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Research on the Location Selection Problem of Electric Bicycle Battery Exchange Cabinets Based on an Improved Immune Algorithm

Author

Listed:
  • Zongfeng Zou

    (School of Management, Shanghai University, Shanghai 200444, China)

  • Weihao Yang

    (School of Management, Shanghai University, Shanghai 200444, China)

  • Shirley Ye Sheng

    (School of Business and Public Administration, Barry University, Miami Shores, FL 33161, USA)

  • Xin Yan

    (School of Cultural Heritage and Information Management, Shanghai University, Shanghai 200444, China)

Abstract

The rise of new energy technologies has accelerated progress towards sustainable development, and many companies are beginning to invest in renewable resource-related facilities. Electric bicycles have always been an important mode of green transportation; however, they also have problems such as slow charging, difficult charging, and that burning and short circuiting may occur during charging. Electric bicycle battery exchange cabinets effectively solve these problems by exchanging low batteries with full batteries instead of charging. However, current battery exchange cabinets face the problems of insufficient construction and unreasonable site selection. Therefore, this paper proposes a location selection model for electric bicycle battery exchange cabinets based on point demand theory, aiming to maximize rider satisfaction and the service capacity of exchange cabinets. The immune algorithm is introduced to solve the location model; however, the traditional immune algorithm has some problems such as poor stability and slow convergence. In this paper, the mutation process of the traditional immune algorithm is improved by introducing multi-point mutation, guided mutation, and local search. Finally, based on the data of electric bicycle riders in Shanghai, we verify that the location model based on point demand theory performs well on two objective functions of rider satisfaction and battery exchange cabinet service capability. We also expand the application of point demand theory to location models. Then, by conducting experiments with different parameter groups, through sensitivity analysis and convergence analysis, we verified that the improved immune algorithm performs better than the traditional immune algorithm in its accuracy, search accuracy, stability, and convergence.

Suggested Citation

  • Zongfeng Zou & Weihao Yang & Shirley Ye Sheng & Xin Yan, 2024. "Research on the Location Selection Problem of Electric Bicycle Battery Exchange Cabinets Based on an Improved Immune Algorithm," Sustainability, MDPI, vol. 16(19), pages 1-21, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:19:p:8394-:d:1486734
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    References listed on IDEAS

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