Performance analysis of batching decisions in waveless order release environments for e‐commerce stock‐to‐picker order fulfillment

Warehouse automation is increasingly adopted to manage throughput fluctuations in e‐commerce order fulfillment. This work develops queuing network models and solution methodologies for performance analysis of a stock‐to‐picker system that connects an upstream automated storage system to a downstream pick station. We focus on the pick station process, quantifying the throughput differences between a pick station that employs a static versus dynamic batching strategy. We consider a waveless order release environment where the item totes are requested from the storage system only when an order arrives. We capture throughput performance in this environment for single as well as multi‐line orders with/without item commonality by developing closed‐queuing network models. The consolidation of multiple product‐lines for a multi‐line order is modeled using fork–join synchronization stations within the closed queuing network. For analyzing such queuing networks, we develop a network‐decomposition based solution methodology. We validate the models using a simulation model of the upstream storage and downstream order‐picking system. We find that in waveless order release environment, dynamic batching always outperforms static batching in terms of system throughput. However, for single‐line orders, the percentage gain in the throughput (by implementing dynamic batching) decreases for smaller item tote inter‐arrival times. For multi‐line orders, dynamic batching increases the system throughput by 37–43%. We also analyze the effect of batch size on the throughput performance. The results indicate that the system throughput increases with an increase in the batch size under both batching policies. But, the marginal benefit reduces as we increase the batch size.