Bottleneck prediction and data-driven discrete-event simulation for a balanced manufacturing line
Autor(a) principal: | |
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Data de Publicação: | 2022 |
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/10773/35452 |
Resumo: | Bottleneck identification is a relevant tool for continuous optimization of production lines. In this work, we implement a data-driven discrete-event simulator (DDS) based on experimental distributions, obtained from real historical data. The DDS allows to analyse the behavior of a balanced manufacturing line at Bosch Thermotechnology, under different hypotheses. It shows that some scenarios perceived as likely to increase output may actually decrease production metrics, reveals the importance of line injection rates, and leads to the need for adequate real time bottleneck forecasting tools, which allow shift managers intervention in a useful time frame. Eleven prediction models are tested, where a random forest and a multi-layer perceptron attain the best performances (above 95% in all metrics). This data flow is operationalized through a micro-services pipeline which is briefly discussed. |
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Bottleneck prediction and data-driven discrete-event simulation for a balanced manufacturing lineData-driven discrete-event simulationBottleneck predictionMachine learningManufacturing linesBottleneck identification is a relevant tool for continuous optimization of production lines. In this work, we implement a data-driven discrete-event simulator (DDS) based on experimental distributions, obtained from real historical data. The DDS allows to analyse the behavior of a balanced manufacturing line at Bosch Thermotechnology, under different hypotheses. It shows that some scenarios perceived as likely to increase output may actually decrease production metrics, reveals the importance of line injection rates, and leads to the need for adequate real time bottleneck forecasting tools, which allow shift managers intervention in a useful time frame. Eleven prediction models are tested, where a random forest and a multi-layer perceptron attain the best performances (above 95% in all metrics). This data flow is operationalized through a micro-services pipeline which is briefly discussed.Elsevier2022-12-19T13:03:25Z2022-01-01T00:00:00Z2022info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/35452eng1877-050910.1016/j.procs.2022.01.314Rocha, Eugénio M.Lopes, Maria J.info: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:RCAAP2024-02-22T12:08:07Zoai:ria.ua.pt:10773/35452Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:06:24.581097Repositório 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 |
Bottleneck prediction and data-driven discrete-event simulation for a balanced manufacturing line |
title |
Bottleneck prediction and data-driven discrete-event simulation for a balanced manufacturing line |
spellingShingle |
Bottleneck prediction and data-driven discrete-event simulation for a balanced manufacturing line Rocha, Eugénio M. Data-driven discrete-event simulation Bottleneck prediction Machine learning Manufacturing lines |
title_short |
Bottleneck prediction and data-driven discrete-event simulation for a balanced manufacturing line |
title_full |
Bottleneck prediction and data-driven discrete-event simulation for a balanced manufacturing line |
title_fullStr |
Bottleneck prediction and data-driven discrete-event simulation for a balanced manufacturing line |
title_full_unstemmed |
Bottleneck prediction and data-driven discrete-event simulation for a balanced manufacturing line |
title_sort |
Bottleneck prediction and data-driven discrete-event simulation for a balanced manufacturing line |
author |
Rocha, Eugénio M. |
author_facet |
Rocha, Eugénio M. Lopes, Maria J. |
author_role |
author |
author2 |
Lopes, Maria J. |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Rocha, Eugénio M. Lopes, Maria J. |
dc.subject.por.fl_str_mv |
Data-driven discrete-event simulation Bottleneck prediction Machine learning Manufacturing lines |
topic |
Data-driven discrete-event simulation Bottleneck prediction Machine learning Manufacturing lines |
description |
Bottleneck identification is a relevant tool for continuous optimization of production lines. In this work, we implement a data-driven discrete-event simulator (DDS) based on experimental distributions, obtained from real historical data. The DDS allows to analyse the behavior of a balanced manufacturing line at Bosch Thermotechnology, under different hypotheses. It shows that some scenarios perceived as likely to increase output may actually decrease production metrics, reveals the importance of line injection rates, and leads to the need for adequate real time bottleneck forecasting tools, which allow shift managers intervention in a useful time frame. Eleven prediction models are tested, where a random forest and a multi-layer perceptron attain the best performances (above 95% in all metrics). This data flow is operationalized through a micro-services pipeline which is briefly discussed. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-12-19T13:03:25Z 2022-01-01T00:00:00Z 2022 |
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/10773/35452 |
url |
http://hdl.handle.net/10773/35452 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1877-0509 10.1016/j.procs.2022.01.314 |
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.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799137718965370880 |