Variable-Relationship Guided LNS for the Car Sequencing Problem

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dc.contributor.authorSouza, Filipe
dc.contributor.authorGrimes, Diarmuid
dc.contributor.authorO'Sullivan, Barry
dc.contributor.editorLongo, L.; O'Reilly, R.
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2023-01-11T12:08:31Z
dc.date.available2023-01-11T12:08:31Z
dc.date.issued2022-02-23
dc.date.updated2023-01-11T12:01:25Z
dc.description.abstractLarge Neighbourhood Search (LNS) is a powerful technique that applies the "divide and conquer" principle to boost the performance of solvers on large scale Combinatorial Optimization Problems. In this paper we consider one of the main hindrances to the LNS popularity, namely the requirement of an expert to define a problem specific neighborhood. We present an approach that learns from problem structure and search performance in order to generate neighbourhoods that can match the performance of domain specific heuristics developed by an expert. Furthermore, we present a new objective function for the optimzation version of the Car Sequencing Problem, that better distinguishes solution quality. Empirical results on public instances demonstrate the effectiveness of our approach against both a domain specific heuristic and state-of-the art generic approaches.en
dc.description.sponsorshipScience Foundation ireland (SFI Centre for Research Training in Artificial Intelligence under Grant No. 18/CRT/6223 and SFI under Grant No. 12/RC/2289-P2, co-funded under the European Regional Development Fund)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationSouza, F., Grimes, D. and O’Sullivan, B. (2023) ‘Variable-relationship guided lns for the car sequencing problem’, AICS2022, in L. Longo and R. O’Reilly (eds) Artificial Intelligence and Cognitive Science. Cham: Springer Nature Switzerland, pp. 437–449. https://doi.org/10.1007/978-3-031-26438-2_34en
dc.identifier.doi10.1007/978-3-031-26438-2_34en
dc.identifier.endpage449en
dc.identifier.isbn978-3-031-26438-2
dc.identifier.isbn978-3-031-26437-5
dc.identifier.startpage437en
dc.identifier.urihttps://hdl.handle.net/10468/14040
dc.identifier.volume1662en
dc.language.isoenen
dc.publisherSpringer Chamen
dc.relation.ispartof30th Irish Conference on Artificial Intelligence and Cognitive Science (AICS2022), Munster Technological University, Cork, 8-9 Decemberen
dc.relation.project12/RC/2289
dc.relation.project18/CRT/6223
dc.rights© 2023 The Author(s). Open Access. This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were madeen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectLNSen
dc.subjectNeighbourhood Selectionen
dc.subjectCar Sequencing Problemen
dc.subjectLarge Neighbourhood Search (LNS)en
dc.subjectArtificial intelligenceen
dc.titleVariable-Relationship Guided LNS for the Car Sequencing Problemen
dc.typeConference itemen
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