Solving multi-objective production scheduling problems using metaheuristics

Most of research in production scheduling is concerned with the optimization of a single criterion. However the analysis of the performance of a schedule often involves more than one aspect and therefore requires a multi-objective treatment. In this paper we first present (Section 1) the general context of multi-objective production scheduling, analyze briefly the different possible approaches and define the aim of this study i.e. to design a general method able to approximate the set of all the efficient schedules for a large set of scheduling models. Then we introduce (Section 2) the models we want to treat--one machine, parallel machines and permutation flow shops--and the corresponding notations. The method used--called multi-objective simulated annealing--is described in Section 3. Section 4 is devoted to extensive numerical experiments and their analysis. Conclusions and further directions of research are discussed in the last section.