Hybrid method with CS and BRKGA applied to the minimization of tool switches problem

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dc.contributor.authorChaves, A. A.
dc.contributor.authorLorena, L. A. N.
dc.contributor.authorSenne, E. L. F. [UNESP]
dc.contributor.authorResende, M. G. C.
dc.contributor.institutionUniversidade Federal de São Paulo (UNIFESP)
dc.contributor.institutionNatl Inst Space Res
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionAmazon Com
dc.date.accessioned2018-11-26T15:28:28Z
dc.date.available2018-11-26T15:28:28Z
dc.date.issued2016-03-01
dc.description.abstractThe minimization of tool switches problem (MTSP) seeks a sequence to process a set of jobs so that the number of tool switches required is minimized. The MTSP is well known to be NP-hard. This paper presents a new hybrid heuristic based on the Biased Random Key Genetic Algorithm (BRKGA) and the Clustering Search (CS). The main idea of CS is to identify promising regions of the search space by generating solutions with a metaheuristic, such as BRKGA, and clustering them to be further explored with local search heuristics. The distinctive feature of the proposed method is to simplify this clustering process. Computational results for the MTSP considering instances available in the literature are presented to demonstrate the efficacy of the CS with BRKGA. (C) 2015 Elsevier Ltd. All rights reserved.en
dc.description.affiliationUniv Fed Sao Paulo, BR-12231280 Sao Jose Dos Campos, Brazil
dc.description.affiliationNatl Inst Space Res, BR-12201970 Sao Jose Dos Campos, Brazil
dc.description.affiliationSao Paulo State Univ, BR-12516410 Guaratingueta, Brazil
dc.description.affiliationAmazon Com, MOP, Seattle, WA 98109 USA
dc.description.affiliationUnespSao Paulo State Univ, BR-12516410 Guaratingueta, Brazil
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdFAPESP: 2012/17523-3
dc.description.sponsorshipIdCNPq: 482170/2013-1
dc.description.sponsorshipIdCNPq: 304979/2012-0
dc.description.sponsorshipIdCNPq: 476862/2012-4
dc.description.sponsorshipIdCNPq: 300692-2009-9
dc.description.sponsorshipIdCNPq: 300692/2009-9
dc.format.extent174-183
dc.identifierhttp://dx.doi.org/10.1016/j.cor.2015.10.009
dc.identifier.citationComputers & Operations Research. Oxford: Pergamon-elsevier Science Ltd, v. 67, p. 174-183, 2016.
dc.identifier.doi10.1016/j.cor.2015.10.009
dc.identifier.fileWOS000367483900015.pdf
dc.identifier.issn0305-0548
dc.identifier.urihttp://hdl.handle.net/11449/158648
dc.identifier.wosWOS:000367483900015
dc.language.isoeng
dc.publisherElsevier B.V.
dc.relation.ispartofComputers & Operations Research
dc.relation.ispartofsjr1,916
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.subjectHybrid heuristics
dc.subjectClustering search
dc.subjectGenetic algorithm
dc.subjectScheduling
dc.subjectTool switches
dc.titleHybrid method with CS and BRKGA applied to the minimization of tool switches problemen
dc.typeArtigo
dcterms.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dcterms.rightsHolderElsevier B.V.

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