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Multi-objective combinatorial optimization analysis of the recycling of retired new energy electric vehicle power batteries in a sustainable dynamic reverse logistics network

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Abstract

The recycling of retired new energy vehicle power batteries produces economic benefits and promotes the sustainable development of environment and society. However, few attentions have been paid to the design and optimization of sustainable reverse logistics network for the recycling of retired power batteries. To this end, we develop a six-level sustainable dynamic reverse logistics network model from the perspectives of economy, environment, and society. We solve the multi-objective combinatorial optimization model to explore the layout of the sustainable reverse logistics network for retired new energy vehicle power batteries recycling. A case study is implemented to verify the effectiveness of the proposed model. The results show that (a) the facility nodes near the front of the network fluctuate more by opening and closing; (b) the dynamic reverse logistics network is superior to its static counterpart; and (c) cooperation cost changes affect the transaction volume between third-party and cooperative enterprises and total network cost.

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Data availability

Data will be made available upon request to the corresponding author.

Notes

  1. http://www.gov.cn/zhengce/content/2020-11/02/content_5556716.htm

  2. http://gs.people.com.cn/n2/2022/0112/c183342-35091574.html

  3. https://auto.sina.com.cn/news/hy/2022-10-12/detail-imqmmthc0568790.shtml

  4. https://www.cdqc.org.cn/4/9243/856491

  5. http://cdjx.chengdu.gov.cn/cdsjxw/c132827/2021-05/12/content_032e43d949574103a5751cee93aec9de.shtml

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Acknowledgements

The authors thank in advance the Editor-in-Chief, Associate Editor, and anonymous referees for their time and efforts in handling and reviewing this paper.

Funding

This work was supported by the Soft Science Research Program of Sichuan Province (Grant No. 2021JDR0072), the Key Research Program of Xinjiang Uygur Autonomous (Grant No. 2022B01015), the National Natural Science Foundation of China (Grant Nos. 72171182, 71801175, 71871171, and 72031009), and the Chinese National Funding of Social Sciences, China (Grant No. 20&ZD058).

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Authors and Affiliations

  1. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, 611756, Sichuan, China

    Nengye Mu, Yuanshun Wang & Peiyuan Xin

  2. College of Transportation Engineering, Xinjiang University, Urumqi, 830046, Xinjiang, China

    Nengye Mu

  3. National and Local Joint Engineering Laboratory for Intelligent Integrated Transportation, Chengdu, 611756, Sichuan, China

    Nengye Mu

  4. National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Chengdu, 611756, Sichuan, China

    Nengye Mu

  5. School of Civil Engineering, Wuhan University, Wuhan, 430072, Hubei, China

    Zhen-Song Chen

  6. The Bartlett School of Sustainable Construction, University College London, London, WC1E 6BT, UK

    Muhammet Deveci

  7. Department of Industrial Engineering, Turkish Naval Academy, National Defence University, Tuzla, Istanbul, 34940, Turkey

    Muhammet Deveci

  8. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G 2R3, Canada

    Witold Pedrycz

  9. Systems Research Institute, Polish Academy of Sciences, 00-901, Warsaw, Poland

    Witold Pedrycz

  10. Department of Electrical and Computer Engineering Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Witold Pedrycz

  11. Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Sariyer, Istanbul, Turkey

    Witold Pedrycz

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  1. Nengye Mu
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  2. Yuanshun Wang
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  3. Zhen-Song Chen
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  4. Peiyuan Xin
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Contributions

Nengye Mu: conceptualization, methodology, software, data curation, funding acquisition, writing: original draft, writing: review and editing. Yuanshun Wang: resources, methodology, software, writing: original draft, writing: review and editing. Zhen-Song Chen: funding acquisition, verification, writing: review and editing. Peiyuan Xin: writing: review and editing. Muhammet Deveci: writing: review and editing. Witold Pedrycz: Writing: review and editing.

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Correspondence to Zhen-Song Chen.

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Mu, N., Wang, Y., Chen, ZS. et al. Multi-objective combinatorial optimization analysis of the recycling of retired new energy electric vehicle power batteries in a sustainable dynamic reverse logistics network. Environ Sci Pollut Res 30, 47580–47601 (2023). https://doi.org/10.1007/s11356-023-25573-w

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