Order picking (OP) is a time and cost consuming operation in many warehouses. The optimization of order picking planning is crucial to speed up customers’ delivery process and reduce warehouse expenses. A commonly adopted strategy is called mixed-shelves storage strategy (MSSS) that is highly recommended for e-commerce warehouses. Applying this, the units of the same stock keeping unit (SKU) are scattered to various storage locations which provides more improvement room for OP. However, consolidated methods have not been sufficiently discussed when OP is under the MSSS. In this paper, we study the problem of wave picking systems to jointly address order batching, batch assignment, and picker routing (BAR) in a MSSS-based warehouse. We propose the decomposition of OP into BAR subproblems (DOPBAR) for solving such a wave OP problem. We examine our approach on a large set of instances, validate the effectiveness of our method, and point out the advantages of the MSSS in OP. Moreover, we analyze the trade-offs between two conflicting objectives that practitioners aim to optimize: customer service level (makespan) and workforce level (labor cost). Finally, a few insightful managerial recommendations are proposed that support the decision making in choosing a storage location assignment strategy.

在许多仓库中，订单拣选 (OP) 是一项耗时且成本高的操作。订单拣选计划的优化对于加快客户的交货流程和降低仓库费用至关重要。一种普遍采用的策略称为混合货架存储策略（MSSS），强烈推荐用于电子商务仓库。应用这一点，同一库存单位（SKU）的单位分散到各个存储地点，这为 OP 提供了更大的改进空间。然而，当 OP 在 MSSS 之下时，并没有充分讨论合并方法。在本文中，我们研究了波次拣货系统在基于 MSSS 的仓库中联合解决订单批处理、批次分配和拣货员路由 (BAR) 的问题。我们建议将 OP 分解为 BAR 子问题 (DOPBAR) 以解决此类波 OP 问题。我们在大量实例上检查我们的方法，验证我们方法的有效性，并指出 MSSS 在 OP 中的优势。此外，我们分析了从业者旨在优化的两个相互冲突的目标之间的权衡：客户服务水平（makespan）和劳动力水平（劳动力成本）。最后，提出了一些有见地的管理建议，以支持选择存储位置分配策略的决策。