## 6 Conclusions and Future Works  
This study addresses the electric vehicle routing problem with limited charging resources, aiming to minimize the total distribution cost of electric logistics fleets by leveraging the complementary advantages of heterogeneous energy replenishment infrastructures. An MIP model is developed, and a HACO algorithm incorporating a VNS mechanism is proposed. During the initial solution construction phase, HACO employs a dynamic rescheduling mechanism to proactively mitigate charging task conflicts induced by CI capacity limitations, thereby optimizing the utilization of constrained charging resources. The effectiveness and stability of HACO are validated through computational experiments on 40 instances of varying sizes by comparing its performance with the Gurobi solver, BR-GRASP, SA, GA, and GA-GSK. The comparative analysis of HACO results across large-scale instances demonstrates that CI capacity constraints fundamentally reshape cost structures by modulating the operational synergy between CI and BSI. Empirical data reveal that the charging costs via partial charging constitute merely 30-40% of full charging expenditures, yielding a 3-4% reduction in total costs. Sensitivity analysis further identifies moderate CI capacity as the optimal equilibrium point, effectively balancing energy replenishment, fixed vehicle, and distance costs. This study advances EVRP research by bridging a critical knowledge gap in modeling real-world operational constraints, specifically the simultaneous consideration of HERI and CI capacity limitations, while also yielding practical managerial implications for logistics optimization. First, the implementation of heterogeneous energy replenishment networks significantly enhances operational flexibility and efficiency. Although electric logistics vehicles with dual charging and battery swapping capabilities require higher initial investment, they substantially reduce long-term operational costs and improve distribution profitability. Therefore, fleet integration strategies should systematically evaluate cost-benefit trade-offs while maintaining service quality. Second, the partial charging strategy emerges as a cost-effective solution, particularly beneficial in high-demand scenarios or capacity-constrained areas, offering improved route flexibility and fleet efficiency through rapid energy replenishment. Third, sensitivity analysis underscores the significance of the CI capacity optimization in operational efficiency. Managers should strategically calibrate the CI capacity according to regional demand patterns, establishing an optimal operational range that simultaneously prevents resource underutilization and replenishment constraints while accommodating the heterogeneous energy replenishment demands of vehicle. Such optimization ensures maximal resource efficiency and economic returns. Finally, the findings provide valuable insights for policy development, suggesting that governments should promote standardized interface protocols for CI and BSI to facilitate the synergistic development of heterogeneous energy replenishment networks. This study assumes a fixed energy unit price with capacity constraints only for CI. In practice, electricity prices fluctuate throughout the day. Furthermore, the battery inventory at BSI is dynamic, involving both the restocking of fully charged batteries and the recharging of used ones. Future research could explore variable energy replenishment costs under time-of-use tariffs and the limited battery inventory at BSI. Additionally, with the emergence of mobile charging and battery swapping vehicles, which enable electric logistics vehiclesto select flexible locations for energy replenishment during transit, future studies could investigate the integration of mobile energy replenishment solutions to address practical EVRP optimization challenges. Lastly, this study's implications extend beyond logistics.