Modeling and optimization of multi-layer aggregate production planning
Autor(a) principal: | |
---|---|
Data de Publicação: | 2018 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | JOSCM. Journal of Operations and Supply Chain Management |
Texto Completo: | https://periodicos.fgv.br/joscm/article/view/67615 |
Resumo: | Aggregate production planning has attracted the attention of researchers for quite a long time now; and the continued researches depict the significance and scope for improvement in this arena. Here, a multi-product, multi-level and multi-period model has been formulated to identify the required aggregate plan for meeting the forecast demand, by regulating production rates, inventory, workforce, various production costs, and other controllable variables. Several new contributing factors, such as costs related to material handling, raw material inventory and worker training have been included in the objective function and constraint equations to make the model more realistic. A case study has been presented for a cosmetics and toiletries manufacturer in Bangladesh. Eventually, the problem has been solved using Genetic Algorithm and Particle Swarm Optimization approach. The solution illustrates that the model can be applied in a real world scenario to enhance productivity and profitability. |
id |
FGV-3_548d023cfcd3985ec0ab7a2ccc9c7bf5 |
---|---|
oai_identifier_str |
oai:ojs.periodicos.fgv.br:article/67615 |
network_acronym_str |
FGV-3 |
network_name_str |
JOSCM. Journal of Operations and Supply Chain Management |
repository_id_str |
|
spelling |
Modeling and optimization of multi-layer aggregate production planningAggregate production planninginventory optimizationmulti-period modelinggenetic algorithmparticle swarm optimizationAggregate production planning has attracted the attention of researchers for quite a long time now; and the continued researches depict the significance and scope for improvement in this arena. Here, a multi-product, multi-level and multi-period model has been formulated to identify the required aggregate plan for meeting the forecast demand, by regulating production rates, inventory, workforce, various production costs, and other controllable variables. Several new contributing factors, such as costs related to material handling, raw material inventory and worker training have been included in the objective function and constraint equations to make the model more realistic. A case study has been presented for a cosmetics and toiletries manufacturer in Bangladesh. Eventually, the problem has been solved using Genetic Algorithm and Particle Swarm Optimization approach. The solution illustrates that the model can be applied in a real world scenario to enhance productivity and profitability.FGV EAESP2018-11-17info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.fgv.br/joscm/article/view/6761510.12660/joscmv11n2p1-15Journal of Operations and Supply Chain Management; Vol. 11 No. 2 (2018): July-December; 1-15Journal of Operations and Supply Chain Management; v. 11 n. 2 (2018): July-December; 1-151984-3046reponame:JOSCM. Journal of Operations and Supply Chain Managementinstname:Fundação Getulio Vargas (FGV)instacron:FGVenghttps://periodicos.fgv.br/joscm/article/view/67615/pdf_55Copyright (c) 2018 Journal of Operations and Supply Chain Managementinfo:eu-repo/semantics/openAccessAziz, Ridwan AlPaul, Himangshu KumarKarim, Touseef MashrurulAhmed, ImtiazAzeem, Abdullahil2019-02-15T18:21:48Zoai:ojs.periodicos.fgv.br:article/67615Revistahttp://bibliotecadigital.fgv.br/ojs/index.php/joscmPRIhttp://bibliotecadigital.fgv.br/ojs/index.php/joscm/oai||joscm@fgv.br1984-30461984-3046opendoar:2019-02-15T18:21:48JOSCM. Journal of Operations and Supply Chain Management - Fundação Getulio Vargas (FGV)false |
dc.title.none.fl_str_mv |
Modeling and optimization of multi-layer aggregate production planning |
title |
Modeling and optimization of multi-layer aggregate production planning |
spellingShingle |
Modeling and optimization of multi-layer aggregate production planning Aziz, Ridwan Al Aggregate production planning inventory optimization multi-period modeling genetic algorithm particle swarm optimization |
title_short |
Modeling and optimization of multi-layer aggregate production planning |
title_full |
Modeling and optimization of multi-layer aggregate production planning |
title_fullStr |
Modeling and optimization of multi-layer aggregate production planning |
title_full_unstemmed |
Modeling and optimization of multi-layer aggregate production planning |
title_sort |
Modeling and optimization of multi-layer aggregate production planning |
author |
Aziz, Ridwan Al |
author_facet |
Aziz, Ridwan Al Paul, Himangshu Kumar Karim, Touseef Mashrurul Ahmed, Imtiaz Azeem, Abdullahil |
author_role |
author |
author2 |
Paul, Himangshu Kumar Karim, Touseef Mashrurul Ahmed, Imtiaz Azeem, Abdullahil |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Aziz, Ridwan Al Paul, Himangshu Kumar Karim, Touseef Mashrurul Ahmed, Imtiaz Azeem, Abdullahil |
dc.subject.por.fl_str_mv |
Aggregate production planning inventory optimization multi-period modeling genetic algorithm particle swarm optimization |
topic |
Aggregate production planning inventory optimization multi-period modeling genetic algorithm particle swarm optimization |
description |
Aggregate production planning has attracted the attention of researchers for quite a long time now; and the continued researches depict the significance and scope for improvement in this arena. Here, a multi-product, multi-level and multi-period model has been formulated to identify the required aggregate plan for meeting the forecast demand, by regulating production rates, inventory, workforce, various production costs, and other controllable variables. Several new contributing factors, such as costs related to material handling, raw material inventory and worker training have been included in the objective function and constraint equations to make the model more realistic. A case study has been presented for a cosmetics and toiletries manufacturer in Bangladesh. Eventually, the problem has been solved using Genetic Algorithm and Particle Swarm Optimization approach. The solution illustrates that the model can be applied in a real world scenario to enhance productivity and profitability. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-11-17 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicos.fgv.br/joscm/article/view/67615 10.12660/joscmv11n2p1-15 |
url |
https://periodicos.fgv.br/joscm/article/view/67615 |
identifier_str_mv |
10.12660/joscmv11n2p1-15 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://periodicos.fgv.br/joscm/article/view/67615/pdf_55 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2018 Journal of Operations and Supply Chain Management info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2018 Journal of Operations and Supply Chain Management |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
FGV EAESP |
publisher.none.fl_str_mv |
FGV EAESP |
dc.source.none.fl_str_mv |
Journal of Operations and Supply Chain Management; Vol. 11 No. 2 (2018): July-December; 1-15 Journal of Operations and Supply Chain Management; v. 11 n. 2 (2018): July-December; 1-15 1984-3046 reponame:JOSCM. Journal of Operations and Supply Chain Management instname:Fundação Getulio Vargas (FGV) instacron:FGV |
instname_str |
Fundação Getulio Vargas (FGV) |
instacron_str |
FGV |
institution |
FGV |
reponame_str |
JOSCM. Journal of Operations and Supply Chain Management |
collection |
JOSCM. Journal of Operations and Supply Chain Management |
repository.name.fl_str_mv |
JOSCM. Journal of Operations and Supply Chain Management - Fundação Getulio Vargas (FGV) |
repository.mail.fl_str_mv |
||joscm@fgv.br |
_version_ |
1798943730686754816 |