An interactive fuzzy satisficing method based on fractile criterion optimization for multiobjective stochastic integer programming problems

In this paper, we focus on multiobjective integer programming problems involving random variable coefficients in objective functions and constraints. Using the concept of chance constrained conditions, such multiobjective stochastic integer programming problems are transformed into deterministic ones based on the fractile criterion optimization model. As a fusion of stochastic programming and fuzzy one, we introduce fuzzy goals representing the ambiguity of the decision maker's judgments into them and define M-@q-efficiency, a new concept of efficient solution, as a fusion of stochastic approaches and fuzzy ones. Then, we construct an interactive fuzzy satisficing method using genetic algorithms to derive a satisficing solution for the decision maker which is guaranteed to be M-@q-efficient by updating the reference membership levels. Finally, the efficiency of the proposed method is demonstrated through numerical experiments.

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