Author
Listed:
- Zhu-Jun Wang
(Xi’an Jiaotong University
University of Maryland)
- Zhen-Song Chen
(Wuhan University)
- Qin Su
(Xi’an Jiaotong University)
- Kwai-Sang Chin
(City University of Hong Kong
City University of Hong Kong Shenzhen Research Institute)
- Witold Pedrycz
(University of Alberta
Polish Academy of Sciences
Istinye University)
- Mirosław J. Skibniewski
(University of Maryland
Polish Academy of Sciences
Chaoyang University of Technology)
Abstract
In 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.
Suggested Citation
Zhu-Jun Wang & Zhen-Song Chen & Qin Su & Kwai-Sang Chin & Witold Pedrycz & Mirosław J. Skibniewski, 2024.
"Enhancing the sustainability and robustness of critical material supply in electrical vehicle market: an AI-powered supplier selection approach,"
Annals of Operations Research, Springer, vol. 342(1), pages 921-958, November.
Handle:
RePEc:spr:annopr:v:342:y:2024:i:1:d:10.1007_s10479-023-05698-4
DOI: 10.1007/s10479-023-05698-4
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