Big data-enabled intelligent synchronisation for the complex production logistics system under the opti-state control strategy.
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| Title: | Big data-enabled intelligent synchronisation for the complex production logistics system under the opti-state control strategy. |
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| Authors: | Zhang, Kai1,2 (AUTHOR), Qu, Ting2,3,4 (AUTHOR) quting@jnu.edu.cn, Zhang, Yongheng1,2 (AUTHOR), Zhong, Ray Y.5 (AUTHOR), Huang, George2,5 (AUTHOR) |
| Source: | International Journal of Production Research. Jul2022, Vol. 60 Issue 13, p4159-4175. 17p. 1 Illustration, 2 Diagrams, 8 Charts, 5 Graphs. |
| Subjects: | Big data, Artificial neural networks, Databases, Feature selection, Logistics, Operating costs |
| Abstract: | Diversified customer needs make the production system more susceptible to high-frequency fluctuations of uncertain factors (UFs), which puts forward higher requirements for the real-time and systematic decision-making of the system. The opti-state control strategy enables the system to maintain the adaptive optimal state after being disturbed. The core intelligent synchronisation of the opti-state control strategy needs to perceive the state of the affected system and its degree of change. Aiming at the challenge of difficulty in evaluating the uncertain factors impact degree (UFID) of the complex production logistics system, this work proposes a big data-enabled intelligent synchronisation under the opti-state control strategy. Based on the simulation data of system operation, big data is used to mine the relationship between the UFID and the system states, then use wrapper GA-DNN (Deep Neural Network) feature selection and classification method evaluates the UFID, which will be applied to the synchronisation decision. The results show that the method can accurately evaluate the UFID and avoid the waste of resources and the increase in operating costs caused by excessive evaluation of the UFID, thereby also improves the effectiveness and efficiency of the opti-state control strategy. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 158009789 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Big data-enabled intelligent synchronisation for the complex production logistics system under the opti-state control strategy. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Kai%22">Zhang, Kai</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qu%2C+Ting%22">Qu, Ting</searchLink><relatesTo>2,3,4</relatesTo> (AUTHOR)<i> quting@jnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yongheng%22">Zhang, Yongheng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhong%2C+Ray+Y%2E%22">Zhong, Ray Y.</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+George%22">Huang, George</searchLink><relatesTo>2,5</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Jul2022, Vol. 60 Issue 13, p4159-4175. 17p. 1 Illustration, 2 Diagrams, 8 Charts, 5 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Databases%22">Databases</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Logistics%22">Logistics</searchLink><br /><searchLink fieldCode="DE" term="%22Operating+costs%22">Operating costs</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Diversified customer needs make the production system more susceptible to high-frequency fluctuations of uncertain factors (UFs), which puts forward higher requirements for the real-time and systematic decision-making of the system. The opti-state control strategy enables the system to maintain the adaptive optimal state after being disturbed. The core intelligent synchronisation of the opti-state control strategy needs to perceive the state of the affected system and its degree of change. Aiming at the challenge of difficulty in evaluating the uncertain factors impact degree (UFID) of the complex production logistics system, this work proposes a big data-enabled intelligent synchronisation under the opti-state control strategy. Based on the simulation data of system operation, big data is used to mine the relationship between the UFID and the system states, then use wrapper GA-DNN (Deep Neural Network) feature selection and classification method evaluates the UFID, which will be applied to the synchronisation decision. The results show that the method can accurately evaluate the UFID and avoid the waste of resources and the increase in operating costs caused by excessive evaluation of the UFID, thereby also improves the effectiveness and efficiency of the opti-state control strategy. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00207543.2021.2000657 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 4159 Subjects: – SubjectFull: Big data Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Databases Type: general – SubjectFull: Feature selection Type: general – SubjectFull: Logistics Type: general – SubjectFull: Operating costs Type: general Titles: – TitleFull: Big data-enabled intelligent synchronisation for the complex production logistics system under the opti-state control strategy. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Kai – PersonEntity: Name: NameFull: Qu, Ting – PersonEntity: Name: NameFull: Zhang, Yongheng – PersonEntity: Name: NameFull: Zhong, Ray Y. – PersonEntity: Name: NameFull: Huang, George IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 60 – Type: issue Value: 13 Titles: – TitleFull: International Journal of Production Research Type: main |
| ResultId | 1 |
