Optimal Picking Policies in E-Commerce Warehouses

• 成果类型: Article
• 署名作者: Schiffer, Maximilian; Boysen, Nils; Klein, Patrick S.; Laporte, Gilbert; Pavone, Marco
• 署名单位: Technical University of Munich; Technical University of Munich; Friedrich Schiller University of Jena; Universite de Montreal; HEC Montreal; University of Bath; Stanford University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2021.4275
• 发表日期: 2022
• 页码: 7497-7517
• 关键词: mixed-shelves warehouse; order picking policies; picking zone layout; dynamic programming

摘要

In e-commerce warehouses, online retailers increase their efficiency by using a mixed-shelves (or scattered storage) concept, where unit loads are purposefully broken down into single items, which are individually stored in multiple locations. Irrespective of the stock keeping units a customer jointly orders, this storage strategy increases the likelihood that somewhere in the warehouse the items of the requested stock keeping units will be in close vicinity, which may significantly reduce an order picker's unproductive walking time. This paper optimizes picker routing through such mixed-shelves warehouses. Specifically, we introduce a generic exact algorithmic framework that covers a multitude of picking policies, independently of the underlying picking zone layout, and is suitable for real-time applications. This framework embeds a bidirectional layered graph algorithm that provides the best known performance for the simple picking problem with a single depot and no further attributes. We compare three different real-world e-commerce warehouse settings that differ slightly in their application of scattered storage and in their picking policies. Based on these, we derive additional layouts and settings that yield further managerial insights. Our results reveal that the right combination of drop-off points, dynamic batching, the utilization of picking carts, and the picking zone layout can greatly improve the picking performance. In particular, some combinations of policies yield efficiency increases of more than 30% compared with standard policies currently used in practice.