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

Detalhes bibliográficos
Autor(a) principal: Chaves, A. A.
Data de Publicação: 2016
Outros Autores: Lorena, L. A. N., Senne, E. L. F. [UNESP], Resende, M. G. C.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.cor.2015.10.009
http://hdl.handle.net/11449/158648
Resumo: The 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.
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spelling Hybrid method with CS and BRKGA applied to the minimization of tool switches problemHybrid heuristicsClustering searchGenetic algorithmSchedulingTool switchesThe 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.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Univ Fed Sao Paulo, BR-12231280 Sao Jose Dos Campos, BrazilNatl Inst Space Res, BR-12201970 Sao Jose Dos Campos, BrazilSao Paulo State Univ, BR-12516410 Guaratingueta, BrazilAmazon Com, MOP, Seattle, WA 98109 USASao Paulo State Univ, BR-12516410 Guaratingueta, BrazilFAPESP: 2012/17523-3CNPq: 482170/2013-1CNPq: 304979/2012-0CNPq: 476862/2012-4CNPq: 300692-2009-9CNPq: 300692/2009-9Elsevier B.V.Universidade Federal de São Paulo (UNIFESP)Natl Inst Space ResUniversidade Estadual Paulista (Unesp)Amazon ComChaves, A. A.Lorena, L. A. N.Senne, E. L. F. [UNESP]Resende, M. G. C.2018-11-26T15:28:28Z2018-11-26T15:28:28Z2016-03-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article174-183application/pdfhttp://dx.doi.org/10.1016/j.cor.2015.10.009Computers & Operations Research. Oxford: Pergamon-elsevier Science Ltd, v. 67, p. 174-183, 2016.0305-0548http://hdl.handle.net/11449/15864810.1016/j.cor.2015.10.009WOS:000367483900015WOS000367483900015.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengComputers & Operations Research1,916info:eu-repo/semantics/openAccess2024-01-13T06:34:11Zoai:repositorio.unesp.br:11449/158648Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-01-13T06:34:11Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Hybrid method with CS and BRKGA applied to the minimization of tool switches problem
title Hybrid method with CS and BRKGA applied to the minimization of tool switches problem
spellingShingle Hybrid method with CS and BRKGA applied to the minimization of tool switches problem
Chaves, A. A.
Hybrid heuristics
Clustering search
Genetic algorithm
Scheduling
Tool switches
title_short Hybrid method with CS and BRKGA applied to the minimization of tool switches problem
title_full Hybrid method with CS and BRKGA applied to the minimization of tool switches problem
title_fullStr Hybrid method with CS and BRKGA applied to the minimization of tool switches problem
title_full_unstemmed Hybrid method with CS and BRKGA applied to the minimization of tool switches problem
title_sort Hybrid method with CS and BRKGA applied to the minimization of tool switches problem
author Chaves, A. A.
author_facet Chaves, A. A.
Lorena, L. A. N.
Senne, E. L. F. [UNESP]
Resende, M. G. C.
author_role author
author2 Lorena, L. A. N.
Senne, E. L. F. [UNESP]
Resende, M. G. C.
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade Federal de São Paulo (UNIFESP)
Natl Inst Space Res
Universidade Estadual Paulista (Unesp)
Amazon Com
dc.contributor.author.fl_str_mv Chaves, A. A.
Lorena, L. A. N.
Senne, E. L. F. [UNESP]
Resende, M. G. C.
dc.subject.por.fl_str_mv Hybrid heuristics
Clustering search
Genetic algorithm
Scheduling
Tool switches
topic Hybrid heuristics
Clustering search
Genetic algorithm
Scheduling
Tool switches
description The 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.
publishDate 2016
dc.date.none.fl_str_mv 2016-03-01
2018-11-26T15:28:28Z
2018-11-26T15:28:28Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1016/j.cor.2015.10.009
Computers & Operations Research. Oxford: Pergamon-elsevier Science Ltd, v. 67, p. 174-183, 2016.
0305-0548
http://hdl.handle.net/11449/158648
10.1016/j.cor.2015.10.009
WOS:000367483900015
WOS000367483900015.pdf
url http://dx.doi.org/10.1016/j.cor.2015.10.009
http://hdl.handle.net/11449/158648
identifier_str_mv Computers & Operations Research. Oxford: Pergamon-elsevier Science Ltd, v. 67, p. 174-183, 2016.
0305-0548
10.1016/j.cor.2015.10.009
WOS:000367483900015
WOS000367483900015.pdf
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Computers & Operations Research
1,916
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 174-183
application/pdf
dc.publisher.none.fl_str_mv Elsevier B.V.
publisher.none.fl_str_mv Elsevier B.V.
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1803650246874497024

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