Design of reverse logistics network for retired power batteries

Cenglin Yao; Jun Zhou

doi:10.1117/12.3009254

16 October 2023 Design of reverse logistics network for retired power batteries

Cenglin Yao, Jun Zhou

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128032T (2023) https://doi.org/10.1117/12.3009254
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

Battery electric vehicles are becoming more and more popular, and the number of power batteries scrapped is obviously increasing linearly. How to improve the utilization rate of retired power batteries has aroused extensive discussion among scholars from all walks of life. Based on the analysis of the retired power battery industry recycling chain, reverse logistics theory and reverse logistics network, this paper puts forward the reverse logistics recycling mode studied in this paper. According to the structure and layout characteristics of the retired power battery reverse logistics network, the location of each node in the reverse logistics network is systematically analyzed, and the mixed integer linear programming model is used to carry out the layout location and path planning of the logistics network under the retired power battery recycling mode. Finally, a specific numerical simulation case is selected and the model is analyzed and solved. Under the condition of sensitivity analysis of the objective function, the reverse logistics network optimization design scheme is obtained. This paper is very necessary to improve the utilization rate of used batteries, save resources, protect the environment and improve the academic system.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Cenglin Yao and Jun Zhou "Design of reverse logistics network for retired power batteries", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128032T (16 October 2023); https://doi.org/10.1117/12.3009254

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