A Developed Framework for Sequencing of Mixed-model Assembly Line with Customer’s Satisfaction and Heterogeneous Operators
Autor(a) principal: | |
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Data de Publicação: | 2020 |
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/705 |
Resumo: | Goals: We present a multi-objective mathematical model to determine the optimum production sequence of the mixed-model assembly line (MMAL). Maximizing customer satisfaction and minimizing costs are the objectives of the problem. Methodology: Customers are divided into two clusters of high priority and low priority by k-medoids method. Also, to get closer to the real world, heterogeneous workers are considered. As the actual scale of the problem cannot be solved by an exact method, two meta-heuristic algorithms, namely Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II) are proposed to solve the problem and reach approximate and efficient results in large scale. Results: It observes that this model can plan the customers' orders by considering their satisfaction. Also, comparing the results of these algorithms indicates a slight superiority of the SPEA2 method. Limitations of the investigation: This study is mainly limited by clustering criteria. In the future, more criteria can be considered for analyzing customer behavior and expanding customer clusters. Practical implications: This model can help all manufacturers who use MMAL by providing a Pareto front for deciding between costs and customers' satisfaction. Originality / Value: Applying k-medoids to cluster the customers for better orders management and proposing SPEA2 and NSGA-II for solving the problem are the main novelties of this study. |
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Brazilian Journal of Operations & Production Management (Online) |
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A Developed Framework for Sequencing of Mixed-model Assembly Line with Customer’s Satisfaction and Heterogeneous OperatorsMixed model assembly line, Sequencing, SPEA2, Customer satisfaction, k-medoidsGoals: We present a multi-objective mathematical model to determine the optimum production sequence of the mixed-model assembly line (MMAL). Maximizing customer satisfaction and minimizing costs are the objectives of the problem. Methodology: Customers are divided into two clusters of high priority and low priority by k-medoids method. Also, to get closer to the real world, heterogeneous workers are considered. As the actual scale of the problem cannot be solved by an exact method, two meta-heuristic algorithms, namely Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II) are proposed to solve the problem and reach approximate and efficient results in large scale. Results: It observes that this model can plan the customers' orders by considering their satisfaction. Also, comparing the results of these algorithms indicates a slight superiority of the SPEA2 method. Limitations of the investigation: This study is mainly limited by clustering criteria. In the future, more criteria can be considered for analyzing customer behavior and expanding customer clusters. Practical implications: This model can help all manufacturers who use MMAL by providing a Pareto front for deciding between costs and customers' satisfaction. Originality / Value: Applying k-medoids to cluster the customers for better orders management and proposing SPEA2 and NSGA-II for solving the problem are the main novelties of this study.Brazilian Association for Industrial Engineering and Operations Management (ABEPRO)2020-10-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionResearch paperapplication/pdfhttps://bjopm.org.br/bjopm/article/view/70510.14488/BJOPM.2020.027Brazilian Journal of Operations & Production Management; Vol. 17 No. 4 (2020); 1-202237-8960reponame:Brazilian Journal of Operations & Production Management (Online)instname:Associação Brasileira de Engenharia de Produção (ABEPRO)instacron:ABEPROenghttps://bjopm.org.br/bjopm/article/view/705/949Copyright (c) 2020 Masoud Rabbani, Seyedeh Zeinab Beladian Behbahan, Mrs, Hamed Farrokhi-asl, Mr.info:eu-repo/semantics/openAccessRabbani, MasoudBeladian Behbahan, Seyedeh ZeinabFarrokhi-asl, HamedEsmizadeh, Majedeh2021-02-23T23:19:07Zoai:ojs.bjopm.org.br:article/705Revistahttps://bjopm.org.br/bjopmONGhttps://bjopm.org.br/bjopm/oaibjopm.journal@gmail.com2237-89601679-8171opendoar:2023-03-13T09:45:20.530572Brazilian Journal of Operations & Production Management (Online) - Associação Brasileira de Engenharia de Produção (ABEPRO)false |
dc.title.none.fl_str_mv |
A Developed Framework for Sequencing of Mixed-model Assembly Line with Customer’s Satisfaction and Heterogeneous Operators |
title |
A Developed Framework for Sequencing of Mixed-model Assembly Line with Customer’s Satisfaction and Heterogeneous Operators |
spellingShingle |
A Developed Framework for Sequencing of Mixed-model Assembly Line with Customer’s Satisfaction and Heterogeneous Operators Rabbani, Masoud Mixed model assembly line, Sequencing, SPEA2, Customer satisfaction, k-medoids |
title_short |
A Developed Framework for Sequencing of Mixed-model Assembly Line with Customer’s Satisfaction and Heterogeneous Operators |
title_full |
A Developed Framework for Sequencing of Mixed-model Assembly Line with Customer’s Satisfaction and Heterogeneous Operators |
title_fullStr |
A Developed Framework for Sequencing of Mixed-model Assembly Line with Customer’s Satisfaction and Heterogeneous Operators |
title_full_unstemmed |
A Developed Framework for Sequencing of Mixed-model Assembly Line with Customer’s Satisfaction and Heterogeneous Operators |
title_sort |
A Developed Framework for Sequencing of Mixed-model Assembly Line with Customer’s Satisfaction and Heterogeneous Operators |
author |
Rabbani, Masoud |
author_facet |
Rabbani, Masoud Beladian Behbahan, Seyedeh Zeinab Farrokhi-asl, Hamed Esmizadeh, Majedeh |
author_role |
author |
author2 |
Beladian Behbahan, Seyedeh Zeinab Farrokhi-asl, Hamed Esmizadeh, Majedeh |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Rabbani, Masoud Beladian Behbahan, Seyedeh Zeinab Farrokhi-asl, Hamed Esmizadeh, Majedeh |
dc.subject.por.fl_str_mv |
Mixed model assembly line, Sequencing, SPEA2, Customer satisfaction, k-medoids |
topic |
Mixed model assembly line, Sequencing, SPEA2, Customer satisfaction, k-medoids |
description |
Goals: We present a multi-objective mathematical model to determine the optimum production sequence of the mixed-model assembly line (MMAL). Maximizing customer satisfaction and minimizing costs are the objectives of the problem. Methodology: Customers are divided into two clusters of high priority and low priority by k-medoids method. Also, to get closer to the real world, heterogeneous workers are considered. As the actual scale of the problem cannot be solved by an exact method, two meta-heuristic algorithms, namely Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II) are proposed to solve the problem and reach approximate and efficient results in large scale. Results: It observes that this model can plan the customers' orders by considering their satisfaction. Also, comparing the results of these algorithms indicates a slight superiority of the SPEA2 method. Limitations of the investigation: This study is mainly limited by clustering criteria. In the future, more criteria can be considered for analyzing customer behavior and expanding customer clusters. Practical implications: This model can help all manufacturers who use MMAL by providing a Pareto front for deciding between costs and customers' satisfaction. Originality / Value: Applying k-medoids to cluster the customers for better orders management and proposing SPEA2 and NSGA-II for solving the problem are the main novelties of this study. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-10-06 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Research paper |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://bjopm.org.br/bjopm/article/view/705 10.14488/BJOPM.2020.027 |
url |
https://bjopm.org.br/bjopm/article/view/705 |
identifier_str_mv |
10.14488/BJOPM.2020.027 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://bjopm.org.br/bjopm/article/view/705/949 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2020 Masoud Rabbani, Seyedeh Zeinab Beladian Behbahan, Mrs, Hamed Farrokhi-asl, Mr. info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2020 Masoud Rabbani, Seyedeh Zeinab Beladian Behbahan, Mrs, Hamed Farrokhi-asl, Mr. |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
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. 17 No. 4 (2020); 1-20 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_ |
1797051461064982528 |