Process Plan Generation for Reconfigurable Manufacturing Systems: Exact Versus Evolutionary-Based Multi-objective Approaches

Abstract

Future productions are facing an increasingly complex environments, customized, flexible and high-quality production. Moreover, low costs, high reactivity and high quality products are necessary criteria for industries to achieve competitiveness in nowadays market. In this context, reconfigurable manufacturing systems (RMSs) have emerged to fulfill these requirements. This chapter addresses the multi-objective process plan generation problem in RMS environment. Three approaches are proposed and compared: an iterative multi-objective integer linear program (I-MOILP) and adapted versions of the well-known evolutionary algorithms, respectively, archived multi-objective simulated annealing (AMOSA) and the non-dominated sorting genetic algorithm (NSGA-II). Moreover, in addition to the minimization of the classical total production cost and the total completion time, the minimization of the maximum machines exploitation time is considered as a novel optimization criterion, in order to have high quality products. To illustrate the applicability of the three approaches, an example is presented and the obtained numerical results are analysed.