Enhance picking viability in E-commerce warehouses under pandemic
研究疫情下电商仓库的订单分批问题，提出基于通道的构造性分批算法（ABCBA），同时考虑分散存储、分区波次批量拣选和拣货员近距离接触风险，实验表明可减少46%拣选时间和72%感染风险。
The COVID-19 pandemic has caused critical challenges for e-commerce warehouses that strive to fulfill surging customer demand while facing a high virus infection risk. Current literature on picking optimization overlooks warehouse safety under pandemic conditions. Meanwhile, scattered storage and zone-wave-batch picking have been used in parallel by many large e-commerce warehouses, these two operational policies have not been considered together in picking optimization studies. This paper fills these gaps by solving an order batching problem considering scattered storage, zone-wave-batch picking, and pickers’ proximity simultaneously. We formulate and solve the mathematical model of the discussed problem and propose the Aisle-Based Constructive Batching Algorithm (ABCBA) to help warehouses pick more efficiently and safely. Experiments with extensive datasets from a major third-party logistics (3PL) company show that, compared to the current picking strategy, ABCBA can reduce the total picking time and the virus infection risk due to pickers’ proximity by 46% and 72%, respectively. Compared to other heuristics like tabu + nLSA3 (Yang, Zhao, and Guo 2020), ABCBA gets better results using less computation time.