Synchronizing order picking and delivery in e-commerce warehouses under community logistics

The interplay between inbound order picking and outbound delivery operations is imperative. Nevertheless, these two operations are generally studied in a separate manner, and most existing studies on inbound order picking do not consider the influence of picking operations on downstream delivery operations. This paper addresses the order picking problem in e-commerce warehouses from a new perspective, i.e., towards achieving picking and delivery synchronization. We define a Dynamic Order Picking Problem with Delivery Decisions (DOPP-DD), where picking decisions are dynamically determined, given periodically received community logistics (CL) delivery decisions, to minimize vehicle waiting time at loading docks. The DOPP-DD is modeled as a Markov Decision Process and a selective order picking (SOP) policy is proposed to solve it, leveraging a convolutional neural network (CNN) to predict near-future delivery decisions. Our numerical study showcases the CNN's high accuracy toward predicting the CL delivery decisions. We compare the performance of the SOP against three benchmark policies across 23 instances. The results reveal that the SOP policy outperforms the others in instances with limited picking capability and demonstrates higher robustness toward limited picking resources, highlighting its potential to alleviate overstock issues and foster a more synchronized workflow in order picking and delivery operations.