Layout-Agnostic Order-Batching Optimization
(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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- author
- Oxenstierna, Johan LU ; Malec, Jacek LU and Krueger, Volker LU
- organization
- publishing date
- 2021
- 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
- 2024-09-22 04:06:05
@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}}, }