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Research Articles

Toward efficient waste electric vehicle battery recycling via auction-based market trading mechanisms

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Pages 8598-8617 | Received 15 Jun 2022, Accepted 02 Dec 2022, Published online: 28 Dec 2022
 

Abstract

This paper proposes an auction-based market trade mechanism for the electric vehicle battery recycling (EVBR) problem, which aims to realise the optimal resource allocation and pricing of EVBR. The main motivation of this paper is to attempt to explore an approach to achieving efficient battery recycling. We first consider an EVBR market with m buyers and n sellers, and develop the multi-unit trade reduction (MTR) mechanism in the EVBR market. According to the supply and demand relationship in the EVBR market, we consider three market scenarios of supply and demand balance, oversupply, and overdemand, and formulated corresponding auction allocation rules. Numerical study results show that the proposed MTR mechanism can achieve efficient resource allocation. We also observed that not all results increased with the number of sellers/buyers. Second, considering the distance between sellers and buyers, we developed a stochastic multiple MTR (SM-MTR) mechanism to enable sellers and buyers within the region to conduct transactions. Finally, we propose an integrated MTR, SM-MTR and one-sided Vickrey–Clarke–Groves auction mechanism that is feasible in both one-sided and bilateral environments. Furthermore, our work can provide novel managerial implications for EVBR market stakeholders in terms of practical application.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article.

Notes

Additional information

Funding

This work is supported by the National Natural Science Foundation of China [grant numbers 72071093, 72231005, and 71901023], the 2019 Guangdong Special Support Talent Program–Innovation and Entrepreneurship Leading Team (China) [grant number 2019BT02S593], 2018 Guangzhou Leading Innovation Team Program [grant number 201909010006], RGC TRS Project [grant number T32-707-22-N], Beijing Social Science Foundation [grant number 20GLC057], and the Humanities and Social Sciences Research Project from Department of Education of Guangdong Province [grant number 2022WQNCX201], Fundamental Research Funds for the Central Universities [grant number2021JBW111], and Research Center for Central and Eastern Europe.

Notes on contributors

Su Xiu Xu

Su Xiu Xu is currently a professor in the School of Management and Economics, Beijing Institute of Technology, Beijing, China. He received his B.S. in mathematics from Harbin Institute of Technology (Harbin, China) in 2008 and PhD in industrial engineering from the University of Hong Kong in 2014. His major research interests are smart city, auction mechanism design, logistics and operations management. He has published more than 50 papers in such journals as International Journal of Production Research, IISE Transactions, Transportation Science, Transportation Research Part B, Transportation Research Part E, Transportation Research Part A, Production and Operations Management, Ecological Economics, International Journal of Production Economics, IEEE Transactions on Automation Science and Engineering, etc.

Jianghong Feng

Jianghong Feng is a Ph.D. candidate in the School of Management, Jinan University (Guangzhou, China). He received his bachelor degree in industrial engineering and master degree in system science from Sichuan University. His major research interests are smart city, auction mechanism design, logistics and operations management, waste management. He has published papers in such journals as International Journal of Production Research, Transportation Research Part E, Sustainable Cities and Society, Applied Mathematical Modelling, Journal Of Cleaner Production, etc.

George Q. Huang

George Q. Huang is Chair Professor of Smart Manufacturing at Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University. He gained BEng and PhD in Mechanical Engineering from Southeast University (China) and Cardiff University (UK) respectively. He has conducted research projects in areas of Smart Manufacturing, Logistics, and Construction Systems Analytics through IoT-enabled Cyber-Physical Internet with substantial government and industrial grants exceeding HK$100M. He collaborated closely with industries through joint projects and start-up companies. He has published extensively and his works have been widely cited by research communities. He serves as associate editors and editorial members for several international journals. He is Chartered Engineer (CEng), Fellow of ASME, HKIE, IET, CILT, and IISE.

Yue Zhai

Yue Zhai is currently an associate professor in the School of Economics and Management, Beijing Jiaotong University, Beijing, China. She received her B.E. in Automation from Beijing University of Posts and Telecommunications, Beijing, China in 2013 and PhD in industrial engineering from the University of Hong Kong in 2017. Her major research interests are operational hedging, supply chain coordination, prefabricated construction supply chain. She has published more than 10 papers in such journals as International Journal of Production Research, International Journal of Production Economics, European Journal of Operational Research, Computers & Industrial Engineering, Advanced Engineering Informatics, etc.

Meng Cheng

Meng Cheng is currently an assistant professor in the School of Management, Shenzhen Polytechnic, Shenzhen, China. She received her B.S. in engineering from the University of Science and Technology of China (Hefei, China) in 2011 and Ph.D. in industrial engineering from the University of Hong Kong in 2016. Her research interests include auction mechanism design and supply chain management. She has published papers in such journals as International Journal of Production Research, IISE Transactions, Transportation Science, Transportation Research Part B, Transportation Research Part E, IEEE Transactions on Automation Science and Engineering, etc.

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