The paper describes a computational experiment which goal is to evaluate computational efficiency of three multipleobjective evolutionary metaheuristics on the multipleobjectivemultiple constraints knapsack problem...
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The paper describes a computational experiment which goal is to evaluate computational efficiency of three multipleobjective evolutionary metaheuristics on the multipleobjectivemultiple constraints knapsack problem. The relative efficiency of the multipleobjective algorithms is evaluated with respect to a single objective evolutionary algorithm (EA). We use a methodology that allows consistent evaluation of the quality of approximately Pareto-optimal solutions generated by both multiple and single objective metaheuristics. Then, we compare computational efforts needed to generate solutions of approximately the same quality by the two kinds of methods. The results indicate that computational efficiency of multipleobjective EAs deteriorates with the growth of the number of objectives. Furthermore, significant differences in the performance of the three algorithms are observed. (C) 2003 Elsevier B.V. All rights reserved.
Post-design decisions in a cellular manufacturing system are modeled in this paper as an interval programming model in which the coefficients of the objective function are expressed in range rather than a point estima...
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Post-design decisions in a cellular manufacturing system are modeled in this paper as an interval programming model in which the coefficients of the objective function are expressed in range rather than a point estimate. Uncertainties in estimations for costs related to exceptional elements and bottleneck machines have been modeled. The interval objective function involves order relations, which represents the decision makers' preference between interval costs. The objective function is minimized by converting it into a multi-objective problem using order relations. A quantitative estimate of 'risk by gain ratio' is proposed to understand the model behavior and to facilitate the selection of appropriate strategies. (C) 1998 Elsevier Science B.V. All rights reserved.
A common way of computing all efficient (Pareto optimal) solutions for a biobjective combinatorial optimisation problem is to compute first the extreme efficient solutions and then the remaining, non-extreme solutions...
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A common way of computing all efficient (Pareto optimal) solutions for a biobjective combinatorial optimisation problem is to compute first the extreme efficient solutions and then the remaining, non-extreme solutions. The second phase, the computation of non-extreme solutions, can be based on a "k-best" algorithm for the single-objective version of the problem or on the branch-and-bound method. A k-best algorithm computes the k-best solutions in order of their objective values. We compare the performance of these two approaches applied to the biobjective minimum spanning tree problem. Our extensive computational experiments indicate the overwhelming superiority of the k-best approach. We propose heuristic enhancements to this approach which further improve its performance. (C) 2006 Elsevier Ltd. All rights reserved.
This paper addresses the problem of computing minimum risk paths by taking as objective the expected accident cost. The computation is based on a dynamic programming formulation which can be considered an extension of...
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This paper addresses the problem of computing minimum risk paths by taking as objective the expected accident cost. The computation is based on a dynamic programming formulation which can be considered an extension of usual dynamic programming models: path costs are recursively computed via functions which are assumed to be monotonic. A large part of the paper is devoted to analyze in detail this formulation and provide some new results. Based on the dynamic programming model a linear programming model is also presented to compute minimum risk paths. This formulation turns out to be useful in solving a biobjective version of the problem, in which also expected travel length is taken into consideration. This leads to define nondominated mixed strategies. Finally it is shown how to extend the basic updating device of dynamic programming in order to enumerate all nondominated paths. (c) 2005 Elsevier B.V. All rights reserved.
In multi-criteria decision making approaches it is typical to consider an underlying preference function that is assumed to represent the decision maker's preferences. In this paper we introduce a broad family of ...
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In multi-criteria decision making approaches it is typical to consider an underlying preference function that is assumed to represent the decision maker's preferences. In this paper we introduce a broad family of preference functions that can represent a wide variety of preference structures. We develop the necessary theory and interactive algorithms for both the general family of the preference functions and for its special cases. The algorithms guarantee to find the most preferred solution (point) of the decision maker under the assumed conditions. The convergence of the algorithms are achieved by progressively reducing the solution space based on the preference information obtained from the decision maker and the properties of the assumed underlying preference functions. We first demonstrate the algorithms on a simple bi-criteria problem with a given set of available points. We also test the performances of the algorithms on three-criteria knapsack problems and show that they work well. (C) 2017 Elsevier B.V. All rights reserved.
The aim of this paper is to discuss the optimality of interval multi-objective optimization problems with the help of different interval metric. For this purpose, we have proposed the new definitions of interval order...
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The aim of this paper is to discuss the optimality of interval multi-objective optimization problems with the help of different interval metric. For this purpose, we have proposed the new definitions of interval order relations by modifying the existing definitions and also modified different definitions of interval mathematics. Using the definitions of interval order relations and interval metric, the multi-objective optimization problem is converted into single objective optimization problem by different techniques. Then the corresponding problems have been solved by hybrid Tournament Genetic Algorithm with whole arithmetic crossover and double mutation (combination of non-uniform and boundary mutations). To illustrate the methodology, five numerical examples have been solved and the computational results have been compared. Finally, to test the efficiency of the proposed hybrid Tournament Genetic Algorithm, sensitivity analyses have been carried out graphically with respect to genetic algorithm parameters. (C) 2014 Elsevier Ltd. All rights reserved.
We propose a new distributed heuristic for approximating the Pareto set of bi-objective optimization problems. Our approach is at the crossroads of parallel cooperative computation, objective space decomposition, and ...
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We propose a new distributed heuristic for approximating the Pareto set of bi-objective optimization problems. Our approach is at the crossroads of parallel cooperative computation, objective space decomposition, and adaptive search. Given a number of computing nodes, we self-coordinate them locally, in order to cooperatively search different regions of the Pareto front. This offers a trade-off between a fully independent approach, where each node would operate independently of the others, and a fully centralized approach, where a global knowledge of the entire population is required at every step. More specifically, the population of solutions is structured and mapped into computing nodes. As local information, every node uses only the positions of its neighbors in the objective space and evolves its local solution based on what we term a 'localized fitness function'. This has the effect of making the distributed search evolve, over all nodes, to a high quality approximation set, with minimum communications. We deploy our distributed algorithm using a computer cluster of hundreds of cores and study its properties and performance on rho MNK-landscapes. Through extensive large-scale experiments, our approach is shown to be very effective in terms of approximation quality, computational time and scalability. (C) 2014 Elsevier B.V. All rights reserved.
This research deals with a real-world planning problem in railway infrastructure operations. It is part of the RECIFE project, which seeks to develop a decision support software to help evaluate the capacity of a rail...
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This research deals with a real-world planning problem in railway infrastructure operations. It is part of the RECIFE project, which seeks to develop a decision support software to help evaluate the capacity of a rail junction or station. To this end, the project is working on a timetable optimization model, as well as timetable evaluation modules. This paper presents a module for evaluating timetable stability, which uses an original method based on delay propagation and using shortest path problem resolution. A didactic example and a complete case study applying this method to the Pierrefitte-Gonesse junction are also presented. (C) 2007 Elsevier B.V. All rights reserved.
This note presents the main characteristics of a decision support system (DSS) dealing with multiobjective integer and mixed-integer programming problems. The DSS is based on interactive reference point approaches dev...
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This note presents the main characteristics of a decision support system (DSS) dealing with multiobjective integer and mixed-integer programming problems. The DSS is based on interactive reference point approaches developed by the authors for this kind of problems. It is implemented for Windows platforms and aims at providing an open communication protocol for interaction with the decision maker(s). (C) 2003 Elsevier B.V. All rights reserved.
We present an approach to interactive multiple Criteria Decision Making based on preference driven Evolutionary Multiobjective Optimization with controllable accuracy. The approach relies on formulae for lower and upp...
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We present an approach to interactive multiple Criteria Decision Making based on preference driven Evolutionary Multiobjective Optimization with controllable accuracy. The approach relies on formulae for lower and upper bounds on coordinates of the outcome of an arbitrary efficient variant corresponding to preference information expressed by the Decision Maker. In contrast to earlier works on that subject, here lower and upper bounds can be calculated and their accuracy controlled entirely within evolutionary computation framework. This is made possible by exploration of not only the region of feasible variants - a standard within evolutionary optimization, but also the region of infeasible variants, the latter to our best knowledge being a novel approach within Evolutionary Multiobjective Optimization. To illustrate how this concept can be applied to interactive multiple Criteria Decision Making, two algorithms employing evolutionary computations are proposed and their usefulness demonstrated by a numerical example. (C) 2011 Elsevier B.V. All rights reserved.
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