Correlation-Aware Stowage and Storage Assignment in Robotic Mobile Fulfillment Systems: Optimizing Efficiency and Picker Workload Balance

The rapid adoption of Robotic Mobile Fulfillment Systems (RMFSs) is transforming e-commerce logistics by combining robotic efficiency with human flexibility. However, substantial workload differences between picking stations present an operational challenge in these human–robot collaborative environments. Such imbalance may cause congestion, worker fatigue, and reduced system performance. This paper addresses this efficiency–equity trade-off by evaluating proactive storage planning alongside real-time operational control. We study a joint stowage and storage assignment problem that determines how SKUs should be allocated to racks and where racks should be positioned in the warehouse in order to simultaneously reduce robot travel distances and balance workloads across picking stations. To solve this problem, we develop a data-driven framework that exploits SKU correlation information to cluster frequently co-purchased items and strategically position high-demand racks within the warehouse. Using large-scale operational data from a major e-commerce retailer in China, numerical experiments demonstrate that the combined storage-and-control framework significantly improves system performance. Compared with existing benchmark approaches, the proposed method reduces robot travel distances by 23%, increases system throughput by 27%, and improves workload balance by 62.16%. These results highlight the importance of incorporating workload considerations into storage planning alongside operational control and provide practical guidance for managers seeking to design more efficient and human-centered RMFS operations under the tested operating conditions.