A deep learning approach for the selection of an order picking system

Jelmer Pier van der Gaast and Felix Weidinger

European Journal of Operational Research, 2022, vol. 302, issue 2, 530-543

Abstract: This paper develops a novel strategic decision support framework for the design of an order picking system, which can be used whenever different systems and/or control mechanisms need to be compared for a given customer order structure. Warehousing companies frequently struggle in selecting the most suitable design for their order picking system. Traditionally, a comparison of different order picking systems is based on time-consuming simulation runs. In addition, the only source of consultancy is most often carried out by the order picking system manufacturers themselves. Our framework, using recent advancements in deep neural networks, provides an efficient methodology for selecting not only the best order picking system for a given order structure but also the most suited design parameters. This enables warehouse companies to compare objectively an extensive number of systems and allows them to identify the most promising order picking systems. We demonstrate our framework for a comprehensive comparison of three different fixed-path order picking systems to find one best suited for a provided order structure.

Keywords: Logistics; Order picking; Warehouse management; Deep learning; System selection (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221722000042
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:302:y:2022:i:2:p:530-543

DOI: 10.1016/j.ejor.2022.01.006

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().