A Class of Level-2 Fuzzy Decision-Making Model with Expected Objectives and Chance Constraints: Application to Supply Chain Network Design

In this paper, we concentrate on dealing with a class of decision-making problems with level-2 fuzzy coefficients. We first discuss how to transform a level-2 fuzzy decision-making model with expected objectives and chance constrained into crisp equivalent models, then an interactive fuzzy satisfying method is introduced to obtain the decision makers satisfying solution. In addition, the technique of level-2 simulations is applied to deal with general level-2 fuzzy models which are usually hard to be converted into their crisp equivalents. Furthermore, based on the level-2 fuzzy programming, we focus on the supply chain network design problem where the total transport costs and the customer demands are assumed to be level-2 fuzzy numbers, a hybrid intelligent algorithm based on GA is used to solve the general supply chain design model. Finally, a numerical example and a case study are presented to illustrate the effectiveness of the model and the algorithm.