Genetic Algorithm, MIP and Improvement Heuristic Applied to the MLCLP with Backlogging

Detalhes bibliográficos
Autor(a) principal: Toledo, Claudio F. M.
Data de Publicação: 2013
Outros Autores: Hossomi, Marcelo Y. B., Arantes, Marcio da Silva, Franca, Paulo Morelato [UNESP], IEEE
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/194769
Resumo: The present paper solves the multi-level capacitated lot sizing problem with backlogging (MLCLSPB) combining a genetic algorithm with the solution of mixed-integer programming models and the improvement heuristic fix and optimize. This approach is evaluated over sets of benchmark instances and compared to methods from literature. Computational results indicate competitive results applying the proposed method when compared with other literature approaches.
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spelling Genetic Algorithm, MIP and Improvement Heuristic Applied to the MLCLP with Backlogginglot-sizingmulti-levelhybrid metaheuristicgenetic algorithmThe present paper solves the multi-level capacitated lot sizing problem with backlogging (MLCLSPB) combining a genetic algorithm with the solution of mixed-integer programming models and the improvement heuristic fix and optimize. This approach is evaluated over sets of benchmark instances and compared to methods from literature. Computational results indicate competitive results applying the proposed method when compared with other literature approaches.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos, SP, BrazilUniv Estadual Paulista, Dept Math & Comp, Sao Paulo, BrazilUniv Estadual Paulista, Dept Math & Comp, Sao Paulo, BrazilFAPESP: 2010/10133-0FAPESP: 2011/15534-5FAPESP: 2012/00997-2IeeeUniversidade de São Paulo (USP)Universidade Estadual Paulista (Unesp)Toledo, Claudio F. M.Hossomi, Marcelo Y. B.Arantes, Marcio da SilvaFranca, Paulo Morelato [UNESP]IEEE2020-12-10T16:37:02Z2020-12-10T16:37:02Z2013-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject1483-14902013 Ieee Congress On Evolutionary Computation (cec). New York: Ieee, p. 1483-1490, 2013.http://hdl.handle.net/11449/194769WOS:000326235301062Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2013 Ieee Congress On Evolutionary Computation (cec)info:eu-repo/semantics/openAccess2021-10-22T20:36:25Zoai:repositorio.unesp.br:11449/194769Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T14:55:55.008953Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Genetic Algorithm, MIP and Improvement Heuristic Applied to the MLCLP with Backlogging
title Genetic Algorithm, MIP and Improvement Heuristic Applied to the MLCLP with Backlogging
spellingShingle Genetic Algorithm, MIP and Improvement Heuristic Applied to the MLCLP with Backlogging
Toledo, Claudio F. M.
lot-sizing
multi-level
hybrid metaheuristic
genetic algorithm
title_short Genetic Algorithm, MIP and Improvement Heuristic Applied to the MLCLP with Backlogging
title_full Genetic Algorithm, MIP and Improvement Heuristic Applied to the MLCLP with Backlogging
title_fullStr Genetic Algorithm, MIP and Improvement Heuristic Applied to the MLCLP with Backlogging
title_full_unstemmed Genetic Algorithm, MIP and Improvement Heuristic Applied to the MLCLP with Backlogging
title_sort Genetic Algorithm, MIP and Improvement Heuristic Applied to the MLCLP with Backlogging
author Toledo, Claudio F. M.
author_facet Toledo, Claudio F. M.
Hossomi, Marcelo Y. B.
Arantes, Marcio da Silva
Franca, Paulo Morelato [UNESP]
IEEE
author_role author
author2 Hossomi, Marcelo Y. B.
Arantes, Marcio da Silva
Franca, Paulo Morelato [UNESP]
IEEE
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade de São Paulo (USP)
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Toledo, Claudio F. M.
Hossomi, Marcelo Y. B.
Arantes, Marcio da Silva
Franca, Paulo Morelato [UNESP]
IEEE
dc.subject.por.fl_str_mv lot-sizing
multi-level
hybrid metaheuristic
genetic algorithm
topic lot-sizing
multi-level
hybrid metaheuristic
genetic algorithm
description The present paper solves the multi-level capacitated lot sizing problem with backlogging (MLCLSPB) combining a genetic algorithm with the solution of mixed-integer programming models and the improvement heuristic fix and optimize. This approach is evaluated over sets of benchmark instances and compared to methods from literature. Computational results indicate competitive results applying the proposed method when compared with other literature approaches.
publishDate 2013
dc.date.none.fl_str_mv 2013-01-01
2020-12-10T16:37:02Z
2020-12-10T16:37:02Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv 2013 Ieee Congress On Evolutionary Computation (cec). New York: Ieee, p. 1483-1490, 2013.
http://hdl.handle.net/11449/194769
WOS:000326235301062
identifier_str_mv 2013 Ieee Congress On Evolutionary Computation (cec). New York: Ieee, p. 1483-1490, 2013.
WOS:000326235301062
url http://hdl.handle.net/11449/194769
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2013 Ieee Congress On Evolutionary Computation (cec)
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 1483-1490
dc.publisher.none.fl_str_mv Ieee
publisher.none.fl_str_mv Ieee
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
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