Reinforcement learning based trustworthy recommendation model for digital twin-driven decision-support in manufacturing systems
Autor(a) principal: | |
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Data de Publicação: | 2023 |
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/10198/27067 |
Resumo: | Digital twin is one promising and key technology that emerged with Industry 4.0 to assist the decision-making process in multiple industries, enabling potential benefits such as reducing costs, and risk, improving efficiency, and supporting decision-making. Despite these, the decision-making approach of carrying out a what-if simulation study using digital twin models of each and every possible scenario independently is time-consuming and requires significant computational resources. The integration of recommendation systems within the digital twindriven decision-support framework can support the decision-making process by providing targeted scenario recommendations, reducing the decision-making time and imposing decision- making efficiency. However, recommendation systems have inherent challenges, such as cold-start, data sparsity, and prediction accuracy. The integration of trust and similarity measures with recommendation systems alleviates the challenges mentioned earlier, and the integration of machine learning techniques enables better recommendations through their ability to simulate human learning. Having this in mind, this paper proposes a trust-based recommendation approach using a reinforcement learning technique combined with similarity measures, which can be integrated within a digital twin-based what-if simulation decision-support system. This approach was experimentally validated by performing accurate recommendations in an industrial case study of a battery pack assembly line. The results show improvements in the proposed model regarding the accuracy of the prediction about the user rating of the recommended scenarios over the state-of-the-art recommendation approaches, particularly in coldstart and data sparsity scenarios. |
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Reinforcement learning based trustworthy recommendation model for digital twin-driven decision-support in manufacturing systemsDigital twinDecision-supportRecommendation systemsSimilarity measuresTrust-based modelDigital twin is one promising and key technology that emerged with Industry 4.0 to assist the decision-making process in multiple industries, enabling potential benefits such as reducing costs, and risk, improving efficiency, and supporting decision-making. Despite these, the decision-making approach of carrying out a what-if simulation study using digital twin models of each and every possible scenario independently is time-consuming and requires significant computational resources. The integration of recommendation systems within the digital twindriven decision-support framework can support the decision-making process by providing targeted scenario recommendations, reducing the decision-making time and imposing decision- making efficiency. However, recommendation systems have inherent challenges, such as cold-start, data sparsity, and prediction accuracy. The integration of trust and similarity measures with recommendation systems alleviates the challenges mentioned earlier, and the integration of machine learning techniques enables better recommendations through their ability to simulate human learning. Having this in mind, this paper proposes a trust-based recommendation approach using a reinforcement learning technique combined with similarity measures, which can be integrated within a digital twin-based what-if simulation decision-support system. This approach was experimentally validated by performing accurate recommendations in an industrial case study of a battery pack assembly line. The results show improvements in the proposed model regarding the accuracy of the prediction about the user rating of the recommended scenarios over the state-of-the-art recommendation approaches, particularly in coldstart and data sparsity scenarios.ElsevierBiblioteca Digital do IPBPires, FláviaLeitão, PauloMoreira, António Paulo G. M.Ahmad, Bilal2023-02-20T15:37:46Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10198/27067engPires, Flávia; Leitão, Paulo; Moreira, António Paulo G. M.; Ahmad, Bilal (2023). Reinforcement learning based trustworthy recommendation model for digital twin-driven decision-support in manufacturing systems. Computers in Industry. eISSN 1872-6194. 158, p. 1-130166-361510.1016/j.compind.2023.1038841872-6194info: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:RCAAP2024-02-14T01:17:43Zoai:bibliotecadigital.ipb.pt:10198/27067Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:17:28.466616Repositório 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 |
Reinforcement learning based trustworthy recommendation model for digital twin-driven decision-support in manufacturing systems |
title |
Reinforcement learning based trustworthy recommendation model for digital twin-driven decision-support in manufacturing systems |
spellingShingle |
Reinforcement learning based trustworthy recommendation model for digital twin-driven decision-support in manufacturing systems Pires, Flávia Digital twin Decision-support Recommendation systems Similarity measures Trust-based model |
title_short |
Reinforcement learning based trustworthy recommendation model for digital twin-driven decision-support in manufacturing systems |
title_full |
Reinforcement learning based trustworthy recommendation model for digital twin-driven decision-support in manufacturing systems |
title_fullStr |
Reinforcement learning based trustworthy recommendation model for digital twin-driven decision-support in manufacturing systems |
title_full_unstemmed |
Reinforcement learning based trustworthy recommendation model for digital twin-driven decision-support in manufacturing systems |
title_sort |
Reinforcement learning based trustworthy recommendation model for digital twin-driven decision-support in manufacturing systems |
author |
Pires, Flávia |
author_facet |
Pires, Flávia Leitão, Paulo Moreira, António Paulo G. M. Ahmad, Bilal |
author_role |
author |
author2 |
Leitão, Paulo Moreira, António Paulo G. M. Ahmad, Bilal |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Biblioteca Digital do IPB |
dc.contributor.author.fl_str_mv |
Pires, Flávia Leitão, Paulo Moreira, António Paulo G. M. Ahmad, Bilal |
dc.subject.por.fl_str_mv |
Digital twin Decision-support Recommendation systems Similarity measures Trust-based model |
topic |
Digital twin Decision-support Recommendation systems Similarity measures Trust-based model |
description |
Digital twin is one promising and key technology that emerged with Industry 4.0 to assist the decision-making process in multiple industries, enabling potential benefits such as reducing costs, and risk, improving efficiency, and supporting decision-making. Despite these, the decision-making approach of carrying out a what-if simulation study using digital twin models of each and every possible scenario independently is time-consuming and requires significant computational resources. The integration of recommendation systems within the digital twindriven decision-support framework can support the decision-making process by providing targeted scenario recommendations, reducing the decision-making time and imposing decision- making efficiency. However, recommendation systems have inherent challenges, such as cold-start, data sparsity, and prediction accuracy. The integration of trust and similarity measures with recommendation systems alleviates the challenges mentioned earlier, and the integration of machine learning techniques enables better recommendations through their ability to simulate human learning. Having this in mind, this paper proposes a trust-based recommendation approach using a reinforcement learning technique combined with similarity measures, which can be integrated within a digital twin-based what-if simulation decision-support system. This approach was experimentally validated by performing accurate recommendations in an industrial case study of a battery pack assembly line. The results show improvements in the proposed model regarding the accuracy of the prediction about the user rating of the recommended scenarios over the state-of-the-art recommendation approaches, particularly in coldstart and data sparsity scenarios. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-02-20T15:37:46Z 2023 2023-01-01T00:00:00Z |
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/10198/27067 |
url |
http://hdl.handle.net/10198/27067 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Pires, Flávia; Leitão, Paulo; Moreira, António Paulo G. M.; Ahmad, Bilal (2023). Reinforcement learning based trustworthy recommendation model for digital twin-driven decision-support in manufacturing systems. Computers in Industry. eISSN 1872-6194. 158, p. 1-13 0166-3615 10.1016/j.compind.2023.103884 1872-6194 |
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 |
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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) |
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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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