A new memetic algorithm for optimizing the partitioning problem of tandem AGV systems

In tandem automated guided vehicle (AGV) systems, all the work stations (processing centers) are partitioned into non-overlapping zones, where each zone is served by a dedicated vehicle. Beside the system input/output stations, additional pickup/drop-off (P/D) points are installed to link these zones as transfer points. In this paper, a new memetic algorithm (MA) is proposed to optimize the partitioning problem of tandem AGV systems. MAs are hybrid evolutionary algorithms (EAs) that combine the global and local search by using a genetic algorithm (GA) to perform exploration and a local search method to perform exploitation. Hence, a local search method has been designed and combined with a GA to refine the individuals of the population (i.e. improve their fitness). The objective is to minimize the maximum AGVs' workload in order to balance the workload among all the zones and hence avoid the presence of bottlenecks. The MA performance is evaluated by comparing the obtained results with the reported results in the literature as well as the pure GA. Furthermore, some newly designed test cases are proposed and solved using both, MA and GA. The results show that, overall, the developed MA outperforms both pure GA and the other reported methods in the literature.