A genetic algorithm based fuzzy goal programming solution approach to chance constrained bilevel programming problems

In this article, a genetic algorithm (GA) based fuzzy goal programming (FGP) procedure for solving bilevel programming problems (BLPPs) having the chance constraints in large hierarchical decision problems is presented. In the proposed approach, first the chance constraints are converted into their deterministic equivalent in the decision making environment. In the model formulation of the problem, the concept of tolerance membership functions for measuring the degree of satisfaction of the decision makers (DMs) regarding achievement of their fuzzily described objective goals as well as the degree of optimality of the decision vector controlled by the upper-level DM are defined in the decision making horizon. In the solution process, the GA method is employed to solve the FGP model of the problem to make a reasonable balance of execution of decision powers of the DMs in the decision making environment. A numerical example is solved to illustrate the proposed approach.