Pareto-optimality approach based on uniform design and fuzzy evolutionary algorithms for flexible job-shop scheduling problems (FJSPs)

In our previous work (2002), we have proposed a Pareto-optimality approach for solving multiobjective optimization problems (MOPs) based on the hybridization of fuzzy logic and evolutionary algorithms (EAs). Such an approach makes it possible to construct a set of satisfactory solutions in order to provide flexibility to the decision-maker. In this work, we aim to enhance the suggested approach and propose a new variant of such a hybridization. Thereafter, we show how a uniform design can be used for finding a set of Pareto optimality solutions uniformly scattered. We briefly describe the Pareto-optimality concepts used for solving MOPs and those especially applied in EAs. Then, the mathematical formulation of FJSP is presented. The proposed hybrid approach is described. We illustrate the suggested approach by applying it to solving FJSP and highlights some practical aspects of the application of such an approach for solving hard combinatorial problems. Finally, we conclude with some future research directions.