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Multiobjective Evolutionary Algorithms: Analyzing the StateoftheArt
, 2000
"... Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mideighties in an attempt to stochastically solve problems of this generic class. During the past decade, ..."
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Cited by 285 (7 self)
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Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mideighties in an attempt to stochastically solve problems of this generic class. During the past decade, a variety of multiobjective EA (MOEA) techniques have been proposed and applied to many scientific and engineering applications. Our discussion's intent is to rigorously define multiobjective optimization problems and certain related concepts, present an MOEA classification scheme, and evaluate the variety of contemporary MOEAs. Current MOEA theoretical developments are evaluated; specific topics addressed include fitness functions, Pareto ranking, niching, fitness sharing, mating restriction, and secondary populations. Since the development and application of MOEAs is a dynamic and rapidly growing activity, we focus on key analytical insights based upon critical MOEA evaluation of c...
Reliability Models for Facility Location: The Expected Failure Cost Case
 Transportation Science
, 2004
"... Classical facility location models like the Pmedian problem (PMP) and the uncapacitated fixedcharge location problem (UFLP) implicitly assume that once constructed, the facilities chosen will always operate as planned. In reality, however, facilities "fail" from time to time due to poor weather, l ..."
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Cited by 26 (9 self)
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Classical facility location models like the Pmedian problem (PMP) and the uncapacitated fixedcharge location problem (UFLP) implicitly assume that once constructed, the facilities chosen will always operate as planned. In reality, however, facilities "fail" from time to time due to poor weather, labor actions, changes of ownership, or other factors. Such failures may lead to excessive transportation costs as customers must be served from facilities much farther than their regularly assigned facilities. In this paper, we present models for choosing facility locations to minimize cost while also taking into account the expected transportation cost after failures of facilities. The goal is to choose facility locations that are both inexpensive under traditional objective functions and also reliable. This reliability approach is new in the facility location literature. We formulate reliability models based on both the PMP and the UFLP and present an optimal Lagrangian relaxation algorithm to solve them. We discuss how to use these models to generate a tradeo# curve between the daytoday operating cost and the expected cost taking failures into account, and use these tradeo# curves to demonstrate empirically that substantial improvements in reliability are often possible with minimal increases in operating cost.
Optimal operation of multi reservoir systems: stateoftheart review
 J. Water Resour. Plann. Manag
, 2004
"... Abstract: With construction of new largescale water storage projects on the wane in the U.S. and other developed countries, attention must focus on improving the operational effectiveness and efficiency of existing reservoir systems for maximizing the beneficial uses of these projects. Optimal coor ..."
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Cited by 24 (0 self)
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Abstract: With construction of new largescale water storage projects on the wane in the U.S. and other developed countries, attention must focus on improving the operational effectiveness and efficiency of existing reservoir systems for maximizing the beneficial uses of these projects. Optimal coordination of the many facets of reservoir systems requires the assistance of computer modeling tools to provide information for rational management and operational decisions. The purpose of this review is to assess the stateoftheart in optimization of reservoir system management and operations and consider future directions for additional research and application. Optimization methods designed to prevail over the highdimensional, dynamic, nonlinear, and stochastic characteristics of reservoir systems are scrutinized, as well as extensions into multiobjective optimization. Application of heuristic programming methods using evolutionary and genetic algorithms are described, along with application of neural networks and fuzzy rulebased systems for inferring reservoir system operating rules.
A network flow model for lanebased evacuation routing
 TRANSPORTATION RESEARCH PART A
, 2003
"... Most traffic delays in regional evacuations occur at intersections. Lanebased routing is one strategy for reducing these delays. This paper presents a network flow model for identifying optimal lanebased evacuation routing plans in a complex road network. The model is an integer extension of the m ..."
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Cited by 9 (0 self)
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Most traffic delays in regional evacuations occur at intersections. Lanebased routing is one strategy for reducing these delays. This paper presents a network flow model for identifying optimal lanebased evacuation routing plans in a complex road network. The model is an integer extension of the minimumcost flow problem. It can be used to generate routing plans that trade total vehicle traveldistance against merging, while preventing traffic crossingconflicts at intersections. A mixedinteger programming solver is used to derive optimal routing plans for a sample network. Manual capacity analysis and microscopic traffic simulation are used to compare the relative efficiency of the plans. An application is presented for
Constraint MethodBased Evolutionary Algorithm (CMEA) for Multiobjective Optimization
, 2001
"... . Evolutionary algorithms are becoming increasingly valuable in solving largescale, realistic engineering multiobjective optimization (MO) problems, which typically require consideration of conflicting and competing design issues. The new procedure, Constraint MethodBased Evolutionary Algorith ..."
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Cited by 9 (0 self)
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. Evolutionary algorithms are becoming increasingly valuable in solving largescale, realistic engineering multiobjective optimization (MO) problems, which typically require consideration of conflicting and competing design issues. The new procedure, Constraint MethodBased Evolutionary Algorithm (CMEA), presented in this paper is based upon underlying concepts in the constraint method described in the mathematical programming literature. Pareto optimality is achieved implicitly via a constraint approach, and convergence is enhanced by using beneficial seeding of the initial population. CMEA is evaluated by solving two test problems reported in the multiobjective evolutionary algorithm (MOEA) literature. Performance comparisons based on quantitative metrics for accuracy, coverage, and spread are presented. CMEA is relatively simple to implement and incorporate into existing implementations of evolutionary algorithmbased optimization procedures. 1
Stochastic pRobust Location Problems
, 2004
"... Many objectives have been proposed for optimization under uncertainty. The typical stochastic programming objective of minimizing expected cost may yield solutions that are inexpensive in the long run but perform poorly under certain realizations of the random data. On the other hand, the typical ro ..."
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Cited by 8 (3 self)
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Many objectives have been proposed for optimization under uncertainty. The typical stochastic programming objective of minimizing expected cost may yield solutions that are inexpensive in the long run but perform poorly under certain realizations of the random data. On the other hand, the typical robust optimization objective of minimizing maximum cost or regret tends to be overly conservative, planning against a disastrous but unlikely scenario. In this paper, we present facility location models that combine the two objectives by minimizing the expected cost while bounding the relative regret in each scenario. In particular, the models seek the minimumexpectedcost solution that is probust; i.e., whose relative regret is no more than 100p% in each scenario.
Multicriterion Decision Merging Competitive Development of an Aboriginal Whaling Management Procedure
 Journal of the American Statistical Association
"... this paper about the collective opinion and behavior of the IWC and its subcommittees are entirely the author's own, based on membership in the US delegation to the IWC Scientific Committee since 1992. They do not reflect official IWC policy or opinion. ..."
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Cited by 4 (4 self)
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this paper about the collective opinion and behavior of the IWC and its subcommittees are entirely the author's own, based on membership in the US delegation to the IWC Scientific Committee since 1992. They do not reflect official IWC policy or opinion.
Singleobjective vs. Multiobjective Optimisation for Integrated Decision Support, In: Integrated Assessment and Decision
 Proceedings of the First Biennial Meeting of the International Environmental Modelling and Software Society
, 2002
"... Abstract: In many optimisation problems, analysts are often confronted with multiobjective decision problems. The most common purpose of an analysis is to choose the best tradeoffs among all the defined and conflicting objectives. However, many optimisation studies are formulated as a problem whose ..."
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Cited by 4 (1 self)
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Abstract: In many optimisation problems, analysts are often confronted with multiobjective decision problems. The most common purpose of an analysis is to choose the best tradeoffs among all the defined and conflicting objectives. However, many optimisation studies are formulated as a problem whose goal is to find the “best ” solution, which corresponds to the minimum or maximum value of a single objective function that lumps all different objectives into one. Water distribution system design is a multiobjective problem for which it is difficult to identify the true benefits and constraints due primarily to the uncertainty in future demands. This paper shows some shortcomings of the use of singleobjective optimisation for water distribution system design and introduces a genetic algorithm multiobjective model that promises to ease the difficulties in applying optimisation and providing decision support for that important problem. The optimisation model used in this paper utilises simple and intuitive objectives and constraints that are not difficult to formulate in mathematical terms. Those objectives allow a decisionmaker to visualise the tradeoffs between different benefits and costs, and more importantly to consider uncertainty in future demands and performance levels. This type of optimisation could also take into account that the system needs to be implemented in stages.
Tradeoffs Between Customer Service and Cost in an Integrated Supply Chain Design Framework,” submitted to Manufacturing and Service Operations Management
 in Integrated Supply Chain Design. Forthcoming in Manufacturing and Service Operations Management
, 2003
"... In designing a supply chain, firms are often faced with the competing demands of improved customer service and reduced cost. We develop a model that incorporates fixed distribution center location costs, working and safety stock inventory costs at the distribution centers, fixed and variable transpo ..."
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Cited by 4 (2 self)
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In designing a supply chain, firms are often faced with the competing demands of improved customer service and reduced cost. We develop a model that incorporates fixed distribution center location costs, working and safety stock inventory costs at the distribution centers, fixed and variable transport costs from a plant to the distribution centers and delivery costs to customers. Service is measured by the fraction of all demands that are located within an exogenously specified service standard. The nonlinear model simultaneously determines distribution center locations, the assignment of demands to distribution centers and the inventory policy at the distribution centers to optimize the cost and service objectives. We use the weighting method to find all supported points on the tradeoff curve. Our results suggest that significant service improvements can be achieved relative to the minimum cost solution at a relatively incremental cost. 1.
Analytic Efficient Solution Set For MultiCriteria Quadratic Programs
 European Journal of Operations Research
"... : We present active set methods to evaluate the exact analytic efficient solution set for multicriteria convex quadratic programming problems (MCQP) subject to linear constraints. The idea is based on the observations that a strictly convex programming problem admits a unique solution, and that the ..."
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Cited by 4 (3 self)
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: We present active set methods to evaluate the exact analytic efficient solution set for multicriteria convex quadratic programming problems (MCQP) subject to linear constraints. The idea is based on the observations that a strictly convex programming problem admits a unique solution, and that the efficient solution set for a multicriteria strictly convex quadratic programming problem with linear equality constraints can be parameterized. The case of bicriteria quadratic programming (BCQP) is first discussed since many of the underlying ideas can be explained much more clearly in the case of two objectives. In particular we note that the efficient solution set of a BCQP problem is a curve on the surface of a polytope. The extension to problems with more than two objectives is straight forward albeit some slightly more complicated notation. Two numerical examples are given to illustrate the proposed methods. Keywords: Quadratic programming, Multiple criteria, Active set methods, An...