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.
Note to Practitioners
—This paper was motivated by the practical problem of predictively reserving orders to improve the holistic efficiency of online batch picking in e-commerce warehouses. Existing approaches generally have assumed that warehouse management system (WMS) releases all orders in the order pool to generate the order batch and not considered the potential benefits of order reservation. This paper proposes a new online order batching approach with order reservation using machine learning. The proposed approach could bring many benefits to practitioners. Firstly, the proposed algorithm could improve the quality of order batch and could easily be embedded in the existing WMS without interference to current operation process. Secondly, the proposed algorithm framework could be easily re-configured to cover diverse scenarios of operation strategy combination and used to evaluate the performance of different methods. Thirdly, the proposed online order batching approach with order reservation using machine learning can be used to various order picking systems including manual order picking system, human-robot collaborative order picking system and robotic order picking system. In future research, we will address the order reservation mechanism considering different objective functions, such as minimizing tardy orders or variance of order turnover time and explore its applicable scenarios.