A study on data-driven and multi-scale modeling method for production system simulation “Model configuration and identification for flow shop production systems”
To efficiently operate complicated production systems such as mixed-flow production, production system simulation is a promising tool. However, constructing accurate simulation models requires significant effort. In this study, we proposed a data-driven and multi-scale modeling approach that optimiz...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | Japanese |
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The Japan Society of Mechanical Engineers
2024-12-01
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| Series: | Nihon Kikai Gakkai ronbunshu |
| Subjects: | |
| Online Access: | https://www.jstage.jst.go.jp/article/transjsme/91/941/91_24-00107/_pdf/-char/en |
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| _version_ | 1832584950902161408 |
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| author | Satoshi NAGAHARA Toshiya KAIHARA Nobutada FUJII Daisuke KOKURYO |
| author_facet | Satoshi NAGAHARA Toshiya KAIHARA Nobutada FUJII Daisuke KOKURYO |
| author_sort | Satoshi NAGAHARA |
| collection | DOAJ |
| description | To efficiently operate complicated production systems such as mixed-flow production, production system simulation is a promising tool. However, constructing accurate simulation models requires significant effort. In this study, we proposed a data-driven and multi-scale modeling approach that optimizes model configuration by combining multiple modeling methods. In order to verify the usefulness of the proposed approach, we organized the possible model configurations from the perspective of available data from target system and conducted computational experiments for flow shop production systems to compare various model configurations. The experimental results show that the optimal model configuration strongly depends on the complexity of target system and models to be identified, and it is important to divide the model so as to reduce the complexity. |
| format | Article |
| id | doaj-art-ae7aa5c7653849eca4bac5916652fbc4 |
| institution | Kabale University |
| issn | 2187-9761 |
| language | Japanese |
| publishDate | 2024-12-01 |
| publisher | The Japan Society of Mechanical Engineers |
| record_format | Article |
| series | Nihon Kikai Gakkai ronbunshu |
| spelling | doaj-art-ae7aa5c7653849eca4bac5916652fbc42025-01-27T08:34:35ZjpnThe Japan Society of Mechanical EngineersNihon Kikai Gakkai ronbunshu2187-97612024-12-019194124-0010724-0010710.1299/transjsme.24-00107transjsmeA study on data-driven and multi-scale modeling method for production system simulation “Model configuration and identification for flow shop production systems”Satoshi NAGAHARA0Toshiya KAIHARA1Nobutada FUJII2Daisuke KOKURYO3Industry Automation Research Department., Research and Development Group, Hitachi, Ltd.Graduate School of System Informatics, Kobe UniversityGraduate School of System Informatics, Kobe UniversityGraduate School of System Informatics, Kobe UniversityTo efficiently operate complicated production systems such as mixed-flow production, production system simulation is a promising tool. However, constructing accurate simulation models requires significant effort. In this study, we proposed a data-driven and multi-scale modeling approach that optimizes model configuration by combining multiple modeling methods. In order to verify the usefulness of the proposed approach, we organized the possible model configurations from the perspective of available data from target system and conducted computational experiments for flow shop production systems to compare various model configurations. The experimental results show that the optimal model configuration strongly depends on the complexity of target system and models to be identified, and it is important to divide the model so as to reduce the complexity.https://www.jstage.jst.go.jp/article/transjsme/91/941/91_24-00107/_pdf/-char/enproduction system simulationproduction systemsystem identificationmachine learning |
| spellingShingle | Satoshi NAGAHARA Toshiya KAIHARA Nobutada FUJII Daisuke KOKURYO A study on data-driven and multi-scale modeling method for production system simulation “Model configuration and identification for flow shop production systems” Nihon Kikai Gakkai ronbunshu production system simulation production system system identification machine learning |
| title | A study on data-driven and multi-scale modeling method for production system simulation “Model configuration and identification for flow shop production systems” |
| title_full | A study on data-driven and multi-scale modeling method for production system simulation “Model configuration and identification for flow shop production systems” |
| title_fullStr | A study on data-driven and multi-scale modeling method for production system simulation “Model configuration and identification for flow shop production systems” |
| title_full_unstemmed | A study on data-driven and multi-scale modeling method for production system simulation “Model configuration and identification for flow shop production systems” |
| title_short | A study on data-driven and multi-scale modeling method for production system simulation “Model configuration and identification for flow shop production systems” |
| title_sort | study on data driven and multi scale modeling method for production system simulation model configuration and identification for flow shop production systems |
| topic | production system simulation production system system identification machine learning |
| url | https://www.jstage.jst.go.jp/article/transjsme/91/941/91_24-00107/_pdf/-char/en |
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