Multi-agent deep reinforcement learning for integrated production and maintenance optimisation in shared manufacturing.
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| Title: | Multi-agent deep reinforcement learning for integrated production and maintenance optimisation in shared manufacturing. |
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| Authors: | Peng, Xiaoshuai1 (AUTHOR), Wang, Shiyi1 (AUTHOR), Wang, Kangzhou1 (AUTHOR) kzhwang@lzu.edu.cn |
| Source: | International Journal of Production Research. Dec2025, Vol. 63 Issue 23, p8761-8780. 20p. |
| Subjects: | Reinforcement learning, Production planning, Simulation methods & models, Resource allocation, Multiagent systems, Process optimization, Computer integrated manufacturing systems, Markov processes |
| Abstract: | Coordinating production and maintenance is essential for optimising manufacturing systems but remains challenging due to inherent trade-offs and uncertainties. Shared manufacturing offers a flexible solution by allowing companies to access shared resources, maintain production during maintenance, and enhance overall adaptability and productivity. However, this promising manufacturing model has received limited attention within integrated production and maintenance planning (IPMP). This study bridges this gap by framing the IPMP within shared manufacturing as a Markov decision process. To solve this problem, we propose a multi-agent deep reinforcement learning framework that incorporates three key components: (1) a coordination allocation mechanism with reward reshaping to guide feasible decisions; (2) a multi-discrete action distribution combined with eligibility masking to streamline the decision process; and (3) an attention mechanism paired with generalised advantage estimation to enhance learning efficiency. Computational experiments demonstrate the effectiveness of the proposed framework, highlighting the increasing advantages of shared manufacturing as lost sales costs and demand increase, and shared resource costs decrease. These results indicate that stakeholders should incentivize manufacturing resource suppliers to participate, thereby fostering competition and reducing resource costs. Manufacturers, especially those experiencing high lost sales costs and demand pressures, stand to benefit significantly from adopting this manufacturing model. [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: 189849834 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Multi-agent deep reinforcement learning for integrated production and maintenance optimisation in shared manufacturing. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Peng%2C+Xiaoshuai%22">Peng, Xiaoshuai</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Shiyi%22">Wang, Shiyi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Kangzhou%22">Wang, Kangzhou</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> kzhwang@lzu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Dec2025, Vol. 63 Issue 23, p8761-8780. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Production+planning%22">Production planning</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Multiagent+systems%22">Multiagent systems</searchLink><br /><searchLink fieldCode="DE" term="%22Process+optimization%22">Process optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+integrated+manufacturing+systems%22">Computer integrated manufacturing systems</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+processes%22">Markov processes</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Coordinating production and maintenance is essential for optimising manufacturing systems but remains challenging due to inherent trade-offs and uncertainties. Shared manufacturing offers a flexible solution by allowing companies to access shared resources, maintain production during maintenance, and enhance overall adaptability and productivity. However, this promising manufacturing model has received limited attention within integrated production and maintenance planning (IPMP). This study bridges this gap by framing the IPMP within shared manufacturing as a Markov decision process. To solve this problem, we propose a multi-agent deep reinforcement learning framework that incorporates three key components: (1) a coordination allocation mechanism with reward reshaping to guide feasible decisions; (2) a multi-discrete action distribution combined with eligibility masking to streamline the decision process; and (3) an attention mechanism paired with generalised advantage estimation to enhance learning efficiency. Computational experiments demonstrate the effectiveness of the proposed framework, highlighting the increasing advantages of shared manufacturing as lost sales costs and demand increase, and shared resource costs decrease. These results indicate that stakeholders should incentivize manufacturing resource suppliers to participate, thereby fostering competition and reducing resource costs. Manufacturers, especially those experiencing high lost sales costs and demand pressures, stand to benefit significantly from adopting this manufacturing model. [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: 10.1080/00207543.2025.2514255 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 8761 Subjects: – SubjectFull: Reinforcement learning Type: general – SubjectFull: Production planning Type: general – SubjectFull: Simulation methods & models Type: general – SubjectFull: Resource allocation Type: general – SubjectFull: Multiagent systems Type: general – SubjectFull: Process optimization Type: general – SubjectFull: Computer integrated manufacturing systems Type: general – SubjectFull: Markov processes Type: general Titles: – TitleFull: Multi-agent deep reinforcement learning for integrated production and maintenance optimisation in shared manufacturing. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Peng, Xiaoshuai – PersonEntity: Name: NameFull: Wang, Shiyi – PersonEntity: Name: NameFull: Wang, Kangzhou IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 63 – Type: issue Value: 23 Titles: – TitleFull: International Journal of Production Research Type: main |
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