Advanced Vehicle Routing for Electric Fleets Using DPCGA: Addressing Charging and Traffic Constraints

Yuehan Zheng, Hao Chang, Peng Yu, Taofeng Ye and Ying Wang ()
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Yuehan Zheng: School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Hao Chang: School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Peng Yu: School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Taofeng Ye: School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Ying Wang: School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang 212100, China

Mathematics, 2025, vol. 13, issue 11, 1-27

Abstract: With the rapid proliferation of electric vehicles (EVs), urban logistics faces increasing challenges in optimizing vehicle routing. This paper presents a new modeling framework for the Electric Vehicle Routing Problem (EVRP), where multiple electric trucks serve a set of customers within their capacity limits. The model incorporates critical EV-specific constraints, including limited battery range, charging demand, and dynamic urban traffic conditions, with the objective of minimizing total delivery cost. To efficiently solve this problem, a Dual Population Cooperative Genetic Algorithm (DPCGA) is proposed. The algorithm employs a dual-population mechanism for global exploration, effectively expanding the search space and accelerating convergence. It then introduces local refinement operators to improve solution quality and enhance population diversity. A large number of experimental results demonstrate that DPCGA significantly outperforms traditional algorithms in terms of performance, achieving an average 3% improvement in customer satisfaction and a 15% reduction in computation time. Furthermore, this algorithm shows superior solution quality and robustness compared to the AVNS and ESA-VRPO algorithms, particularly in complex scenarios such as adjustments in charging station layouts and fluctuations in vehicle range. Sensitivity analysis further verifies the stability and practicality of DPCGA in real-world urban delivery environments.

Keywords: electric vehicles; dual-population genetic algorithm; multi-objective optimization; VRP; charging station optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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