A suggested approach for solving fuzzy stochastic multiobjective programming problems in the case of discrete random variables

In many real-world decision-making problems, the decision-maker may have rough information about some of the random coefficients that are used in his mathematical model. Limited information is available about the uncertain probabilities that are associated with the possible different values of the independent discrete random coefficients. This paper presents a general multiobjective programming model when these coefficients exist in the constraints and the objective functions. Moreover, the desired probability levels for the probabilistic constraints are considered imprecise. The suggested approach is implemented by three sequential steps. The first is to utilise the chance-constrained approach, with imprecise probability levels, by optimising their α-cut value in order to find the best probabilities. The second is to find the most desirable values of the objective functions, while the third is to exploit the mean-variance method within the global criterion for the objective functions. A numerical example is given to illustrate the proposed methodology.