Modelling and solving sustainable supply chain network design based on graph autoencoder clustering algorithm.
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| Title: | Modelling and solving sustainable supply chain network design based on graph autoencoder clustering algorithm. |
|---|---|
| Authors: | Guo, Yuhan1 (AUTHOR) yuhan.guo@zust.edu.cn, Chen, Runsheng2 (AUTHOR), Boulaksil, Youssef3 (AUTHOR), Allaoui, Hamid4 (AUTHOR) |
| Source: | International Journal of Production Research. Dec2025, Vol. 63 Issue 24, p10000-10026. 27p. |
| Subjects: | Sustainable development, Sustainability, Graph neural networks, Mathematical models, Logistics, Supply chains, Clustering algorithms |
| Abstract: | The modelling of Sustainable Supply Chain Network Design (SSCND) is evolving significantly with increasing problem diversity and complexity. Existing algorithms face substantial challenges in accommodating various models and effectively handling large-scale instances. To address these challenges, we propose an intercity distances supply chain network model to reflect real-world scenarios, and develop a clustering mapping algorithm based on Graph Autoencoder (GAE) to solve the model. The mathematical model incorporates both economic and environmental sustainability dimensions, while also including supply chain responsiveness metrics. The algorithm operates by abstracting attribute information of supply chain potential participants, generating sparse graphs, and applying GAE clustering to create abstract nodes. The classified abstract nodes are then processed using the simplex method to generate preliminary solutions, which are subsequently mapped back into real-world solutions through a mapping mechanism. Experimental results demonstrate the high efficiency and stability of the proposed approach. For large-scale instances, it produces high-quality solutions in substantially less time compared to commercial solvers like CPLEX, offering a novel and practical approach for enterprises addressing sustainable supply chain design challenges. [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: 189933531 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Modelling and solving sustainable supply chain network design based on graph autoencoder clustering algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Guo%2C+Yuhan%22">Guo, Yuhan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yuhan.guo@zust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Runsheng%22">Chen, Runsheng</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Boulaksil%2C+Youssef%22">Boulaksil, Youssef</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Allaoui%2C+Hamid%22">Allaoui, Hamid</searchLink><relatesTo>4</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>. Dec2025, Vol. 63 Issue 24, p10000-10026. 27p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Sustainable+development%22">Sustainable development</searchLink><br /><searchLink fieldCode="DE" term="%22Sustainability%22">Sustainability</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+neural+networks%22">Graph neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink><br /><searchLink fieldCode="DE" term="%22Logistics%22">Logistics</searchLink><br /><searchLink fieldCode="DE" term="%22Supply+chains%22">Supply chains</searchLink><br /><searchLink fieldCode="DE" term="%22Clustering+algorithms%22">Clustering algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The modelling of Sustainable Supply Chain Network Design (SSCND) is evolving significantly with increasing problem diversity and complexity. Existing algorithms face substantial challenges in accommodating various models and effectively handling large-scale instances. To address these challenges, we propose an intercity distances supply chain network model to reflect real-world scenarios, and develop a clustering mapping algorithm based on Graph Autoencoder (GAE) to solve the model. The mathematical model incorporates both economic and environmental sustainability dimensions, while also including supply chain responsiveness metrics. The algorithm operates by abstracting attribute information of supply chain potential participants, generating sparse graphs, and applying GAE clustering to create abstract nodes. The classified abstract nodes are then processed using the simplex method to generate preliminary solutions, which are subsequently mapped back into real-world solutions through a mapping mechanism. Experimental results demonstrate the high efficiency and stability of the proposed approach. For large-scale instances, it produces high-quality solutions in substantially less time compared to commercial solvers like CPLEX, offering a novel and practical approach for enterprises addressing sustainable supply chain design challenges. [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: 10.1080/00207543.2025.2542506 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 10000 Subjects: – SubjectFull: Sustainable development Type: general – SubjectFull: Sustainability Type: general – SubjectFull: Graph neural networks Type: general – SubjectFull: Mathematical models Type: general – SubjectFull: Logistics Type: general – SubjectFull: Supply chains Type: general – SubjectFull: Clustering algorithms Type: general Titles: – TitleFull: Modelling and solving sustainable supply chain network design based on graph autoencoder clustering algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Guo, Yuhan – PersonEntity: Name: NameFull: Chen, Runsheng – PersonEntity: Name: NameFull: Boulaksil, Youssef – PersonEntity: Name: NameFull: Allaoui, Hamid IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 63 – Type: issue Value: 24 Titles: – TitleFull: International Journal of Production Research Type: main |
| ResultId | 1 |
