Genetic algorithms for the multiple-machine economic lot scheduling problem

This paper focuses on an extension of the Economic Lot Scheduling Problem, which schedules productions of products on multiple identical machines. The objective is to minimize the total average production and inventory costs per unit time for all products. We develop a genetic algorithm under the Common Cycle policy and compare it with an existing heuristic under the same policy. Computational results show that our genetic algorithm outperforms the existing heuristic and its running time does not increase much even for high utilization problems, while the latter requires substantial time to solve most of the high utilization problems. In addition, a genetic algorithm under the Extended Basic Period and Power-of-Two policy is proposed. This new heuristic performs much better, especially when the number of machines is small and the machine utilization is not very high.