Order batching optimization in automated warehouses with metaheuristics
Résumé
Order picking is a cost consuming activity. Before picking the customer demands, consolidating orders into batches can contribute to decrease these costs. In this study, we will focus on order batching optimization in automated warehouses, where Vertical Lift Modules (VLM) are used to store and retrieve products. The treated order batching problem deals with the question of how to combine orders into batches in such a way the total picking time is minimized. This problem is recognized as NP-Hard and its optimal resolution is difficult with large-scale instances and within acceptable computation time. To overtake this issue, metaheuristics are applied: The Tabu Search and the Simulated Annealing algorithm. Their performance is analyzed for different instances and evaluated regarding both computation time and solution quality. We will show that the proposed approaches are able to provide powerful solutions that enable VLMs to operate effectively.