Iterated Greedy Local Search for the Order Picking Problem Considering Storage Location and Order Batching Decisions

Abstract: Order picking is the process of collecting products from a specific location to complete customer orders. Two related activities that affect the order picking are the storage location of products and the order batching. In the first, the decision is where to place items arriving at the warehouse and, in the second, the decision is to group multiple customer orders into a single batch. Although these three decisions are typically addressed separately, their integration may result in an enhanced decision-making process. We study an integrated picking problem that considers these three decisions simultaneously. The aim is to obtain the best storage location of products, order batching, and picking routing that minimizes the total picking routing cost. We propose a mathematical model and an iterated greedy local search metaheuristic. The proposed approaches were assessed on pseudo-real and real-world instances. The results show that the mathematical model, solved using a general-purpose optimization solver, can generate feasible solutions for instances with up to 100 order lines in approximately 14 400 s. In contrast, the proposed metaheuristic efficiently solves instances with up to 8197 order lines in less than 4200 s, achieving improvement gaps of up to 87.2% for artificial instances. For real-world instances, the improvement reaches up to 56% compared to the solver results. An analysis was conducted under different demand scenarios showing that the metaheuristic obtained the best performance in most of the cases.