A New Possibilistic Programming Approach For Solving Fuzzy Multiobjective Assignment Problem

In this paper, we propose a new possibilistic programming approach to solve a fuzzy multiobjective assignment problem in which the objective function coefficients are characterized by triangular possibility distributions. The proposed solution approach simultaneously minimizes the best scenario, the likeliest scenario, and the worst scenario for the imprecise objective functions using α-level sets. The α-level sets are used to define the confidence level of the fuzzy judgments of the decision maker. Additionally, we provide a systematic framework in which the decision maker controls the search direction by updating both the membership values and aspiration levels until a set of satisfactory solutions is obtained. Numerical examples, with dataset from realistic situations, are provided to demonstrate the effectiveness of the proposed approach.