The Study on the impact of charging strategy on path planning under charging station queuing time

With the popularization of electric logistics vehicles and the advancement of green transformation in logistics industry, the Electric Vehicle Routing Problem with Time Windows (EVRPTW) has become a crucial issue for optimizing urban distribution efficiency and reducing operational costs. This study first establishes a multi-objective optimization model incorporating fixed costs, transportation costs, charging costs, and time window penalty costs. Compared with conventional VRP models, the proposed model introduces nonlinear charging constraints and threshold constraints for both full and partial charging strategies. Specifically, it examines how different charging strategies (full charging vs. partial charging) affect both the final distribution costs and the queuing waiting time of electric logistics vehicles at charging stations. A genetic simulated annealing algorithm is introduced to solve this optimization problem. The research systematically considers the interdependent relationships between charging strategy selection, charging station scheduling, and vehicle routing optimization under time window constraints.