Simulation-based optimization approach for simultaneous scheduling of vehicles and machines with processing time uncertainty in FMS

Abstract

Many stochastic factors, such as vehicle congestion, deadlock or conflict, or stochastic processing time have significant effects on performance in scheduling problem in flexible manufacturing system (FMS). This paper proposed a simulation-based optimization, L-GAOCBA, to address the simultaneous scheduling of vehicles and machines in FMS. The simulation model is constructed to evaluate the performance of scheduling decision, and includes stochastic elements, such as vehicle congestion, deadlock, and uncertain processing time. Genetic algorithm (GA) combined with local search, L-GA, plays important role in exploring the good design alternative based on simulation output. Optimal computing budget allocation (OCBA) embedded with L-GA is used to allocate the number of replications for reducing simulation replications. The design of experiments is used to analyze and set the parameters of L-GA and OCBA. This study shows that L-GAOCBA is superior for enhancing solution quality and search efficiency.