Optimal Picking Policies in E-Commerce Warehouses

Maximilian Schiffer (), Nils Boysen (), Patrick S. Klein (), Gilbert Laporte () and Marco Pavone ()
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Maximilian Schiffer: School of Management, Technical University of Munich, 80333 Munich, Germany; Munich Data Science Institute, Technical University of Munich, 80333 Munich, Germany
Nils Boysen: Operations Management, Friedrich Schiller Universität Jena, 07743 Jena, Germany
Patrick S. Klein: School of Management, Technical University of Munich, 80333 Munich, Germany
Gilbert Laporte: HEC Montréal, Montréal, Quebec H3T 2A7, Canada; School of Management, University of Bath, Bath BA2 7AY, United Kingdom
Marco Pavone: Department of Aeronautics and Astronautics, Stanford University, Stanford, California 94035

Management Science, 2022, vol. 68, issue 10, 7497-7517

Abstract: 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.

Keywords: mixed-shelves warehouse; order picking policies; picking zone layout; dynamic programming (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (8)

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