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Stochastic approach versus multiobjective approach for obtaining efficient solutions in stochastic multiobjective programming problems

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2002-09
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Instituto Complutense de Análisis Económico. Universidad Complutense de Madrid
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In this work, we deal with obtaining efficient solutions for stochastic multiobjective programming problems. In general, these solutions are obtained in two stages: in one of them, the stochastic problem is transformed into its equivalent deterministic problem, and in the other one, some of the existing generating techniques in multiobjective programming are applied to obtain efficient solutions, which involves transforming the multiobjective problem into a problem with only one objective function. Our aim is to determine whether the order in which these two transformations are carried out influences, in any way, the efficient solution obtained. Our results show that depending on the type of stochastic criterion followed and the statistical characteristics of the initial problem, the order can have an influence on the final set of efficient solutions obtained for a given problem.
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Ben Abdelaziz, F., 1992. L’efficacité en Programmation Multi-objectifs Stochastique. Ph. D. Thesis, Université de Laval, Québec. Ben Abdelaziz, F., Lang, P., Nadeau, R., 1997. Distributional Unanimity Multiobjective Stochastic Linear Programming. In: Climaco, J. (Ed.: Multicriteria Analysis: Proceedings of the With Conference on MCDM, pp. 225-236. Springer-Verlag. Ben Abdelaziz, F., Lang, P., Nadeau, R., 1999. Dominance and Efficiency in Multicriteria Decision under Uncertainty. Theory and Decision, 47, 191-211. Caballero, C., Cerdá, E., Muñoz, M.M., Rey, L., 2000. Relations among Several Efficiency Concepts in Stochastic Multiple Objective Programming. Research and Practice in Multiple Criteria Decision Making, Edited by Y. Y. Haimes and R. Steuer, Lectures Notes in Economics and Mathematical Systems, Springer-Verlag, Berlin, Germany, Vol. 487, 57-68. Caballero, R., Cerdá, E., Muñoz, M.M., Rey, L., Stancu Minasian, I. M., (2001), Efficient Solution Concepts and Their Relations in Stochastic Multiobjective Programming. Journal of Optimization, Theory and Applications, Vol. 110, 1, 53-74. Chankong, V., Haimes, Y.Y., 1983. Multiobjective Decision Making: Theory and Methodology. North-Holland, New York. Goicoechea, A., Hansen, D. R., Duckstein, L., 1982. Multiobjective Decision Analysis with Engineering and Business Applications. John Wiley and Sons, New York. Hogg, R. V., Craig, A. T., 1989. Introduction to Mathematical Statistics. MacMillan Publishing Co., New York. Kall, P., Wallace, S.W., 1994. Stochastic Programming. John Wiley and sons, Chichester. Liu, B., Iwamura, K., 1997. Modelling Stochastic Decision Systems Using Dependent-Chance Programming. European Journal of Operational Research, 101, 193-203. Prékopa, A., 1995. Stochastic Programming. Kluwer Academic Publishers. Dordrecht. Sawaragi, Y., Nakayama H., Tanino T., 1985. Theory of Multiobjective Optimization. Academic Press, New York. Slowinski, R., Teghem, J. (Ed.), 1990. Stochastic Versus Fuzzy Approaches to Multiobjective Mathematical Programming Under Uncertainty. Kluwer Academic Publishers, Dordrecht. Stancu-Minasian, I. M., 1984. Stochastic Programming with Multiple Objective Functions. D. Reidel Publishing Company, Dordrecht. Stancu-Minasian, I., Tigan, S., 1984. The Vectorial Minimum Risk Problem. Proceedings of the Colloquium on Approximation and Optimization. Cluj-Napoca, 321-328. White, D. J., 1982. Optimality and Efficiency. John Wiley and Sons, Chichester.