Enhancing E-Commerce RMFS Order Fulfillment Through Pod Positioning with Jointly Optimized Task Allocation

Robotic mobile fulfillment systems have become an integral part of e-commerce warehouses. The pod repositioning problem, due to its interdependence with robot task allocation strategies, poses a significant challenge that constrains system performance. In this paper, we aim to jointly optimize the two interrelated problems of pod repositioning and task allocation. A multi-objective mixed-integer planning model is developed to minimize the maximum completion time of robots and the deviation between the pod position and the expected position. To tackle the challenges of decision coupling and a vast solution space, an adaptive genetic-neighborhood search algorithm guided by pod heat maps is designed. Additionally, to promptly correct expected layout deviations and avoid layout instability, a progressive storage mechanism is designed to update the expected layout. The numerical experiments show that compared to the staged optimization strategy, the joint optimization strategy proposed in this paper can reduce the maximum completion time by approximately 48%, and that the strategy reduces the maximum completion time by 9% to 16% compared to the nearest allocation strategy, which is commonly used and performs best in practice.