Chance-Constrained Approaches for Multiobjective Stochastic Linear Programming Problems

Justin Dupar Busili Kampempe, Monga Kalonda Luhandjula

Department of Decision Sciences, University of South Africa, Pretoria, South Africa.
Department of Mathematics and Computer Science, University of Kinshasa, Kinshasa, D.R. Congo.

DOI: 10.4236/ajor.2012.24061                

Abstract

Multiple objective stochastic linear programming is a relevant topic. As a matter of fact, many practical problems ranging from portfolio selection to water resource management may be cast into this framework. Severe limitations on objectivity are encountered in this field because of the simultaneous presence of randomness and conflicting goals. In such a turbulent environment, the mainstay of rational choice cannot hold and it is virtually impossible to provide a truly scientific foundation for an optimal decision. In this paper, we resort to the bounded rationality principle to introduce satisfying solution for multiobjective stochastic linear programming problems. These solutions that are based on the chance-constrained paradigm are characterized under the assumption of normality of involved random variables. Ways for singling out such solutions are also discussed and a numerical example provided for the sake of illustration.

Keywords

Satisfying Solution; Chance-Constrained; Multiobjective Programming; Stochastic Programming

Conflicts of Interest

The authors declare no conflicts of interest.

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