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Layout-Agnostic Order-Batching Optimization

Oxenstierna, Johan LU orcid ; Malec, Jacek LU orcid and Krueger, Volker LU orcid (2021) 12th International Conference on Computational Logistics, ICCL 2021 In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 13004 LNCS. p.115-129
Abstract

Order-batching is an important methodology in warehouse material handling. This paper addresses three identified shortcomings in the current literature on order-batching optimization. The first concerns the overly large dependence on conventional warehouse layouts. The second is a lack of proposed optimization methods capable of producing approximate solutions in minimal computational time. The third is a scarcity of benchmark datasets, which are necessary for data-driven performance evaluation. This paper introduces an optimization algorithm, SBI, capable of generating reasonably strong solutions to order-batching problems for any warehouse layout at great speed. On an existing benchmark dataset for a conventional layout, Foodmart,... (More)

Order-batching is an important methodology in warehouse material handling. This paper addresses three identified shortcomings in the current literature on order-batching optimization. The first concerns the overly large dependence on conventional warehouse layouts. The second is a lack of proposed optimization methods capable of producing approximate solutions in minimal computational time. The third is a scarcity of benchmark datasets, which are necessary for data-driven performance evaluation. This paper introduces an optimization algorithm, SBI, capable of generating reasonably strong solutions to order-batching problems for any warehouse layout at great speed. On an existing benchmark dataset for a conventional layout, Foodmart, results show that the algorithm on average used 6.9% computational time and 105.8% travel cost relative to the state of the art. New benchmark instances and proposed solutions for various layouts and problem settings were shared on a public repository.

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Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Discrete optimization, Order picking, Order-batching problem
host publication
Computational Logistics - 12th International Conference, ICCL 2021, Proceedings
series title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
editor
Mes, Martijn ; Lalla-Ruiz, Eduardo and Voß, Stefan
volume
13004 LNCS
pages
15 pages
publisher
Springer Science and Business Media B.V.
conference name
12th International Conference on Computational Logistics, ICCL 2021
conference location
Virtual, Online
conference dates
2021-09-27 - 2021-09-29
external identifiers
  • scopus:85116390344
ISSN
1611-3349
0302-9743
ISBN
9783030876715
DOI
10.1007/978-3-030-87672-2_8
project
RobotLab LTH
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2021, Springer Nature Switzerland AG.
id
07683631-1db5-4c82-955f-1219cdeeb53d
date added to LUP
2021-10-25 14:52:57
date last changed
2024-06-15 19:05:31
@inproceedings{07683631-1db5-4c82-955f-1219cdeeb53d,
  abstract     = {{<p>Order-batching is an important methodology in warehouse material handling. This paper addresses three identified shortcomings in the current literature on order-batching optimization. The first concerns the overly large dependence on conventional warehouse layouts. The second is a lack of proposed optimization methods capable of producing approximate solutions in minimal computational time. The third is a scarcity of benchmark datasets, which are necessary for data-driven performance evaluation. This paper introduces an optimization algorithm, SBI, capable of generating reasonably strong solutions to order-batching problems for any warehouse layout at great speed. On an existing benchmark dataset for a conventional layout, Foodmart, results show that the algorithm on average used 6.9% computational time and 105.8% travel cost relative to the state of the art. New benchmark instances and proposed solutions for various layouts and problem settings were shared on a public repository.</p>}},
  author       = {{Oxenstierna, Johan and Malec, Jacek and Krueger, Volker}},
  booktitle    = {{Computational Logistics - 12th International Conference, ICCL 2021, Proceedings}},
  editor       = {{Mes, Martijn and Lalla-Ruiz, Eduardo and Voß, Stefan}},
  isbn         = {{9783030876715}},
  issn         = {{1611-3349}},
  keywords     = {{Discrete optimization; Order picking; Order-batching problem}},
  language     = {{eng}},
  pages        = {{115--129}},
  publisher    = {{Springer Science and Business Media B.V.}},
  series       = {{Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}},
  title        = {{Layout-Agnostic Order-Batching Optimization}},
  url          = {{http://dx.doi.org/10.1007/978-3-030-87672-2_8}},
  doi          = {{10.1007/978-3-030-87672-2_8}},
  volume       = {{13004 LNCS}},
  year         = {{2021}},
}