A hybrid genetic algorithm for simultaneous scheduling of machines and AGVs in FMS

Abstract

In this paper, simulation optimization approach for simultaneous scheduling of machines and identical automated guided vehicles with minimizing the makespan in flexible manufacturing system is presented. In the past time, this problem has been solved by using heuristic algorithm such as genetic algorithms (GA), particle swarm optimization (PSO) etc. Actually, many factors should be considered in real situation such as deadlock or blockage of AGV and processing time with uncertainty in FMS. This will impact the system performance significantly. Hence, discrete event simulation model is used to evaluate the system performances which consider those random factors and to compare alternatives. In addition, optimal computing budget allocation (OCBA) embedded with GA is used to reduce simulation replications and provide reliable evaluations and identified for ranking chromosomes of the GA procedure. As a result, we prove those random factors affect system performance significantly. The numerical experiment results demonstrate the superiority of the hybrid approach in terms of computing cost for this problem.