Multi-objective variable neighborhood search algorithms for a single machine scheduling problem with distinct due windows

Detalhes bibliográficos
Autor(a) principal: Arroyo, José Elias Claudio
Data de Publicação: 2011
Outros Autores: Ottoni, Rafael dos Santos, Oliveira, Alcione de Paiva
Tipo de documento: Artigo
Idioma: eng
Título da fonte: LOCUS Repositório Institucional da UFV
Texto Completo: https://doi.org/10.1016/j.entcs.2011.11.022
http://www.locus.ufv.br/handle/123456789/21631
Resumo: In this paper, we compare three multi-objective algorithms based on Variable Neighborhood Search (VNS) heuristic. The algorithms are applied to solve the single machine scheduling problem with sequence dependent setup times and distinct due windows. In this problem, we consider minimizing the total weighted earliness/tardiness and the total flowtime criteria. We introduce two intensification procedures to improve a multi-objective VNS (MOVNS) algorithm proposed in the literature. The performance of the algorithms is tested on a set of medium and larger instances of the problem. The computational results show that the proposed algorithms outperform the original MOVNS algorithm in terms of solution quality. A statistical analysis is conducted in order to analyze the performance of the proposed methods.
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spelling Arroyo, José Elias ClaudioOttoni, Rafael dos SantosOliveira, Alcione de Paiva2018-09-04T17:06:46Z2018-09-04T17:06:46Z2011-12-2915710661https://doi.org/10.1016/j.entcs.2011.11.022http://www.locus.ufv.br/handle/123456789/21631In this paper, we compare three multi-objective algorithms based on Variable Neighborhood Search (VNS) heuristic. The algorithms are applied to solve the single machine scheduling problem with sequence dependent setup times and distinct due windows. In this problem, we consider minimizing the total weighted earliness/tardiness and the total flowtime criteria. We introduce two intensification procedures to improve a multi-objective VNS (MOVNS) algorithm proposed in the literature. The performance of the algorithms is tested on a set of medium and larger instances of the problem. The computational results show that the proposed algorithms outperform the original MOVNS algorithm in terms of solution quality. A statistical analysis is conducted in order to analyze the performance of the proposed methods.engElectronic Notes in Theoretical Computer Sciencev. 281, p. 5- 19, december 2011Multi-objective optimizationLocal search heuristicsJob schedulingMulti-objective variable neighborhood search algorithms for a single machine scheduling problem with distinct due windowsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfinfo:eu-repo/semantics/openAccessreponame:LOCUS Repositório Institucional da UFVinstname:Universidade Federal de Viçosa (UFV)instacron:UFVORIGINALartigo.pdfartigo.pdfTexto completoapplication/pdf567786https://locus.ufv.br//bitstream/123456789/21631/1/artigo.pdfb6254c90db91524b705eccf26538c949MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://locus.ufv.br//bitstream/123456789/21631/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52THUMBNAILartigo.pdf.jpgartigo.pdf.jpgIM Thumbnailimage/jpeg5171https://locus.ufv.br//bitstream/123456789/21631/3/artigo.pdf.jpg4653c9d5f8642e42d6bf209f1883a085MD53123456789/216312018-09-04 23:00:33.586oai:locus.ufv.br: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Repositório InstitucionalPUBhttps://www.locus.ufv.br/oai/requestfabiojreis@ufv.bropendoar:21452018-09-05T02:00:33LOCUS Repositório Institucional da UFV - Universidade Federal de Viçosa (UFV)false
dc.title.en.fl_str_mv Multi-objective variable neighborhood search algorithms for a single machine scheduling problem with distinct due windows
title Multi-objective variable neighborhood search algorithms for a single machine scheduling problem with distinct due windows
spellingShingle Multi-objective variable neighborhood search algorithms for a single machine scheduling problem with distinct due windows
Arroyo, José Elias Claudio
Multi-objective optimization
Local search heuristics
Job scheduling
title_short Multi-objective variable neighborhood search algorithms for a single machine scheduling problem with distinct due windows
title_full Multi-objective variable neighborhood search algorithms for a single machine scheduling problem with distinct due windows
title_fullStr Multi-objective variable neighborhood search algorithms for a single machine scheduling problem with distinct due windows
title_full_unstemmed Multi-objective variable neighborhood search algorithms for a single machine scheduling problem with distinct due windows
title_sort Multi-objective variable neighborhood search algorithms for a single machine scheduling problem with distinct due windows
author Arroyo, José Elias Claudio
author_facet Arroyo, José Elias Claudio
Ottoni, Rafael dos Santos
Oliveira, Alcione de Paiva
author_role author
author2 Ottoni, Rafael dos Santos
Oliveira, Alcione de Paiva
author2_role author
author
dc.contributor.author.fl_str_mv Arroyo, José Elias Claudio
Ottoni, Rafael dos Santos
Oliveira, Alcione de Paiva
dc.subject.pt-BR.fl_str_mv Multi-objective optimization
Local search heuristics
Job scheduling
topic Multi-objective optimization
Local search heuristics
Job scheduling
description In this paper, we compare three multi-objective algorithms based on Variable Neighborhood Search (VNS) heuristic. The algorithms are applied to solve the single machine scheduling problem with sequence dependent setup times and distinct due windows. In this problem, we consider minimizing the total weighted earliness/tardiness and the total flowtime criteria. We introduce two intensification procedures to improve a multi-objective VNS (MOVNS) algorithm proposed in the literature. The performance of the algorithms is tested on a set of medium and larger instances of the problem. The computational results show that the proposed algorithms outperform the original MOVNS algorithm in terms of solution quality. A statistical analysis is conducted in order to analyze the performance of the proposed methods.
publishDate 2011
dc.date.issued.fl_str_mv 2011-12-29
dc.date.accessioned.fl_str_mv 2018-09-04T17:06:46Z
dc.date.available.fl_str_mv 2018-09-04T17:06:46Z
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 https://doi.org/10.1016/j.entcs.2011.11.022
http://www.locus.ufv.br/handle/123456789/21631
dc.identifier.issn.none.fl_str_mv 15710661
identifier_str_mv 15710661
url https://doi.org/10.1016/j.entcs.2011.11.022
http://www.locus.ufv.br/handle/123456789/21631
dc.language.iso.fl_str_mv eng
language eng
dc.relation.ispartofseries.pt-BR.fl_str_mv v. 281, p. 5- 19, december 2011
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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dc.publisher.none.fl_str_mv Electronic Notes in Theoretical Computer Science
publisher.none.fl_str_mv Electronic Notes in Theoretical Computer Science
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reponame_str LOCUS Repositório Institucional da UFV
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