Optimizing Electric Vehicle Routing for Cross-Period Distribution of Multitype Scarce Resources under Hybrid Time Windows and Dynamic Priorities

This study investigates the problem of multitype scarce resource distribution using electric vehicles, incorporating practical factors including hybrid time windows, cross-period delivery, and priority-based allocation. We develop a mixed integer programming model to minimize the total distribution costs subject to limits on electric vehicle routing, hybrid time windows, priority, cross-period assignment, load capacity, and battery swapping. A tailored genetic algorithm is designed to solve the proposed model. Numerical experiments demonstrate the model's effectiveness and superiority over benchmark scenarios-including models without priority, demand splitting, battery swapping stations, and region partitioning, as well as intraperiod and soft time windows-in terms of both the distribution cost and demand satisfaction rate. To further balance service fairness across all customers, we introduce an adaptive dynamic priority mechanism based on the cumulative demand satisfaction rate and service start time, and propose a epsilon-adaptive dynamic priority-based multitype scarce resource distribution model with cross-period hybrid time windows. The results show that the enhanced model significantly improves equity performance while maintaining operational efficiency.