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

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.