Sustainable Reconfigurable Manufacturing System Design Using Adapted Multi-Objective Evolutionary Based Approaches

Nowadays, manufacturing systems should be cost-effective and environmentally harmless to cope with various challenges in today's competitive markets. In this paper, we aim to solve an environmental oriented multi-objective reconfigurable manufacturing system design (ie., sustainable reconfigurable machines and tools selection) in the case of a single unit process plan generation. A non-linear multi-objective integer program (NL-MOIP) is presented first, where four objectives are minimised respectively, the total production cost, the total production time, the amount of the greenhouse gases emitted by machines and the hazardous liquid wastes. Second, to solve the problem, we propose four adapted versions of evolutionary approaches, namely two versions of the well known non-dominated sorting genetic algorithm (NSGA-II and NSGA-III), weighted genetic algorithms (WGA) and random weighted genetic algorithms (RWGA). To illustrate the efficiency of the four approaches, several instances of the problem are experimented and the obtained results are analysed using three metrics respectively hypervolume, spacing metric and cardinality of the mixed Pareto fronts. Moreover, the influences of the probabilities of genetic operators on the convergence of the adapted NSGA-III are analysed and TOPSIS method is used to help the decision maker ranking and selecting the best process plans.