Using metaheuristic algorithms for solving a mixed model assembly line balancing problem considering express parallel line and learning effect
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Journal of Operations & Production Management (Online) |
Texto Completo: | https://bjopm.org.br/bjopm/article/view/414 |
Resumo: | Mixed-model assembly line attracts many manufacturing centers' attentions, since it enables them to manufacture different models of one product in the same line. The present work proposes a new mathematical model to balancing mixed-model assembly two parallel lines, in which first one is a common line and the other is an express line due to more modern technology or operators with higher skills. Therefore, the cost of equipment and skilled labor in the express line is higher, and also, the learning effect on resource dependent task times and setup times is considered in the assemble-to-order environment. The aim of this study is to minimize the cycle time and the total operating cost and smoothness index by configuration of tasks in stations, according to their precedence diagrams. Also, assigning the assistants to some tasks in some stations and for some models is allowed. This problem is categorized as an NP-hard problem and for solving this multi-objective problem, non-dominated sorting genetic algorithm ІІ (NSGA-II) and multi-objective particle swarm optimization (MOPSO) are applied. Finally, for comparing the proposed methods some numerical examples are implemented and the result show that MOPSO outperforms NSGAII. |
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oai:ojs.bjopm.org.br:article/414 |
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Brazilian Journal of Operations & Production Management (Online) |
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Using metaheuristic algorithms for solving a mixed model assembly line balancing problem considering express parallel line and learning effectMixed-model assembly linebalancinglearning effectparallel lineMixed-model assembly line attracts many manufacturing centers' attentions, since it enables them to manufacture different models of one product in the same line. The present work proposes a new mathematical model to balancing mixed-model assembly two parallel lines, in which first one is a common line and the other is an express line due to more modern technology or operators with higher skills. Therefore, the cost of equipment and skilled labor in the express line is higher, and also, the learning effect on resource dependent task times and setup times is considered in the assemble-to-order environment. The aim of this study is to minimize the cycle time and the total operating cost and smoothness index by configuration of tasks in stations, according to their precedence diagrams. Also, assigning the assistants to some tasks in some stations and for some models is allowed. This problem is categorized as an NP-hard problem and for solving this multi-objective problem, non-dominated sorting genetic algorithm ІІ (NSGA-II) and multi-objective particle swarm optimization (MOPSO) are applied. Finally, for comparing the proposed methods some numerical examples are implemented and the result show that MOPSO outperforms NSGAII.Brazilian Association for Industrial Engineering and Operations Management (ABEPRO)2018-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPeer-reviewed Articletext/htmlapplication/pdfhttps://bjopm.org.br/bjopm/article/view/41410.14488/BJOPM.2018.v15.n2.a8Brazilian Journal of Operations & Production Management; Vol. 15 No. 2 (2018): June, 2018; 254-2692237-8960reponame:Brazilian Journal of Operations & Production Management (Online)instname:Associação Brasileira de Engenharia de Produção (ABEPRO)instacron:ABEPROenghttps://bjopm.org.br/bjopm/article/view/414/601https://bjopm.org.br/bjopm/article/view/414/629Copyright (c) 2018 Brazilian Journal of Operations & Production Managementinfo:eu-repo/semantics/openAccessRabbani, MasoudAlipour, FarahnazFarrokhi-Asl, HamedManavizadeh, Neda2021-07-13T14:14:35Zoai:ojs.bjopm.org.br:article/414Revistahttps://bjopm.org.br/bjopmONGhttps://bjopm.org.br/bjopm/oaibjopm.journal@gmail.com2237-89601679-8171opendoar:2023-03-13T09:45:16.468478Brazilian Journal of Operations & Production Management (Online) - Associação Brasileira de Engenharia de Produção (ABEPRO)false |
dc.title.none.fl_str_mv |
Using metaheuristic algorithms for solving a mixed model assembly line balancing problem considering express parallel line and learning effect |
title |
Using metaheuristic algorithms for solving a mixed model assembly line balancing problem considering express parallel line and learning effect |
spellingShingle |
Using metaheuristic algorithms for solving a mixed model assembly line balancing problem considering express parallel line and learning effect Rabbani, Masoud Mixed-model assembly line balancing learning effect parallel line |
title_short |
Using metaheuristic algorithms for solving a mixed model assembly line balancing problem considering express parallel line and learning effect |
title_full |
Using metaheuristic algorithms for solving a mixed model assembly line balancing problem considering express parallel line and learning effect |
title_fullStr |
Using metaheuristic algorithms for solving a mixed model assembly line balancing problem considering express parallel line and learning effect |
title_full_unstemmed |
Using metaheuristic algorithms for solving a mixed model assembly line balancing problem considering express parallel line and learning effect |
title_sort |
Using metaheuristic algorithms for solving a mixed model assembly line balancing problem considering express parallel line and learning effect |
author |
Rabbani, Masoud |
author_facet |
Rabbani, Masoud Alipour, Farahnaz Farrokhi-Asl, Hamed Manavizadeh, Neda |
author_role |
author |
author2 |
Alipour, Farahnaz Farrokhi-Asl, Hamed Manavizadeh, Neda |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Rabbani, Masoud Alipour, Farahnaz Farrokhi-Asl, Hamed Manavizadeh, Neda |
dc.subject.por.fl_str_mv |
Mixed-model assembly line balancing learning effect parallel line |
topic |
Mixed-model assembly line balancing learning effect parallel line |
description |
Mixed-model assembly line attracts many manufacturing centers' attentions, since it enables them to manufacture different models of one product in the same line. The present work proposes a new mathematical model to balancing mixed-model assembly two parallel lines, in which first one is a common line and the other is an express line due to more modern technology or operators with higher skills. Therefore, the cost of equipment and skilled labor in the express line is higher, and also, the learning effect on resource dependent task times and setup times is considered in the assemble-to-order environment. The aim of this study is to minimize the cycle time and the total operating cost and smoothness index by configuration of tasks in stations, according to their precedence diagrams. Also, assigning the assistants to some tasks in some stations and for some models is allowed. This problem is categorized as an NP-hard problem and for solving this multi-objective problem, non-dominated sorting genetic algorithm ІІ (NSGA-II) and multi-objective particle swarm optimization (MOPSO) are applied. Finally, for comparing the proposed methods some numerical examples are implemented and the result show that MOPSO outperforms NSGAII. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-06-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://bjopm.org.br/bjopm/article/view/414 10.14488/BJOPM.2018.v15.n2.a8 |
url |
https://bjopm.org.br/bjopm/article/view/414 |
identifier_str_mv |
10.14488/BJOPM.2018.v15.n2.a8 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://bjopm.org.br/bjopm/article/view/414/601 https://bjopm.org.br/bjopm/article/view/414/629 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2018 Brazilian Journal of Operations & Production Management info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2018 Brazilian Journal of Operations & Production Management |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html application/pdf |
dc.publisher.none.fl_str_mv |
Brazilian Association for Industrial Engineering and Operations Management (ABEPRO) |
publisher.none.fl_str_mv |
Brazilian Association for Industrial Engineering and Operations Management (ABEPRO) |
dc.source.none.fl_str_mv |
Brazilian Journal of Operations & Production Management; Vol. 15 No. 2 (2018): June, 2018; 254-269 2237-8960 reponame:Brazilian Journal of Operations & Production Management (Online) instname:Associação Brasileira de Engenharia de Produção (ABEPRO) instacron:ABEPRO |
instname_str |
Associação Brasileira de Engenharia de Produção (ABEPRO) |
instacron_str |
ABEPRO |
institution |
ABEPRO |
reponame_str |
Brazilian Journal of Operations & Production Management (Online) |
collection |
Brazilian Journal of Operations & Production Management (Online) |
repository.name.fl_str_mv |
Brazilian Journal of Operations & Production Management (Online) - Associação Brasileira de Engenharia de Produção (ABEPRO) |
repository.mail.fl_str_mv |
bjopm.journal@gmail.com |
_version_ |
1797051460887773184 |