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Article: Industrial internet platform-driven decentralised multi-level synchronised reconfiguration of assembly supply chains

TitleIndustrial internet platform-driven decentralised multi-level synchronised reconfiguration of assembly supply chains
Authors
Keywordsaugmented lagrangian coordination
decentralised supply chain
graph neural networks
Industrial internet
industry chain
synchronised reconfiguration
Issue Date8-Jan-2025
PublisherTaylor and Francis Group
Citation
International Journal of Production Research, 2025, v. 63, n. 12, p. 4328-4350 How to Cite?
Abstract

Disruptive environments, characterised by frequent contingencies, significantly challenge supply chain operations, particularly assembly supply chains with complex structures and decentralised decision-making. Failure to promptly reconfigure chains during disruptions may ripple adverse effects through the network, causing substantial losses. Consequently, decentralised dynamic reconfiguration of assembly supply chains under contingencies warrants attention. However, existing research on this topic is limited, necessitating further exploration utilising emerging technologies. Isolated information systems, such as MES and ERP, are widely deployed across firms and increasingly integrated into industry-level Industrial Internet Platforms (IIPs) to enhance information sharing and utilisation. Nevertheless, limited research explores the integration of real-time IIP data with reconfiguration mechanisms for resilient, flexible, and decentralised chain reconfiguration. Thus, this paper proposes an IIP-driven synchronised reconfiguration of supply chains (SyncRSC) solution inspired by the concepts of synchronisation and reconfigurable supply chains. SyncRSC employs the IIP architecture for real-time information support, a three-state mechanism as the qualitative method, and improved graph neural networks alongside augmented lagrangian coordination as quantitative methods. A case study of an air-conditioning supply chain verifies the superiority of the proposed methods and analyzes SyncRSC's performance under different disruption levels.


Persistent Identifierhttp://hdl.handle.net/10722/367090
ISSN
2023 Impact Factor: 7.0
2023 SCImago Journal Rankings: 2.668

 

DC FieldValueLanguage
dc.contributor.authorHuang, Hai nan-
dc.contributor.authorQu, Ting-
dc.contributor.authorQiu, Xiao hui-
dc.contributor.authorMa, Lin-
dc.contributor.authorNie, Duxian-
dc.contributor.authorLi, Congdong-
dc.contributor.authorHuang, George Q.-
dc.date.accessioned2025-12-03T00:35:26Z-
dc.date.available2025-12-03T00:35:26Z-
dc.date.issued2025-01-08-
dc.identifier.citationInternational Journal of Production Research, 2025, v. 63, n. 12, p. 4328-4350-
dc.identifier.issn0020-7543-
dc.identifier.urihttp://hdl.handle.net/10722/367090-
dc.description.abstract<p>Disruptive environments, characterised by frequent contingencies, significantly challenge supply chain operations, particularly assembly supply chains with complex structures and decentralised decision-making. Failure to promptly reconfigure chains during disruptions may ripple adverse effects through the network, causing substantial losses. Consequently, decentralised dynamic reconfiguration of assembly supply chains under contingencies warrants attention. However, existing research on this topic is limited, necessitating further exploration utilising emerging technologies. Isolated information systems, such as MES and ERP, are widely deployed across firms and increasingly integrated into industry-level Industrial Internet Platforms (IIPs) to enhance information sharing and utilisation. Nevertheless, limited research explores the integration of real-time IIP data with reconfiguration mechanisms for resilient, flexible, and decentralised chain reconfiguration. Thus, this paper proposes an IIP-driven synchronised reconfiguration of supply chains (SyncRSC) solution inspired by the concepts of synchronisation and reconfigurable supply chains. SyncRSC employs the IIP architecture for real-time information support, a three-state mechanism as the qualitative method, and improved graph neural networks alongside augmented lagrangian coordination as quantitative methods. A case study of an air-conditioning supply chain verifies the superiority of the proposed methods and analyzes SyncRSC's performance under different disruption levels.</p>-
dc.languageeng-
dc.publisherTaylor and Francis Group-
dc.relation.ispartofInternational Journal of Production Research-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectaugmented lagrangian coordination-
dc.subjectdecentralised supply chain-
dc.subjectgraph neural networks-
dc.subjectIndustrial internet-
dc.subjectindustry chain-
dc.subjectsynchronised reconfiguration-
dc.titleIndustrial internet platform-driven decentralised multi-level synchronised reconfiguration of assembly supply chains-
dc.typeArticle-
dc.identifier.doi10.1080/00207543.2024.2448284-
dc.identifier.scopuseid_2-s2.0-85214425802-
dc.identifier.volume63-
dc.identifier.issue12-
dc.identifier.spage4328-
dc.identifier.epage4350-
dc.identifier.eissn1366-588X-
dc.identifier.issnl0020-7543-

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