Iterated Local Search with Neighbourhood Reduction for the Pickups and Deliveries Problem Arising in Retail Industry

Gu, H; MacMillan, L; Zhang, Y; Zinder, Y

Iterated Local Search with Neighbourhood Reduction for the Pickups and Deliveries Problem Arising in Retail Industry

Gu, H

MacMillan, L Zhang, Y Zinder, Y

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Publisher:: Springer International Publishing
Publication Type:: Chapter
Citation:: Optimization and Learning, 2021, 1443, pp. 190-202
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Gu, H https://orcid.org/0000-0003-2035-2583
dc.contributor.author	MacMillan, L
dc.contributor.author	Zhang, Y
dc.contributor.author	Zinder, Y https://orcid.org/0000-0003-2024-8129
dc.date.accessioned	2022-02-20T12:18:31Z
dc.date.available	2022-02-20T12:18:31Z
dc.date.issued	2021-01-01
dc.identifier.citation	Optimization and Learning, 2021, 1443, pp. 190-202
dc.identifier.isbn	9783030856717
dc.identifier.uri	http://hdl.handle.net/10453/154703
dc.description.abstract	The paper studies a vehicle routing problem with simultaneous pickups and deliveries that arises in the retail sector, which considers a heterogeneous fleet of vehicles, time windows of the demands, practical restrictions on the drivers and a roster specifying the order of vehicle loading at the depot. The high competition in this industry requires that a viable optimisation approach must achieve a good balance of solution time, quality and robustness. In this paper, a novel iterated local search algorithm is proposed which dynamically reduces the neighbourhood so that only the most promising moves are considered. The results of computational experiments on real-world data demonstrate the high efficiency of the presented optimisation procedure in terms of computation time, stability of the optimisation procedure and solution quality.
dc.language	en
dc.publisher	Springer International Publishing
dc.relation.ispartof	Optimization and Learning
dc.relation.isbasedon	10.1007/978-3-030-85672-4_14
dc.rights	This is a post-peer-review, pre-copyedit version of a Book Chapter published in the book. Iterated Local Search with Neighbourhood Reduction for the Pickups and Deliveries Problem Arising in Retail Industry The final authenticated version is available online at: https://link.springer.com/chapter/10.1007/978-3-030-85672-4_14
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Iterated Local Search with Neighbourhood Reduction for the Pickups and Deliveries Problem Arising in Retail Industry
dc.type	Chapter
utslib.citation.volume	1443
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2023-01-01T00:00:00+1000Z
dc.date.updated	2022-02-20T12:18:30Z
pubs.publication-status	Published
pubs.volume	1443

Abstract:

The paper studies a vehicle routing problem with simultaneous pickups and deliveries that arises in the retail sector, which considers a heterogeneous fleet of vehicles, time windows of the demands, practical restrictions on the drivers and a roster specifying the order of vehicle loading at the depot. The high competition in this industry requires that a viable optimisation approach must achieve a good balance of solution time, quality and robustness. In this paper, a novel iterated local search algorithm is proposed which dynamically reduces the neighbourhood so that only the most promising moves are considered. The results of computational experiments on real-world data demonstrate the high efficiency of the presented optimisation procedure in terms of computation time, stability of the optimisation procedure and solution quality.

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http://hdl.handle.net/10453/154703