Batch (wave) release policies are prevalent in warehouses with an automated sorter, and take different forms depending on how batches released consecutively may overlap downstream in the sorter. Continuous (waveless) release constitutes an emerging alternative recently adopted by several firms. Although that new policy presents several advantages relative to waves, it requires more expensive technology and involves the possibility of congestion-induced collapse (gridlock) at the sorter. Using an extensive data set of detailed warehouse flow information from a leading U.S. online retailer, we first develop a model with validated predictive accuracy for a warehouse operating under waveless release. We then use that model to compute operational guidelines for dynamically managing the main control lever of that policy with the goal of maximizing throughput while keeping the risk of gridlock under a specified threshold. Second, we leverage that model and data set to compare the performance of wave-based and waveless policies through simulation. The best waveless policy yields larger or equal throughput than the best wave-based policy in all scenarios considered, and thus appears to merit some consideration by practitioners.
