In the aftermath of the COVID-19 pandemic, supply chains experienced an unprecedented challenge to fulfill consumers’ demand. As a vital operational component, manual order picking operations are highly prone to infection spread among the workers, and thus, susceptible to interruption. This study revisits the well-known order batching problem by considering a new overlap objective that measures the time pickers work in close vicinity of each other and acts as a proxy of infection spread risk. For this purpose, a multi-objective optimization model and three multi-objective metaheuristics with an effective seeding procedure are proposed and are tested on the data obtained from a major US-based logistics company. Through extensive numerical experiments and comparison with the company’s current practices, the results are discussed, and some managerial insights are offered. It is found that the picking capacity can have a determining impact on reducing the risk of infection spread through minimizing the picking overlap.

在COVID-19大流行之后，供应链面临着满足消费者需求的前所未有的挑战。作为重要的操作组件，人工接单操作很容易在工人之间传播感染，因此容易受到干扰。这项研究通过考虑一个新的重叠目标来重新审视众所周知的订单分批问题，该目标可以测量时间选择器在彼此附近工作并充当感染传播风险的代理。为此，提出了具有有效播种程序的多目标优化模型和三种多目标元启发法，并对从美国一家大型物流公司获得的数据进行了测试。通过广泛的数值实验，并与该公司的现行做法进行比较，讨论了结果，并提供一些管理见解。发现拣选能力可以通过使拣选重叠最小化而对降低感染传播的风险具有决定性的影响。