A simulation-based optimisation framework for process plan generation in reconfigurable manufacturing systems (RMSs) in an uncertain environment

Reconfigurable manufacturing system (RMS) is a manufacturing paradigm which is proven to be time and cost-effectively adaptable to a wide range of market changes due to its customisable capacity and functionality. In this paper, a two-step framework is proposed for the process plan generation in RMS. The first step, aimed at solving the multi-objective part-family Single-Unit Process Plan (SUPP) generation problem, involves a comparative approach using three metaheuristics, namely: The Non-Dominated Sorting Genetic Algorithm (NSGA-II), the Archived Multi-Objective Simulated Annealing (AMOSA) and the Multi-Objective Particle Swarm Optimisation (MOPSO) as well as a simulation-based optimisation method. The second step is designed to solve the multi-objective part-family Multi-Unit Process Plan (MUPP) generation problem with unpredictable demands in different periods using a combination of the answers of the algorithms in step 1. The number of units is also optimised using the NSGA-II. Finally, a novel heuristic algorithm named Designed Periods Algorithm (DPA) is proposed in the second step to meet the unpredictable demands in different periods. To illustrate the applicability of the framework, an example is presented, the results of which have shown the superiority of the MUPP over the SUPP in response to unpredictable demands according to the periods designed by DPA.