An extended Akers graphical method with a biased random-key genetic algorithm for job-shop scheduling

Detalhes bibliográficos
Autor(a) principal: José Fernando Gonçalves
Data de Publicação: 2014
Outros Autores: Resende,MGC
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/123456789/5392
http://dx.doi.org/10.1111/itor.12044
Resumo: This paper presents a local search, based on a new neighborhood for the job-shop scheduling problem, and its application within a biased random-key genetic algorithm. Schedules are constructed by decoding the chromosome supplied by the genetic algorithm with a procedure that generates active schedules. After an initial schedule is obtained, a local search heuristic, based on an extension of the 1956 graphical method of Akers, is applied to improve the solution. The new heuristic is tested on a set of 205 standard instances taken from the job-shop scheduling literature and compared with results obtained by other approaches. The new algorithm improved the best-known solution values for 57 instances.
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spelling An extended Akers graphical method with a biased random-key genetic algorithm for job-shop schedulingThis paper presents a local search, based on a new neighborhood for the job-shop scheduling problem, and its application within a biased random-key genetic algorithm. Schedules are constructed by decoding the chromosome supplied by the genetic algorithm with a procedure that generates active schedules. After an initial schedule is obtained, a local search heuristic, based on an extension of the 1956 graphical method of Akers, is applied to improve the solution. The new heuristic is tested on a set of 205 standard instances taken from the job-shop scheduling literature and compared with results obtained by other approaches. The new algorithm improved the best-known solution values for 57 instances.2018-01-03T11:39:30Z2014-01-01T00:00:00Z2014info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/5392http://dx.doi.org/10.1111/itor.12044engJosé Fernando GonçalvesResende,MGCinfo: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:RCAAP2023-05-15T10:20:26Zoai:repositorio.inesctec.pt:123456789/5392Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:53:05.995639Repositório 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 An extended Akers graphical method with a biased random-key genetic algorithm for job-shop scheduling
title An extended Akers graphical method with a biased random-key genetic algorithm for job-shop scheduling
spellingShingle An extended Akers graphical method with a biased random-key genetic algorithm for job-shop scheduling
José Fernando Gonçalves
title_short An extended Akers graphical method with a biased random-key genetic algorithm for job-shop scheduling
title_full An extended Akers graphical method with a biased random-key genetic algorithm for job-shop scheduling
title_fullStr An extended Akers graphical method with a biased random-key genetic algorithm for job-shop scheduling
title_full_unstemmed An extended Akers graphical method with a biased random-key genetic algorithm for job-shop scheduling
title_sort An extended Akers graphical method with a biased random-key genetic algorithm for job-shop scheduling
author José Fernando Gonçalves
author_facet José Fernando Gonçalves
Resende,MGC
author_role author
author2 Resende,MGC
author2_role author
dc.contributor.author.fl_str_mv José Fernando Gonçalves
Resende,MGC
description This paper presents a local search, based on a new neighborhood for the job-shop scheduling problem, and its application within a biased random-key genetic algorithm. Schedules are constructed by decoding the chromosome supplied by the genetic algorithm with a procedure that generates active schedules. After an initial schedule is obtained, a local search heuristic, based on an extension of the 1956 graphical method of Akers, is applied to improve the solution. The new heuristic is tested on a set of 205 standard instances taken from the job-shop scheduling literature and compared with results obtained by other approaches. The new algorithm improved the best-known solution values for 57 instances.
publishDate 2014
dc.date.none.fl_str_mv 2014-01-01T00:00:00Z
2014
2018-01-03T11:39:30Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://repositorio.inesctec.pt/handle/123456789/5392
http://dx.doi.org/10.1111/itor.12044
url http://repositorio.inesctec.pt/handle/123456789/5392
http://dx.doi.org/10.1111/itor.12044
dc.language.iso.fl_str_mv eng
language eng
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eu_rights_str_mv openAccess
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instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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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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