Parallel Metaheuristics for Shop Scheduling: enabling Industry 4.0
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/100685 https://doi.org/10.1016/j.procs.2021.01.328 |
Resumo: | Production scheduling is one of the most critical activities in manufacturing. Under the context of Industry 4.0 paradigm, shop scheduling becomes even more complex. Metaheuristics present the potential to solve these harder problems but demand substantial computational power. The use of high-performance parallel architectures, present in cloud computing and edge computing, may support the develop of better metaheuristics, enabling Industry 4.0 with solution techniques to deal with their scheduling complexity. This study provides an overview of parallel metaheuristics for shop scheduling in recent literature. We reviewed 28 papers and classified them, according to parallel architectures, shop configuration, metaheuristics and optimization criteria. The results support that parallel metaheuristic have potential to tackle Industry 4.0 scheduling problems. However, it is essential to extend the research to the cloud and edge computing, flexible shop configurations, dynamic problems with multi-resource, and multi-objective optimization. Future studies should consider the use of real-world data instances. |
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Parallel Metaheuristics for Shop Scheduling: enabling Industry 4.0Industry 4.0production schedulingmetaheuristicsparallel processingProduction scheduling is one of the most critical activities in manufacturing. Under the context of Industry 4.0 paradigm, shop scheduling becomes even more complex. Metaheuristics present the potential to solve these harder problems but demand substantial computational power. The use of high-performance parallel architectures, present in cloud computing and edge computing, may support the develop of better metaheuristics, enabling Industry 4.0 with solution techniques to deal with their scheduling complexity. This study provides an overview of parallel metaheuristics for shop scheduling in recent literature. We reviewed 28 papers and classified them, according to parallel architectures, shop configuration, metaheuristics and optimization criteria. The results support that parallel metaheuristic have potential to tackle Industry 4.0 scheduling problems. However, it is essential to extend the research to the cloud and edge computing, flexible shop configurations, dynamic problems with multi-resource, and multi-objective optimization. Future studies should consider the use of real-world data instances.2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/100685http://hdl.handle.net/10316/100685https://doi.org/10.1016/j.procs.2021.01.328eng18770509Coelho, PedroSilva, Cristovãoinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-04-16T09:41:01Zoai:estudogeral.uc.pt:10316/100685Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:18:01.185381Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Parallel Metaheuristics for Shop Scheduling: enabling Industry 4.0 |
title |
Parallel Metaheuristics for Shop Scheduling: enabling Industry 4.0 |
spellingShingle |
Parallel Metaheuristics for Shop Scheduling: enabling Industry 4.0 Coelho, Pedro Industry 4.0 production scheduling metaheuristics parallel processing |
title_short |
Parallel Metaheuristics for Shop Scheduling: enabling Industry 4.0 |
title_full |
Parallel Metaheuristics for Shop Scheduling: enabling Industry 4.0 |
title_fullStr |
Parallel Metaheuristics for Shop Scheduling: enabling Industry 4.0 |
title_full_unstemmed |
Parallel Metaheuristics for Shop Scheduling: enabling Industry 4.0 |
title_sort |
Parallel Metaheuristics for Shop Scheduling: enabling Industry 4.0 |
author |
Coelho, Pedro |
author_facet |
Coelho, Pedro Silva, Cristovão |
author_role |
author |
author2 |
Silva, Cristovão |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Coelho, Pedro Silva, Cristovão |
dc.subject.por.fl_str_mv |
Industry 4.0 production scheduling metaheuristics parallel processing |
topic |
Industry 4.0 production scheduling metaheuristics parallel processing |
description |
Production scheduling is one of the most critical activities in manufacturing. Under the context of Industry 4.0 paradigm, shop scheduling becomes even more complex. Metaheuristics present the potential to solve these harder problems but demand substantial computational power. The use of high-performance parallel architectures, present in cloud computing and edge computing, may support the develop of better metaheuristics, enabling Industry 4.0 with solution techniques to deal with their scheduling complexity. This study provides an overview of parallel metaheuristics for shop scheduling in recent literature. We reviewed 28 papers and classified them, according to parallel architectures, shop configuration, metaheuristics and optimization criteria. The results support that parallel metaheuristic have potential to tackle Industry 4.0 scheduling problems. However, it is essential to extend the research to the cloud and edge computing, flexible shop configurations, dynamic problems with multi-resource, and multi-objective optimization. Future studies should consider the use of real-world data instances. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 |
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://hdl.handle.net/10316/100685 http://hdl.handle.net/10316/100685 https://doi.org/10.1016/j.procs.2021.01.328 |
url |
http://hdl.handle.net/10316/100685 https://doi.org/10.1016/j.procs.2021.01.328 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
18770509 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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