Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/37567
Title: Integrated scheduling of order picking operations under dynamic order arrivals
Authors: D'HAEN, Ruben 
BRAEKERS, Kris 
RAMAEKERS, Katrien 
Issue Date: 2022
Publisher: TAYLOR & FRANCIS LTD
Source: INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH,
Status: Early view
Abstract: To remain competitive in the current e-commerce environment, warehouses are expected to handle customer orders as efficiently and quickly as possible. Previous research on order picking in a static context has shown that integrating batching, routing and scheduling decisions leads to better results than addressing these planning problems individually. In this study we propose an integrated solution approach that is able to deal with dynamic order arrivals, a problem often encountered in practice. Furthermore, we demonstrate the need to anticipate on future order arrivals to keep customer service levels high. We develop a new large neighbourhood search algorithm to solve the online, integrated batching, routing and scheduling problem. First, the algorithm is shown to outperform the current state-of-the-art static solution algorithm. Next, we develop an experimental design based on real-life data, to test the applicability of the model in different settings. The results of this experimental design are used to obtain insights on the particularity of this online, integrated problem. The effect of several real-life characteristics is demonstrated by using an ANOVA, leading to several managerial insights that may help companies to operate efficiently without jeopardising customer satisfaction.
Notes: D'Haen, R (corresponding author), UHasselt, Res Grp Logist, Agoralaan Bldg D, B-3590 Diepenbeek, Belgium.; D'Haen, R (corresponding author), Res Fdn Flanders FWO, Egmontstr 5, B-1000 Brussels, Belgium.
ruben.dhaen@uhasselt.be
Keywords: Order batching;order picking;picker routing;metaheuristics;warehouse operations management
Document URI: http://hdl.handle.net/1942/37567
ISSN: 0020-7543
e-ISSN: 1366-588X
DOI: 10.1080/00207543.2022.2078747
ISI #: WOS:000804600700001
Rights: 2022 Informa UK Limited, trading as Taylor & Francis Group
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

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