level dynamics for the many-body system. Finally we classify the possible dynamical regimes in terms of the action s=T{Delta}.

Caneva, Tommaso [International School for Advanced Studies (SISSA), Via Beirut 2-4, I-34014 Trieste (Italy); Institut fuer Quanteninformationsverarbeitung, Universitaet Ulm, D-89069 Ulm (Germany); Calarco, Tommaso; Montangero, Simone [Institut fuer Quanteninformationsverarbeitung, Universitaet Ulm, D-89069 Ulm (Germany); Fazio, Rosario [NEST, Scuola Normale Superiore and Istituto di Nanoscienze-CNR, Piazza dei Cavalieri 7, I-56126 Pisa (Italy); Santoro, Giuseppe E. [International School for Advanced Studies (SISSA), Via Beirut 2-4, I-34014 Trieste (Italy); CNR-INFM Democritos National Simulation Center, Via Beirut 2-4, I-34014 Trieste (Italy); International Centre for Theoretical Physics (ICTP), P.O. Box 586, I-34014 Trieste (Italy)

2011-07-15

157

A Population Prediction Strategy for Evolutionary Dynamic Multiobjective Optimization.

This paper investigates how to use prediction strategies to improve the performance of multiobjective evolutionary optimization algorithms in dealing with dynamic environments. Prediction-based methods have been applied to predict some isolated points in both dynamic single objective optimization and dynamic multiobjective optimization. We extend this idea to predict a whole population by considering the properties of continuous dynamic multiobjective optimization problems. In our approach, called population prediction strategy (PPS), a Pareto set is divided into two parts: a center point and a manifold. A sequence of center points is maintained to predict the next center, and the previous manifolds are used to estimate the next manifold. Thus, PPS could initialize a whole population by combining the predicted center and estimated manifold when a change is detected. We systematically compare PPS with a random initialization strategy and a hybrid initialization strategy on a variety of test instances with linear or nonlinear correlation between design variables. The statistical results show that PPS is promising for dealing with dynamic environments. PMID:23757532

Zhou, Aimin; Jin, Yaochu; Zhang, Qingfu

2013-02-26

158

Optimization of natural-gas pipeline systems via dynamic programming

The complexity and expense of operating natural-gas pipeline systems have made optimum operation and planning of increased interest to the natural-gas pipeline industries. Since the operations of natural-gas pipeline sytems are characterized by inherent nonlinearities and numerous constraints, dynamic programming provides an extremely powerful method for optimizing such systems. This paper summarizes the application of dynamic programming techniques to solve

P. Wong; R. Larson

1968-01-01

159

MULTIOBJECTIVE DYNAMIC APERTURE OPTIMIZATION AT NSLS-II

In this paper we present a multiobjective approach to the dynamic aperture (DA) optimization. Taking the NSLS-II lattice as an example, we have used both sextupoles and quadrupoles as tuning variables to optimize both on-momentum and off-momentum DA. The geometric and chromatic sextupoles are used for nonlinear properties while the tunes are independently varied by quadrupoles. The dispersion and emittance are fixed during tunes variation. The algorithms, procedures, performances and results of our optimization of DA will be discussed and they are found to be robust, general and easy to apply to similar problems.

Yang, L.; Li, Y.; Guo, W.; Krinsky, S.

2011-03-28

160

A Particle Swarm Optimization for Reactive Power and Voltage Control in Electric Power Systems

This paper presents a particle swarm optimization (PSO) for reactive power and voltage control (Volt\\/Var Control: VVC) in electric power systems considering voltage security. VVC can be formulated as a mixed-integer nonlinear optimization problem (MINLP). The proposed method expands the original PSO to handle a MINLP and determines an on-line VVC strategy with continuous and discrete control variables such as

Yoshikazu Fukuyama; Shinichi Takayama; Yosuke Nakanishi; Hirotaka Yoshida

1999-01-01

161

Optimal Capacity Planning in Multi-Radio MultiChannel Wireless Networks

In this paper, we study how to compute the optimal capacity planning in a multi-radio multi-channel (MR-MC) wireless network, that is, to find solutions for a set of coupled problems including channel assignment, scheduling, and routing, with the objective to optimize network capacity. The current state of the art mainly resorts to formulation of a mixed integer programming problem, which

Yu Cheng; Hongkun Li

2010-01-01

162

Optimal dynamic bandwidth allocation for complex networks

NASA Astrophysics Data System (ADS)

Traffic capacity of one network strongly depends on the link's bandwidth allocation strategy. In previous bandwidth allocation mechanisms, once one link's bandwidth is allocated, it will be fixed throughout the overall traffic transmission process. However, the traffic load of every link changes from time to time. In this paper, with finite total bandwidth resource of the network, we propose to dynamically allocate the total bandwidth resource in which each link's bandwidth is proportional to the queue length of the output buffer of the link per time step. With plenty of data packets in the network, the traffic handling ability of all links of the network achieves full utilization. The theoretical analysis and the extensive simulation results on complex networks are consistent. This work is valuable for network service providers to improve network performance or to do reasonable network design efficiently.

Jiang, Zhong-Yuan; Liang, Man-Gui; Li, Qian; Guo, Dong-Chao

2013-03-01

163

Equilibrium selection under evolutionary game dynamics with optimizing behavior

NASA Astrophysics Data System (ADS)

The purpose of this paper is to investigate equilibrium selection in a heterogeneous population composed of both optimizing and programmed agents using an evolutionary game-theoretic framework. Under the Smith dynamic, we are able to identify a class of games in which any programmed behavior will become extinct ultimately starting from any initial state, as well as a class of games in which all programmed agents can get wiped out eventually, as long as there are not enough initially. Besides, the long-run behavior is characterized under a variety of well-behaved dynamics such as the Brown-von Neumann-Nash dynamic.

Zhang, Yanfang; Mei, Shue; Zhong, Weijun

2012-09-01

164

Fractional Order Dynamics in a Particle Swarm Optimization Algorithm

This article reports the study of fractional dynamics during the evolution of a particle swarm optimization (PSO) algorithm. Some initial swarm particles are randomly changed, for stimulating the system response, and its effect is compared with a non-perturbed reference. The perturbation effect in the PSO evolution is observed in the perspective of the fitness time behavior of the best particle.

E. J. Solteiro Pires; P. B. de Oliveira; J. A. T. Machado; I. S. Jesus

2007-01-01

165

Near-optimal dynamical decoupling of a qubit.

We present a near-optimal quantum dynamical decoupling scheme that eliminates general decoherence of a qubit to order n using O(n2) pulses, an exponential decrease in pulses over all previous decoupling methods. Numerical simulations of a qubit coupled to a spin bath demonstrate the superior performance of the new pulse sequences. PMID:20481868

West, Jacob R; Fong, Bryan H; Lidar, Daniel A

2010-04-01

166

Dynamic Optimization of Chemical Processes using Ant Colony Framework

Ant colony framework is illustrated by considering dynamic optimization of six important bench marking examples. This new computational tool is simple to implement and can tackle problems with state as well as terminal constraints in a straightforward fashion. It requires fewer grid points to reach the global optimum at relatively very low computational effort. The examples with varying degree of

J. Rajesh; Kapil Gupta; Hari Shankar Kusumakar; Vaidyanathan K. Jayaraman; Bhaskar D. Kulkarni

2001-01-01

167

Optimal control of switching times in switched dynamical systems

This paper considers an optimal control problem for switched dynamical systems, where the objective is to minimize a cost functional defined on the state, and where the control variable consists of the switching times. The gradient of the cost functional is derived on an especially simple form, which lends itself to be directly used in gradient-descent algorithms. This special structure

M. Egerstedt; Y. Wardi; F. Delmotte

2003-01-01

168

Dynamic constrained optimal power flow using semi-infinite programming

Dynamic constrained optimal power flow can be modeled as a semi-infinite programming problem. In this letter, a local reduction based on infinite norm is proposed to replace the infinite constraints with finite constraints. The equivalent problem is then solved by the standard finite programming method. A case study on the WSCC nine-bus system showed that the proposed model and approach

Yan Xia; Ka Wing Chan

2006-01-01

169

Structural dynamics test simulation and optimization for aerospace components

This paper initially describes an innovative approach to product realization called Knowledge Based Testing (KBT). This research program integrates test simulation and optimization software, rapid fabrication techniques and computational model validation to support a new experimentally-based design concept. This design concept implements well defined tests earlier in the design cycle enabling the realization of highly reliable aerospace components. A test simulation and optimization software environment provides engineers with an essential tool needed to support this KBT approach. This software environment, called the Virtual Environment for Test Optimization (VETO), integrates analysis and test based models to support optimal structural dynamic test design. A goal in developing this software tool is to provide test and analysis engineers with a capability of mathematically simulating the complete structural dynamics test environment within a computer. A developed computational model of an aerospace component can be combined with analytical and/or experimentally derived models of typical structural dynamic test instrumentation within the VETO to determine an optimal test design. The VETO provides the user with a unique analysis and visualization environment to evaluate new and existing test methods in addition to simulating specific experiments designed to maximize test based information needed to validate computational models. The results of both a modal and a vibration test design are presented for a reentry vehicle and a space truss structure.

Klenke, S.E.; Baca, T.J.

1996-06-01

170

Neural dynamic optimization for control systems.III. Applications.

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. PMID:18244817

Seong, C Y; Widrow, B

2001-01-01

171

Beam Dynamics Optimization for the Xfel Photo Injector

NASA Astrophysics Data System (ADS)

The main challenge for the European XFEL photo injector is the production of 1 nC electron beams with a normalized transverse emittance of 0.9 mm mrad. The photo injector setup consists of a 1.5-cell L-band rf gun cavity supplied with solenoids for beam focusing and emittance compensation and the first accelerating section with 8 TESLA superconducting cavities. The first 4 cavities are used as a booster to provide by proper choice of its position, gradient and phase matching conditions for the emittance conservation. For optimization of the beam dynamics in the photo injector, a staged algorithm, based on ASTRA simulations, has been developed. The first stage considers the emission of electrons from a photo cathode. The cathode laser energy and its transverse parameters are adjusted to produce a bunch charge of 1 nC in presence of space charge forces (including image charge at the cathode) and Schottky-like effects. The second stage contains rf gun cavity and solenoid optimization. The booster position, gradient and initial phase are optimized at the third stage yielding the minimum emittance at the photo injector exit. Results of the XFEL photo injector optimization will be presented. Besides simulations experimental studies towards XFEL photo injector are carried out. The photo injector test facility at DESY in Zeuthen (PITZ) develops photo injectors for FELs, including FLASH and the European XFEL. A thorough comparison of measured data with results of beam dynamics simulations is one of the main PITZ goals. Detailed experimental studies on photo emission processes, thermal emittance, transverse and longitudinal phase space of the electron beam are being performed together with beam dynamics simulations. This aims to result in better understanding of beam dynamics in high brightness photo injectors. Experimentally obtained photo injector characteristics (like thermal emittance) have to be used in an additional optimization of the photo injector resulting in more realistic beam dynamics simulations. Results of these studies will be reported as well.

Krasilnikov, Mikhail

172

Optimal Control of HIV Dynamic Using Embedding Method

This present study proposes an optimal control problem, with the final goal of implementing an optimal treatment protocol which could maximize the survival time of patients and minimize the cost of drug utilizing a system of ordinary differential equations which describes the interaction of the immune system with the human immunodeficiency virus (HIV). Optimal control problem transfers into a modified problem in measure space using an embedding method in which the existence of optimal solution is guaranteed by compactness of the space. Then the metamorphosed problem is approximated by a linear programming (LP) problem, and by solving this LP problem a suboptimal piecewise constant control function, which is more practical from the clinical viewpoint, is achieved. The comparison between the immune system dynamics in treated and untreated patients is introduced. Finally, the relationships between the healthy cells and virus are shown.

Zarei, H.; Kamyad, A. V.; Farahi, M. H.

2011-01-01

173

Optimization of cardiovascular stent design using computational fluid dynamics.

Coronary stent design affects the spatial distribution of wall shear stress (WSS), which can influence the progression of endothelialization, neointimal hyperplasia, and restenosis. Previous computational fluid dynamics (CFD) studies have only examined a small number of possible geometries to identify stent designs that reduce alterations in near-wall hemodynamics. Based on a previously described framework for optimizing cardiovascular geometries, we developed a methodology that couples CFD and three-dimensional shape-optimization for use in stent design. The optimization procedure was fully-automated, such that solid model construction, anisotropic mesh generation, CFD simulation, and WSS quantification did not require user intervention. We applied the method to determine the optimal number of circumferentially repeating stent cells (N(C)) for slotted-tube stents with various diameters and intrastrut areas. Optimal stent designs were defined as those minimizing the area of low intrastrut time-averaged WSS. Interestingly, we determined that the optimal value of N(C) was dependent on the intrastrut angle with respect to the primary flow direction. Further investigation indicated that stent designs with an intrastrut angle of approximately 40 deg minimized the area of low time-averaged WSS regardless of vessel size or intrastrut area. Future application of this optimization method to commercially available stent designs may lead to stents with superior hemodynamic performance and the potential for improved clinical outcomes. PMID:22482657

Gundert, Timothy J; Marsden, Alison L; Yang, Weiguang; LaDisa, John F

2012-01-01

174

Optimal control and design of a cold store using dynamic optimization

The design of controlled processes is a combined optimal control and design problem (OCDP). Literature on solving large OCDPs is rare. This paper presents an algorithm for solving large OCDPs. For this algorithm system dynamics, objective function and their first-order derivatives must be continuous in the state, control and design parameters. The algorithm is successfully applied to the combined control

L. J. S. Lukasse; Jan Broeze; Sietze van der Sluis

2009-01-01

175

Adaptive optimal spectral range for dynamically changing scene

NASA Astrophysics Data System (ADS)

A novel multispectral video system that continuously optimizes both its spectral range channels and the exposure time of each channel autonomously, under dynamic scenes, varying from short range-clear scene to long range-poor visibility, is currently being developed. Transparency and contrast of high scattering medium of channels with spectral ranges in the near infrared is superior to the visible channels, particularly to the blue range. Longer wavelength spectral ranges that induce higher contrast are therefore favored. Images of 3 spectral channels are fused and displayed for (pseudo) color visualization, as an integrated high contrast video stream. In addition to the dynamic optimization of the spectral channels, optimal real-time exposure time is adjusted simultaneously and autonomously for each channel. A criterion of maximum average signal, derived dynamically from previous frames of the video stream is used (Patent Application - International Publication Number: WO2009/093110 A2, 30.07.2009). This configuration enables dynamic compatibility with the optimal exposure time of a dynamically changing scene. It also maximizes the signal to noise ratio and compensates each channel for the specified value of daylight reflections and sensors response for each spectral range. A possible implementation is a color video camera based on 4 synchronized, highly responsive, CCD imaging detectors, attached to a 4CCD dichroic prism and combined with a common, color corrected, lens. Principal Components Analysis (PCA) technique is then applied for real time "dimensional collapse" in color space, in order to select and fuse, for clear color visualization, the 3 most significant principal channels out of at least 4 characterized by high contrast and rich details in the image data.

Pinsky, Ephi; Siman-tov, Avihay; Peles, David

2012-05-01

176

This paper presents a particle swarm optimization (PSO) for reactive power and voltage control (Volt\\/Var Control: VVC) considering voltage security assessment (VSA). VVC can be formulated as a mixed-integer nonlinear optimization problem (MINLP). The proposed method expands the original PSO to handle a MINLP and determines an on-line VVC strategy with continuous and discrete control variables such as automatic voltage

Hirotaka Yoshida; Yoshikazu Fukuyama; Shinichi Takayama; Yosuke Nakanishi

1999-01-01

177

Optimal diffusive search: nonequilibrium resetting versus equilibrium dynamics

NASA Astrophysics Data System (ADS)

We study first-passage time problems for a diffusive particle with stochastic resetting with a finite rate r. The optimal search time is compared quantitatively with that of an effective equilibrium Langevin process with the same stationary distribution. It is shown that the intermittent, nonequilibrium strategy with non-vanishing resetting rate is more efficient than the equilibrium dynamics. Our results are extended to multiparticle systems where a team of independent searchers, initially uniformly distributed with a given density, looks for a single immobile target. Both the average and the typical survival probability of the target are smaller in the case of nonequilibrium dynamics.

Evans, Martin R.; Majumdar, Satya N.; Mallick, Kirone

2013-05-01

178

A new approach to optimal dynamic therapy planning.

Therapy planning is a very complex task. One of its crucial aspect is the derivation of therapeutic plans taking into account the dynamic aspect of the decision problem. This paper deals with dynamic decision problems using Influence Views, a novel graphical formalism based on Markov Decision Processes (MDPs) framework, in order to solve decision problems in which the optimal choice has to be revised periodically in accordance to the evolution of the patient's conditions. The proposed methodology has been applied to the plan of the prophylaxis in patients affected by a mild Hereditary Spherocytosis.

Magni, P.

1998-01-01

179

Shape Optimization of Vehicle Radiator Using Computational Fluid Dynamics (cfd)

NASA Astrophysics Data System (ADS)

Automotive manufacturers need to improve the efficiency and lifetime of all engine components. In the case of radiators, performance depends significantly on coolant flow homogeneity across the tubes and overall pressure drop between the inlet and outlet. Design improvements are especially needed in tube-flow uniformity to prevent premature fouling and failure of heat exchangers. Rather than relying on ad-hoc geometry changes, the current study combines Computational Fluid Dynamics with shape optimization methods to improve radiator performance. The goal is to develop an automated suite of virtual tools to assist in radiator design. Two objective functions are considered: a flow non-uniformity coefficient,Cf, and the overall pressure drop, dP*. The methodology used to automate the CFD and shape optimization procedures is discussed. In the first phase, single and multi-variable optimization methods, coupled with CFD, are applied to simplified 2-D radiator models to investigate effects of inlet and outlet positions on the above functions. The second phase concentrates on CFD simulations of a simplified 3-D radiator model. The results, which show possible improvements in both pressure and flow uniformity, validate the optimization criteria that were developed, as well as the potential of shape optimization methods with CFD to improve heat exchanger design. * Improving Radiator Design Through Shape Optimization, L. Guessous and S. Maddipatla, Paper # IMECE2002-33888, Proceedings of the 2002 ASME International Mechanical Engineering Congress and Exposition, November 2002

Maddipatla, Sridhar; Guessous, Laila

2002-11-01

180

A dynamic hybrid framework for constrained evolutionary optimization.

Based on our previous work, this paper presents a dynamic hybrid framework, called DyHF, for solving constrained optimization problems. This framework consists of two major steps: global search model and local search model. In the global and local search models, differential evolution serves as the search engine, and Pareto dominance used in multiobjective optimization is employed to compare the individuals in the population. Unlike other existing methods, the above two steps are executed dynamically according to the feasibility proportion of the current population in this paper, with the purpose of reasonably distributing the computational resource for the global and local search during the evolution. The performance of DyHF is tested on 22 benchmark test functions. The experimental results clearly show that the overall performance of DyHF is highly competitive with that of a number of state-of-the-art approaches from the literature. PMID:21824851

Wang, Yong; Cai, Zixing

2011-08-04

181

Self-Optimization for Dynamic Scheduling in Manufacturing Systems

\\u000a Scheduling is a critical function that is present throughout many industries and applications. A great need exists for developing\\u000a scheduling approaches that can be applied to a number of different scheduling problems with significant impact on performance\\u000a of business organizations. A challenge is emerging in the design of scheduling support systems for manufacturing environments\\u000a where dynamic adaptation and optimization become

Ana Madureira; Ivo Pereira

182

Optimal Dynamic Order Submission Strategies in Some Stylized Trading Problems

Abstract This study derives optimal dynamic,order submission strategies for trading problems faced by three stylized traders: an uninformed liquidity trader, an informed trader and a value- motivated trader. Separate solutions are obtained for quote- and order-driven markets. The results provide practicable rules for how to trade small orders and how to manage traders. Transaction cost measurement,methods,based on implementation shortfall are

Lawrence Harris

1998-01-01

183

Multi-agent System for Dynamic Manufacturing System Optimization

\\u000a This paper deals with the application of multi-agent system concept for optimization of dynamic uncertain process. These problems\\u000a are known to have a computationally demanding objective function, which could turn to be infeasible when large problems are\\u000a considered. Therefore, fast approximations to the objective function are required. This paper employs bundle of intelligent\\u000a systems algorithms tied together in a multi-agent

Tawfeeq Al-kanhal; Maysam Abbod

2008-01-01

184

Optimal Static-Dynamic Hedges for Barrier Options

We study optimal hedging of barrier options using a combination of a static position in vanilla options and dynamic trading of the underlying asset. The problem reduces to computing the Fenchel-Legendre transform of the utility-indifierence price as a function of the number of vanilla options used to hedge. Using the well-known duality between expo- nential utility and relative entropy, we

Ronnie Sircar

2004-01-01

185

An optimal tracking neuro-controller for nonlinear dynamic systems

Multilayer neural networks are used to design an optimal tracking neuro-controller (OTNC) for discrete-time nonlinear dynamic systems with quadratic cost function. The OTNC is made of two controllers: feedforward neuro-controller (FFNC) and feedback neuro-controller (FBNC). The FFNC controls the steady-state output of the plant, while the FBNC controls the transient-state output of the plant. The FFNC is designed using a

Young-Moon Park; Myeon-Song Choi; Kwang Y. Lee

1996-01-01

186

Optimally combining dynamical decoupling and quantum error correction

NASA Astrophysics Data System (ADS)

Quantum control and fault-tolerant quantum computing (FTQC) are two of the cornerstones on which the hope of realizing a large-scale quantum computer is pinned, yet only preliminary steps have been taken towards formalizing the interplay between them. Here we explore this interplay using the powerful strategy of dynamical decoupling (DD), and show how it can be seamlessly and optimally integrated with FTQC. To this end we show how to find the optimal decoupling generator set (DGS) for various subspaces relevant to FTQC, and how to simultaneously decouple them. We focus on stabilizer codes, which represent the largest contribution to the size of the DGS, showing that the intuitive choice comprising the stabilizers and logical operators of the code is in fact optimal, i.e., minimizes a natural cost function associated with the length of DD sequences. Our work brings hybrid DD-FTQC schemes, and their potentially considerable advantages, closer to realization.

Paz-Silva, Gerardo A.; Lidar, D. A.

2013-04-01

187

Confronting dynamics and uncertainty in optimal decision making for conservation

NASA Astrophysics Data System (ADS)

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 critically endangered population through captive breeding, control of invasive species, construction of biodiversity reserves, design of landscapes to increase habitat connectivity, and resource exploitation. Although these decision making problems and their solutions present significant challenges, we suggest that a systematic and effective approach to dynamic decision making in conservation need not be an onerous undertaking. The requirements are shared with any systematic approach to decision making—a careful consideration of values, actions, and outcomes.

Williams, Byron K.; Johnson, Fred A.

2013-06-01

188

An approach combining stochastic simulations and dynamic optimization is constructed to decide the optimal funding policy of the defined benefit pension scheme. The results show a significant advantage and flexibility of this approach in projecting the optimal financial status over the traditional deterministic pension valuation. In this study, the optimal contributions are estimated through dynamic programming under the projected workforce

Shih-Chieh Chang

1999-01-01

189

Human opinion dynamics: An inspiration to solve complex optimization problems.

Human interactions give rise to the formation of different kinds of opinions in a society. The study of formations and dynamics of opinions has been one of the most important areas in social physics. The opinion dynamics and associated social structure leads to decision making or so called opinion consensus. Opinion formation is a process of collective intelligence evolving from the integrative tendencies of social influence with the disintegrative effects of individualisation, and therefore could be exploited for developing search strategies. Here, we demonstrate that human opinion dynamics can be utilised to solve complex mathematical optimization problems. The results have been compared with a standard algorithm inspired from bird flocking behaviour and the comparison proves the efficacy of the proposed approach in general. Our investigation may open new avenues towards understanding the collective decision making. PMID:24141795

Kaur, Rishemjit; Kumar, Ritesh; Bhondekar, Amol P; Kapur, Pawan

2013-10-21

190

Human opinion dynamics: An inspiration to solve complex optimization problems

Human interactions give rise to the formation of different kinds of opinions in a society. The study of formations and dynamics of opinions has been one of the most important areas in social physics. The opinion dynamics and associated social structure leads to decision making or so called opinion consensus. Opinion formation is a process of collective intelligence evolving from the integrative tendencies of social influence with the disintegrative effects of individualisation, and therefore could be exploited for developing search strategies. Here, we demonstrate that human opinion dynamics can be utilised to solve complex mathematical optimization problems. The results have been compared with a standard algorithm inspired from bird flocking behaviour and the comparison proves the efficacy of the proposed approach in general. Our investigation may open new avenues towards understanding the collective decision making.

Kaur, Rishemjit; Kumar, Ritesh; Bhondekar, Amol P.; Kapur, Pawan

2013-01-01

191

Optimal forwarding ratio on dynamical networks with heterogeneous mobility

NASA Astrophysics Data System (ADS)

Since the discovery of non-Poisson statistics of human mobility trajectories, more attention has been paid to understand the role of these patterns in different dynamics. In this study, we first introduce the heterogeneous mobility of mobile agents into dynamical networks, and then investigate packet forwarding strategy on the heterogeneous dynamical networks. We find that the faster speed and the higher proportion of high-speed agents can enhance the network throughput and reduce the mean traveling time in random forwarding. A hierarchical structure in the dependence of high-speed is observed: the network throughput remains unchanged at small and large high-speed value. It is also interesting to find that a slightly preferential forwarding to high-speed agents can maximize the network capacity. Through theoretical analysis and numerical simulations, we show that the optimal forwarding ratio stems from the local structural heterogeneity of low-speed agents.

Gan, Yu; Tang, Ming; Yang, Hanxin

2013-05-01

192

Human opinion dynamics: An inspiration to solve complex optimization problems

NASA Astrophysics Data System (ADS)

Human interactions give rise to the formation of different kinds of opinions in a society. The study of formations and dynamics of opinions has been one of the most important areas in social physics. The opinion dynamics and associated social structure leads to decision making or so called opinion consensus. Opinion formation is a process of collective intelligence evolving from the integrative tendencies of social influence with the disintegrative effects of individualisation, and therefore could be exploited for developing search strategies. Here, we demonstrate that human opinion dynamics can be utilised to solve complex mathematical optimization problems. The results have been compared with a standard algorithm inspired from bird flocking behaviour and the comparison proves the efficacy of the proposed approach in general. Our investigation may open new avenues towards understanding the collective decision making.

Kaur, Rishemjit; Kumar, Ritesh; Bhondekar, Amol P.; Kapur, Pawan

2013-10-01

193

Stochastic programming for optimizing bidding strategies of a Nordic hydropower producer

From the point of view of a price-taking hydropower producer participating in the day-ahead power market, market prices are highly uncertain. The present paper provides a model for determining optimal bidding strategies taking this uncertainty into account. In particular, market price scenarios are generated and a stochastic mixed-integer linear programming model that involves both hydropower production and physical trading aspects

Stein-erik Fleten; Trine Krogh Kristoffersen

2007-01-01

194

logmip: a disjunctive 0–1 non-linear optimizer for process system models

Discrete-continuous non-linear optimization models are frequently used to formulate problems in process system engineering. Major modeling alternatives and solution algorithms include generalized disjunctive programming and mixed integer non-linear programming (MINLP). Both have advantages and drawbacks depending on the problem they are dealing with. In this work, we describe the theory behind logmip, a new computer code for disjunctive programming and

Aldo Vecchietti; Ignacio E. Grossmann

1999-01-01

195

LOGMIP: a disjunctive 0-1 non-linear optimizer for process system models

Discrete-continuous non-linear optimization models are frequently used to formulate problems in process system engineering. Major modeling alternatives and solution algorithms include generalized disjunctive programming and mixed integer non-linear programming (MINLP). Both have advantages and drawbacks depending on the problem they are dealing with. In this work, we describe the theory behind LOGMIP, a new computer code for disjunctive programming and

Aldo Vecchietti; Ignacio E. Grossmann

1999-01-01

196

Optimizing spread dynamics on graphs by message passing

NASA Astrophysics Data System (ADS)

Cascade processes are responsible for many important phenomena in natural and social sciences. Simple models of irreversible dynamics on graphs, in which nodes activate depending on the state of their neighbors, have been successfully applied to describe cascades in a large variety of contexts. Over the past decades, much effort has been devoted to understanding the typical behavior of the cascades arising from initial conditions extracted at random from some given ensemble. However, the problem of optimizing the trajectory of the system, i.e. of identifying appropriate initial conditions to maximize (or minimize) the final number of active nodes, is still considered to be practically intractable, with the only exception being models that satisfy a sort of diminishing returns property called submodularity. Submodular models can be approximately solved by means of greedy strategies, but by definition they lack cooperative characteristics which are fundamental in many real systems. Here we introduce an efficient algorithm based on statistical physics for the optimization of trajectories in cascade processes on graphs. We show that for a wide class of irreversible dynamics, even in the absence of submodularity, the spread optimization problem can be solved efficiently on large networks. Analytic and algorithmic results on random graphs are complemented by the solution of the spread maximization problem on a real-world network (the Epinions consumer reviews network).

Altarelli, F.; Braunstein, A.; Dall'Asta, L.; Zecchina, R.

2013-09-01

197

Maximum, minimum, and optimal mutation rates in dynamic environments

NASA Astrophysics Data System (ADS)

We analyze the dynamics of the parallel mutation-selection quasispecies model with a changing environment. For an environment with the sharp-peak fitness function in which the most fit sequence changes by k spin flips every period T , we find analytical expressions for the minimum and maximum mutation rates for which a quasispecies can survive, valid in the limit of large sequence size. We find an asymptotic solution in which the quasispecies population changes periodically according to the periodic environmental change. In this state we compute the mutation rate that gives the optimal mean fitness over a period. We find that the optimal mutation rate per genome, k/T , is independent of genome size, a relationship which is observed across broad groups of real organisms.

Ancliff, Mark; Park, Jeong-Man

2009-12-01

198

Dynamic stochastic optimization models for air traffic flow management

NASA Astrophysics Data System (ADS)

This dissertation presents dynamic stochastic optimization models for Air Traffic Flow Management (ATFM) that enables decisions to adapt to new information on evolving capacities of National Airspace System (NAS) resources. Uncertainty is represented by a set of capacity scenarios, each depicting a particular time-varying capacity profile of NAS resources. We use the concept of a scenario tree in which multiple scenarios are possible initially. Scenarios are eliminated as possibilities in a succession of branching points, until the specific scenario that will be realized on a particular day is known. Thus the scenario tree branching provides updated information on evolving scenarios, and allows ATFM decisions to be re-addressed and revised. First, we propose a dynamic stochastic model for a single airport ground holding problem (SAGHP) that can be used for planning Ground Delay Programs (GDPs) when there is uncertainty about future airport arrival capacities. Ground delays of non-departed flights can be revised based on updated information from scenario tree branching. The problem is formulated so that a wide range of objective functions, including non-linear delay cost functions and functions that reflect equity concerns can be optimized. Furthermore, the model improves on existing practice by ensuring efficient use of available capacity without necessarily exempting long-haul flights. Following this, we present a methodology and optimization models that can be used for decentralized decision making by individual airlines in the GDP planning process, using the solutions from the stochastic dynamic SAGHP. Airlines are allowed to perform cancellations, and re-allocate slots to remaining flights by substitutions. We also present an optimization model that can be used by the FAA, after the airlines perform cancellation and substitutions, to re-utilize vacant arrival slots that are created due to cancellations. Finally, we present three stochastic integer programming models for managing inbound air traffic flow of an airport, when there is adverse weather impacting the arrival capacity of the airport along with its arrival fixes. These are the first models, for optimizing ATFM decisions, which address uncertainty of future capacities of multiple NAS resources.

Mukherjee, Avijit

199

Dynamic Simulation and Optimization of Nuclear Hydrogen Production Systems

This project is part of a research effort to design a hydrogen plant and its interface with a nuclear reactor. This project developed a dynamic modeling, simulation and optimization environment for nuclear hydrogen production systems. A hybrid discrete/continuous model captures both the continuous dynamics of the nuclear plant, the hydrogen plant, and their interface, along with discrete events such as major upsets. This hybrid model makes us of accurate thermodynamic sub-models for the description of phase and reaction equilibria in the thermochemical reactor. Use of the detailed thermodynamic models will allow researchers to examine the process in detail and have confidence in the accurary of the property package they use.

Paul I. Barton; Mujid S. Kaximi; Georgios Bollas; Patricio Ramirez Munoz

2009-07-31

200

Optimized dynamical decoupling for power-law noise spectra

We analyze the suppression of decoherence by means of dynamical decoupling in the pure-dephasing spin-boson model for baths with power law spectra. The sequence of ideal pi pulses is optimized according to the power of the bath. We expand the decoherence function and separate the canceling divergences from the relevant terms. The proposed sequence is chosen to be the one minimizing the decoherence function. By construction, it provides the best performance. We analytically derive the conditions that must be satisfied. The resulting equations are solved numerically. The solutions are very close to the Carr-Purcell-Meiboom-Gill sequence for a soft cutoff of the bath while they approach the Uhrig dynamical-decoupling sequence as the cutoff becomes harder.

Pasini, S.; Uhrig, G. S. [Lehrstuhl fuer Theoretische Physik I, TU Dortmund, Otto-Hahn Strasse 4, D-44221 Dortmund (Germany)

2010-01-15

201

Optimal control of drug therapy: melding pharmacokinetics with viral dynamics.

Pharmacokinetics were melded with a viral dynamical model to design an optimal drug administration regimen such that the basic reproductive number for the virus was minimized. One-compartmental models with two kinds of drug delivery routes, intravenous and extravascular with multiple dosages, and two drug elimination rates, first order and Michaelis-Menten rates, were considered. We defined explicitly the basic reproductive number for the viral dynamical model melded with pharmacokinetics. When the average plasma drug concentration was constant, intravenous administration of the drug with small dosages applied frequently minimized the basic reproductive number. For extravascular administration, the basic reproductive number initially decreases to a trough point and then increases as the drug dosage increases. When a therapeutic window is considered, numerical studies indicate that the wider the window, the smaller the basic reproductive number. Once the width of the therapeutic window is fixed, the basic reproductive number monotonously declines as the minimum therapeutic level increases. The findings suggest that the existence of drug dosage and drug administration interval that minimize the basic reproductive number could help design the optimal drug administration regimen. PMID:22172775

Yang, Youping; Xiao, Yanni; Wang, Ning; Wu, Jianhong

2011-12-07

202

Hydropower Optimization for the Lower Seyhan System in Turkey using Dynamic Programming

Dynamic programming with successive approximation has been used in the past for optimizing multi-reservoir water resources systems. In this study, the State Incremental Dynamic Programming (SIDP) model is developed for energy optimization of multi-reservoir systems. A random file access method is used for reaching initial and intermediate data to cope with the curse of dimensionality of dynamic programming. A conventional

Recep Yurtal; Galip Seckin; GaliMehmet Ardiclioglu

2005-01-01

203

Hydropower Optimization for the Lower Seyhan Basin System in Turkey using dynamic programming

Dynamic programming with successive approximation has been used in the past for optimizing multi-reservoir water resources systems. In this study, a State Incremental Dynamic Programming (SIDP) model is developed for energy optimization of multi-reservoir systems. A random file access method is used to generate initial and intermediate data and cope with the curse of dimensionality of dynamic programming. The conventional

Recep Yurtal; Galip Seckin; Mehmet Ardiclioglu

2006-01-01

204

Assessment of optimally filtered recent geodetic mean dynamic topographies

NASA Astrophysics Data System (ADS)