An efficient technique for solving fully fuzzified multiobjective stochastic programming problems

Abstract

This paper develops a fuzzy programming technique for solving multiobjective stochastic programming problems having right side parameters associated with the system constraints follow exponential distribution. In the model formulation process the coefficients of the objectives as well as the system constraints are taken as fuzzy numbers. The variables are also considered as fuzzy variables. In the proposed solution process the probabilistic problem is first converted into an equivalent fuzzy programming model applying chance constrained programming methodology. Then using the concept of ranking function the problem is transferred into an equivalent deterministic model. The individual optimal value of each objective is found in isolation to construct the membership goals of the objectives. Finally fuzzy goal programming approach is used for achieving the best compromise solution to the extent possible in the decision making context. An illustrative numerical example is given to demonstrate the efficiency of the proposed methodology.