Simulation studies for credibility-based multi-objective linear programming problems with fuzzy parameters

Abstract

In this paper, hybrid credibility-based multi-objective linear programming models are provided to optimize expected values of objective functions subject to fuzzy chance-constraints. Triangular or non-linear fuzzy numbers are considered in problem parameters like demands and costs. To handle the uncertainty, the constraints are substituted with credibilistic fuzzy chance-constraints and the objective functions with their expected values. The credibilistic approach offers computational ease by the use of techniques which are similar to the stochastic simulation and applicable to all types of fuzzy numbers. The approach uses expected values and chance-constraints respectively to handle uncertain objective functions and to control the confidence level of fulfilling imprecise constraints. Numerical simulations are presented to compare the expected objective function values.