Enhancing the sustainability and robustness of critical material supply in electrical vehicle market: an AI-powered supplier selection approach

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dc.authoridwang, zhujun/0000-0002-3868-6509
dc.contributor.authorWang, Zhu-Jun
dc.contributor.authorChen, Zhen-Song
dc.contributor.authorSu, Qin
dc.contributor.authorChin, Kwai-Sang
dc.contributor.authorPedrycz, Witold
dc.contributor.authorSkibniewski, Miroslaw J.
dc.date.accessioned2024-05-19T14:46:52Z
dc.date.available2024-05-19T14:46:52Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractIn light of the burgeoning electric vehicle market, the demand for lithium-ion batteries (LiBs) is on the rise. However, the supply of materials essential for LiBs is struggling to keep pace, posing a significant challenge in meeting the surging market demand. This study offers a viable solution to bolster the dependability of the material supply chain by prioritizing material suppliers who are deeply committed to sustainable practices and performance. We have developed a comprehensive system for evaluating sustainable performance, encompassing three vital dimensions: economic, social and environmental contexts. Then, we introduced a pioneering approach known as the multi-criteria material supplier selection (MCMSS) methodology which amalgamates multi-criteria decision-making techniques with artificial intelligence to effectively generate sustainability performance of suppliers and identify the most suitable supplier, out of all alternatives. Eventually, the supply of four key materials of LiBs is used as illustrative examples to verify the feasibility and rationality of the proposed MCMSS. This work carries significant implications for overseeing the LiB material industry. The MCMSS model offers a solution for the government to establish a comprehensive material supplier database to intelligently supervise the activities of material suppliers and foster collaboration between upstream and downstream enterprises within the LiB industry.en_US
dc.description.sponsorshipMajor Program of National Nature Science Foundation of Chinaen_US
dc.description.sponsorshipThe authors thank in advance the editor-in-chief, guest editors, and anonymous referees for their time and effort in handling and reviewing this paper.en_US
dc.identifier.doi10.1007/s10479-023-05698-4
dc.identifier.issn0254-5330
dc.identifier.issn1572-9338
dc.identifier.scopus2-s2.0-85178925678en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1007/s10479-023-05698-4
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5610
dc.identifier.wosWOS:001116592400002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofAnnals of Operations Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectSustainabilityen_US
dc.subjectLibs Material Supply Chainen_US
dc.subjectGroup Decision Makingen_US
dc.subjectSupplier Selectionen_US
dc.titleEnhancing the sustainability and robustness of critical material supply in electrical vehicle market: an AI-powered supplier selection approachen_US
dc.typeArticleen_US

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