Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/22614
Title: | The use of time series forecasting in zone order picking systems to predict order pickers’ workload | Authors: | VAN GILS, Teun RAMAEKERS, Katrien CARIS, An COOLS, Mario |
Issue Date: | 2017 | Source: | INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 55(21), p. 6380-6393 | Abstract: | In order to differentiate from competitors in terms of customer service, warehouses accept late orders while providing delivery in a quick and timely way. This trend leads to a reduced time to pick an order. This paper introduces workload forecasting in a warehouse context, in particular a zone picking warehouse. Improved workforce planning can contribute to an effective and efficient order picking process. Most order picking publications treat demand as known in advance. As warehouses accept late orders, the assumption of a constant given demand is questioned in this paper. The objective of this study is to present time series forecasting models that perform well in a zone picking warehouse. A real-life case study demonstrates the value of applying time series forecasting models to forecast the daily number of order lines. The forecast of order lines, along with order pickers’ productivity, can be used by warehouse supervisors to determine the daily required number of order pickers, as well as the allocation of order pickers across warehouse zones. Time series are applied on an aggregated level, as well as on a disaggregated zone level. Both bottom-up and top-down approaches are evaluated in order to find the best-performing forecasting method | Notes: | [van Gils, Teun; Ramaekers, Katrien; Caris, An] Hasselt Univ, Res Grp Logist, Diepenbeek, Belgium. [Cools, Mario] Univ Liege, Dept ARGENCO Local Environm Management & Anal, Liege, Belgium. | Keywords: | case study; operations planning; order picking management; forecasting; workload balancing | Document URI: | http://hdl.handle.net/1942/22614 | ISSN: | 0020-7543 | e-ISSN: | 1366-588X | DOI: | 10.1080/00207543.2016.1216659 | ISI #: | 000412561900011 | Rights: | © 2016 Informa UK Limited, trading as Taylor & Francis Group | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2018 |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Paper IJPR Accepted R1.pdf | Peer-reviewed author version | 3.48 MB | Adobe PDF | View/Open |
the use.pdf Restricted Access | Published version | 1.25 MB | Adobe PDF | View/Open Request a copy |
SCOPUSTM
Citations
13
checked on Sep 7, 2020
WEB OF SCIENCETM
Citations
35
checked on Oct 13, 2024
Page view(s)
118
checked on Sep 6, 2022
Download(s)
556
checked on Sep 6, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.