Item storage reassignment problem in robotic mobile fulfillment systems under customer order characteristic fluctuations

This article studies the item storage reassignment problem (ISRP) in the robotic mobile fulfilment system (RMFS). As the demand pattern evolves, the item storage assignment should be adapted accordingly to avoid potential workload. Hence, the item storage reassignment becomes an indispensable process of warehouse operations. This paper proposes an Interchange-Guided Sequencing Algorithm (IGSA) that identifies the interchange dependencies between item groups and constructs the corresponding group interchange sequence. Computational experiments demonstrate the effectiveness of the proposed algorithm and further analyse when reassignment should be performed under different item varieties and warehouse capacities. E-commerce warehouses using robotic mobile fulfilment systems already rely on robots to ease picking pressure, but they still face the challenge of constantly changing customer order preferences. The closer the item storage assignment matches current order demand, the fewer pod movements are required during picking. When the original and new demand patterns differ significantly, robots may need to retrieve more pods, increasing travel distance and reducing overall system productivity. Evolving customer demand eventually renders existing storage assignments inefficient, but frequent adaptation to restore efficiency risks creating additional pod movements if not handled carefully. This study introduces a practical method for reorganising item storage by identifying which product groups should exchange their items and determining an efficient sequence for completing these exchanges. Through computational experiments, we further examine warehouses of different product varieties and storage capacities, and we offer practical recommendations on suitable timing for performing storage reassignment. Overall, this study helps practitioners reduce unnecessary pod movements and maintain high picking efficiency under changing demand conditions.