Improving order picking efficiency through storage assignment optimization in robotic mobile fulfillment systems

The order picking efficiency in robotic mobile fulfillment systems is not only determined by the order and rack processing sequences, but also by the product distribution on the racks. In this paper, we focus on long-term planning based on historical order data to identify the optimal distribution of SKUs on racks for order picking operation improvement. This product distribution problem is formulated as an integer linear program, with the goal of minimizing rack movements required to fulfill orders. We develop a solution procedure that takes into account both the affinity and frequency of products in the orders, as well as the corresponding performance of the order and rack sequencing. We evaluate the performance of the proposed method through comparisons with the theoretical optimal solution on manually generated small instances and with existing heuristic solutions on a large real-world order dataset provided by a major e-commerce company, demonstrating a significant improvement from using our algorithm.