Hybrid method with CS and BRKGA applied to the minimization of tool switches problem
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , |
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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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:29462024-08-05T22:52:26.862508Repositório 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_ |
1808129468819046400 |