A biased random-key genetic algorithm for the minimization of open stacks problem

Detalhes bibliográficos
Autor(a) principal: José Fernando Gonçalves
Data de Publicação: 2016
Outros Autores: Resende,MGC, Costa,MD
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://repositorio.inesctec.pt/handle/123456789/5384
http://dx.doi.org/10.1111/itor.12109
Resumo: This paper describes a biased random-key genetic algorithm (BRKGA) for the minimization of the open stacks problem (MOSP). The MOSP arises in a production system scenario, and consists of determining a sequence of cutting patterns that minimize the maximum number of open stacks during the cutting process. The proposed approach combines a BRKGA and a local search procedure for generating the sequence of cutting patterns. A novel fitness function for evaluating the quality of the solutions is also developed. Computational tests are presented using available instances taken from the literature. The high quality of the solutions obtained validate the proposed approach.
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spelling A biased random-key genetic algorithm for the minimization of open stacks problemThis paper describes a biased random-key genetic algorithm (BRKGA) for the minimization of the open stacks problem (MOSP). The MOSP arises in a production system scenario, and consists of determining a sequence of cutting patterns that minimize the maximum number of open stacks during the cutting process. The proposed approach combines a BRKGA and a local search procedure for generating the sequence of cutting patterns. A novel fitness function for evaluating the quality of the solutions is also developed. Computational tests are presented using available instances taken from the literature. The high quality of the solutions obtained validate the proposed approach.2018-01-03T11:39:14Z2016-01-01T00:00:00Z2016info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/5384http://dx.doi.org/10.1111/itor.12109engJosé Fernando GonçalvesResende,MGCCosta,MDinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:20:00Zoai:repositorio.inesctec.pt:123456789/5384Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:52:32.612440Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A biased random-key genetic algorithm for the minimization of open stacks problem
title A biased random-key genetic algorithm for the minimization of open stacks problem
spellingShingle A biased random-key genetic algorithm for the minimization of open stacks problem
José Fernando Gonçalves
title_short A biased random-key genetic algorithm for the minimization of open stacks problem
title_full A biased random-key genetic algorithm for the minimization of open stacks problem
title_fullStr A biased random-key genetic algorithm for the minimization of open stacks problem
title_full_unstemmed A biased random-key genetic algorithm for the minimization of open stacks problem
title_sort A biased random-key genetic algorithm for the minimization of open stacks problem
author José Fernando Gonçalves
author_facet José Fernando Gonçalves
Resende,MGC
Costa,MD
author_role author
author2 Resende,MGC
Costa,MD
author2_role author
author
dc.contributor.author.fl_str_mv José Fernando Gonçalves
Resende,MGC
Costa,MD
description This paper describes a biased random-key genetic algorithm (BRKGA) for the minimization of the open stacks problem (MOSP). The MOSP arises in a production system scenario, and consists of determining a sequence of cutting patterns that minimize the maximum number of open stacks during the cutting process. The proposed approach combines a BRKGA and a local search procedure for generating the sequence of cutting patterns. A novel fitness function for evaluating the quality of the solutions is also developed. Computational tests are presented using available instances taken from the literature. The high quality of the solutions obtained validate the proposed approach.
publishDate 2016
dc.date.none.fl_str_mv 2016-01-01T00:00:00Z
2016
2018-01-03T11:39:14Z
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://repositorio.inesctec.pt/handle/123456789/5384
http://dx.doi.org/10.1111/itor.12109
url http://repositorio.inesctec.pt/handle/123456789/5384
http://dx.doi.org/10.1111/itor.12109
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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