AN INTERACTIVE FUZZY SATISFICING METHOD FOR MULTIOBJECTIVE 0-1 PROGRAMMING PROBLEMS THROUGH REVISED GENETIC ALGORITHMS

摘要

In this paper, an interactive fuzzy satisficing method for a multiobjective 0-1 programming problem is presented by incorporating the desirable features of both the interactive fuzzy programming methods and genetic algorithms. By considering the imprecise nature of human judgements, the fuzzy goals of the decision maker (DM) for objective functions are quantified by eliciting linear membership functions. If the DM specifies the reference membership levels for all of the membership functions, the corresponding Pareto optimal solution which is, in the minimax sense, nearest to the requirement can be obtained by solving the minimax problem. To generate Pareto optimal solutions by applying the proposed genetic algorithm which is modified to generate only feasible solutions, the algorithm is further revised by providing some new mechanism for forming an initial population after the first interaction with the DM. Illustrative numerical examples demonstrate the both feasibility and efficiency of the proposed methods.