Sustainable Dynamic Scheduling Optimization of Shared Batteries in Urban Electric Bicycles: An Integer Programming Approach

Zongfeng Zou, Xin Yan, Pupu Liu (), Weihao Yang and Chao Zhang ()
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Zongfeng Zou: School of Management, Shanghai University, Shanghai 200444, China
Xin Yan: School of Cultural Heritage and Information Management, Shanghai University, Shanghai 200444, China
Pupu Liu: School of Management, Shanghai University, Shanghai 200444, China
Weihao Yang: School of Management, Shanghai University, Shanghai 200444, China
Chao Zhang: School of Management, Shanghai University, Shanghai 200444, China

Sustainability, 2025, vol. 17, issue 10, 1-21

Abstract: With the proliferation of electric bicycle battery swapping models, spatial supply demand imbalances of battery resources across swapping stations have become increasingly prominent. Existing studies predominantly focus on location optimization but struggle to address dynamic operational challenges in battery allocation efficiency. This paper proposes an integer programming (IP)-based dynamic scheduling optimization method for shared batteries, aiming to minimize transportation costs and balance battery distribution under multi-constraint conditions. A resource allocation model is constructed and solved via an interior-point method (IPM) combined with a branch-and-bound (B&B) strategy, optimizing the dispatch paths and quantities of fully charged batteries among stations. This study contributes to urban sustainability by enhancing resource utilization efficiency, reducing redundant production, and supporting low-carbon mobility infrastructure. Using the operational data from 729 battery swapping stations in Shanghai, the spatiotemporal heterogeneity of rider demand is analyzed to validate the model’s effectiveness. Results reveal that daily swapping demand in core commercial areas is 3–10 times higher than in peripheral regions. The optimal scheduling network exhibits a ‘centralized radial’ structure, with nearly 50% of batteries dispatched from low-demand peripheral stations to high-demand central zones, significantly reducing transportation costs and resource redundancy. This study shows that the proposed model effectively mitigates battery supply demand mismatches and enhances scheduling efficiency. Future research may incorporate real-time traffic data to refine cost functions and introduce temporal factors to improve model adaptability.

Keywords: sustainable urban mobility; shared battery; integer programming; resource allocation; transportation cost optimization; battery scheduling network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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