Enhancing E-Commerce Warehouse Order Fulfillment Through Predictive Order Reservation Using Machine Learning

Order batching plays a pivotal role in enhancing order fulfillment efficiency within both manual and robotic warehousing systems. Rare attention has been devoted to the impact of future incoming orders on online order batching. This study addresses this gap by exploring the potential benefits of reserving suitable orders when upcoming orders share similarities with existing orders in the order pool. Specifically, we investigate the online order batching problem with predictive order reservation, employing the Ensemble Learning method, to predict similarities between current and future orders. Our proposed approach involves deliberate reservation of certain orders upon arrival, deferring their batching to a subsequent period for additional efficiency gains. To operationalize this predictive order reservation, we develop an algorithmic framework that comprehensively addresses online order batching, encompassing batching, sequencing, and assignment. Experimental results, conducted on real data from an e-commerce warehouse, demonstrate the superiority of our proposed approach over fixed and variable time-window online batching algorithms in terms of order turnover time, with improvements of up to 6.1%. Notably, the benefits are more pronounced when the order arrival rate aligns with the available picking resources.