Joint Retrieval Position Selection and Picking Station Assignment for Order Picking in Autonomous Cube Storage Systems

Abstract

Autonomous cube storage systems require coordinated decisions on where requested items should be retrieved and to which picking station an order should be assigned. This paper proposes an integrated decision approach that jointly considers retrieval-position selection and picking-station assignment based on transport effort, station congestion, and vertical access effort. Under additive cost assumptions, the resulting order-level surrogate model can be solved exactly with linear rather than exponential computational effort. The approach is evaluated in a discrete-event simulation against two sequential reference strategies under dynamic traffic, queuing, and robot interaction effects. Under baseline conditions, the integrated strategy achieves the lowest mean order processing time and the highest throughput, with statistically significant differences between all three strategies for both primary performance indicators. Its performance advantage persists across all scalability scenarios and increases with system size, reaching a 5.1% reduction in mean order processing time and a 5.2% increase in throughput relative to the Station-First strategy in the largest scenario. The gains are associated with lower realized robot travel time and higher station utilization. However, the integrated strategy also results in a more uneven distribution of stack heights, revealing a trade-off between immediate operational efficiency and the longer-term spatial configuration of inventory.