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

Zongfeng Zou, Weihao Yang, Shirley Ye Sheng and Xin Yan ()
Additional contact information
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

Sustainability, 2024, vol. 16, issue 19, 1-21

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.

Keywords: location selection problem; point demand theory; immune algorithm; power change cabinet location; renewable resource-related facilities (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/19/8394/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/19/8394/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:19:p:8394-:d:1486734

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().