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.
Highlights
We resort to the bounded rationality principle to introduce satisfying solution for multiobjective stochastic linear programming problems
They are goal directed and they are intended to pursue those goals in conformity with the classic homo-economicus model [1]. They do not always succeed because of the complexity of some environments that impose both procedural and substantive limits. This is the case when dealing with multiobjective stochastic linear programming (MSLP) problems
A look at the literature reveals that most existing solution concepts for MSLP problems rely heavily on the expected, pessimistic and optimistic values of involved random variables [2,3]
Summary
They are goal directed and they are intended to pursue those goals in conformity with the classic homo-economicus model [1] They do not always succeed because of the complexity of some environments that impose both procedural and substantive limits. This is the case when dealing with multiobjective stochastic linear programming (MSLP) problems. We discuss satisfying solution concepts for MSLP problems. These concepts are based on the chance-constrained philosophy [8].
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