Improving the shift-scheduling problem using non-stationary queueing models with local heuristic and genetic algorithm
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1590/0101-7438.2020.040.00220764 http://hdl.handle.net/11449/198954 |
Resumo: | We improve the shift-scheduling process by using nonstationary queueing models to evaluate schedules and two heuristics to generate schedules. Firstly, we improved the fitness function and the initial population generation method for a benchmark genetic algorithm in the literature. We also proposed a simple local search heuristic. The improved genetic algorithm found solutions that obey the delay probability constraint more often. The proposed local search heuristic also finds feasible solutions with a much lower computational expense, especially under low arrival rates. Differently from a genetic algorithm, the local search heuristic does not rely on random choices. Furthermore, it finds one final solution from one initial solution, rather than from a population of solutions. The developed local search heuristic works with only one well-defined goal, making it simple and straightforward to implement. Nevertheless, the code for the heuristic is simple enough to accept changes and cope with multiple objectives. |
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oai:repositorio.unesp.br:11449/198954 |
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Repositório Institucional da UNESP |
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2946 |
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Improving the shift-scheduling problem using non-stationary queueing models with local heuristic and genetic algorithmGenetic algorithmLocal search heuristicNonstationary queuesWe improve the shift-scheduling process by using nonstationary queueing models to evaluate schedules and two heuristics to generate schedules. Firstly, we improved the fitness function and the initial population generation method for a benchmark genetic algorithm in the literature. We also proposed a simple local search heuristic. The improved genetic algorithm found solutions that obey the delay probability constraint more often. The proposed local search heuristic also finds feasible solutions with a much lower computational expense, especially under low arrival rates. Differently from a genetic algorithm, the local search heuristic does not rely on random choices. Furthermore, it finds one final solution from one initial solution, rather than from a population of solutions. The developed local search heuristic works with only one well-defined goal, making it simple and straightforward to implement. Nevertheless, the code for the heuristic is simple enough to accept changes and cope with multiple objectives.Department of Production Engineering São Paulo State University – UNESPDepartment of Production Engineering São Paulo State University – UNESPUniversidade Estadual Paulista (Unesp)Beojone, Caio Vitor [UNESP]Máximo De Souza, Regiane [UNESP]2020-12-12T01:26:37Z2020-12-12T01:26:37Z2020-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://dx.doi.org/10.1590/0101-7438.2020.040.00220764Pesquisa Operacional, v. 40.1678-51420101-7438http://hdl.handle.net/11449/19895410.1590/0101-7438.2020.040.00220764S0101-743820200001002012-s2.0-85086048999S0101-74382020000100201.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPesquisa Operacionalinfo:eu-repo/semantics/openAccess2023-10-24T06:12:49Zoai:repositorio.unesp.br:11449/198954Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:51:19.036720Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Improving the shift-scheduling problem using non-stationary queueing models with local heuristic and genetic algorithm |
title |
Improving the shift-scheduling problem using non-stationary queueing models with local heuristic and genetic algorithm |
spellingShingle |
Improving the shift-scheduling problem using non-stationary queueing models with local heuristic and genetic algorithm Beojone, Caio Vitor [UNESP] Genetic algorithm Local search heuristic Nonstationary queues |
title_short |
Improving the shift-scheduling problem using non-stationary queueing models with local heuristic and genetic algorithm |
title_full |
Improving the shift-scheduling problem using non-stationary queueing models with local heuristic and genetic algorithm |
title_fullStr |
Improving the shift-scheduling problem using non-stationary queueing models with local heuristic and genetic algorithm |
title_full_unstemmed |
Improving the shift-scheduling problem using non-stationary queueing models with local heuristic and genetic algorithm |
title_sort |
Improving the shift-scheduling problem using non-stationary queueing models with local heuristic and genetic algorithm |
author |
Beojone, Caio Vitor [UNESP] |
author_facet |
Beojone, Caio Vitor [UNESP] Máximo De Souza, Regiane [UNESP] |
author_role |
author |
author2 |
Máximo De Souza, Regiane [UNESP] |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Beojone, Caio Vitor [UNESP] Máximo De Souza, Regiane [UNESP] |
dc.subject.por.fl_str_mv |
Genetic algorithm Local search heuristic Nonstationary queues |
topic |
Genetic algorithm Local search heuristic Nonstationary queues |
description |
We improve the shift-scheduling process by using nonstationary queueing models to evaluate schedules and two heuristics to generate schedules. Firstly, we improved the fitness function and the initial population generation method for a benchmark genetic algorithm in the literature. We also proposed a simple local search heuristic. The improved genetic algorithm found solutions that obey the delay probability constraint more often. The proposed local search heuristic also finds feasible solutions with a much lower computational expense, especially under low arrival rates. Differently from a genetic algorithm, the local search heuristic does not rely on random choices. Furthermore, it finds one final solution from one initial solution, rather than from a population of solutions. The developed local search heuristic works with only one well-defined goal, making it simple and straightforward to implement. Nevertheless, the code for the heuristic is simple enough to accept changes and cope with multiple objectives. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-12T01:26:37Z 2020-12-12T01:26:37Z 2020-01-01 |
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/10.1590/0101-7438.2020.040.00220764 Pesquisa Operacional, v. 40. 1678-5142 0101-7438 http://hdl.handle.net/11449/198954 10.1590/0101-7438.2020.040.00220764 S0101-74382020000100201 2-s2.0-85086048999 S0101-74382020000100201.pdf |
url |
http://dx.doi.org/10.1590/0101-7438.2020.040.00220764 http://hdl.handle.net/11449/198954 |
identifier_str_mv |
Pesquisa Operacional, v. 40. 1678-5142 0101-7438 10.1590/0101-7438.2020.040.00220764 S0101-74382020000100201 2-s2.0-85086048999 S0101-74382020000100201.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Pesquisa Operacional |
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 |
Scopus 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_ |
1808128573882499072 |