Coupling local search methods and simulated annealing to the job shop scheduling problem with transportation

This paper addresses the job and device scheduling problems in flexible manufacturing systems (FMS) using an automated guided vehicle system (AGV) by simultaneously dealing with material processing and transportation functions. The problem is solved using a two stage iterative approach which includes optimization and computer simulation. An iterative procedure is developed. At the first stage, a meta-heuristic determines the AGV schedule, i.e., the order in which the job transfers by AGVs are made. At the second stage, a discrete event simulation model is used to evaluate the makespan depending on the AGVs schedule. This evaluation is used by the meta-heuristic to improve the initial AGV schedule. Finally, the iterative procedure determines jobs and AGV schedules which minimize the makespan (the schedule length). The investigated meta-heuristics are based on the iterated local search method and simulated annealing. An efficient neighboring system used inside meta-heuristic schemes is proposed. Our approach is numerically tested under different experimental conditions.