## 5 Computational Experiments
### Sensitivity Analysis  
As an emerging energy replenishment infrastructure, BSI can effectively alleviate the replenishment pressure on CI, enabling vehicles to complete energy replenishment tasks under limited charging resources. However, the introduction of battery swapping also increases energy replenishment costs. Through comprehensive CI capacity sensitivity analysis, the HACO algorithm not only quantifies the impact of CI capacity variations on route optimization but also provides strategic insights for leveraging the synergistic advantages of HERI. The data in Tables 5 and 6 show that the average times of battery swapping are highest in the large-scale instance set C100, indirectly indicating that CI capacity constraints significantly influence vehicle energy replenishment choices. Therefore, this section conducts a sensitivity analysis on this instance set, exploring vehicle routing planning and cost composition under three scenarios: insufficient, moderate, and sufficient CIs. This analysis provides a theoretical foundation for optimizing electric vehicle energy replenishment strategies. Figure 12(a)-d illustrate the trends in the average charging and battery swapping costs, the average number of vehicles in operation, average distance costs, and average total costs, respectively, as the CI capacity varies. The number of CIs increases in increments of two within the range [4; 12]. Values of four and six represent insufficient CIs, while eight represents a moderate number, and 10 and 12 represent sufficient CIs. As shown in Figure 12(a), as the capacity of CI increases, the average charging cost rises, while the average battery swapping cost decreases significantly. Specifically, despite a 19.65% increase in charging costs, the 51.39% reduction in battery swapping costs results in a 4.78% decrease in total energy replenishment costs. This trend originates from our algorithm's design principle of adhering to current economic decision patterns in EV energy replenishment, specifically the charging-priority strategy. Both the dynamic rescheduling conflict mitigation in initial solution construction and the Change Infrastructure operator during local search aim to maximize limited charging resource utilization. Consequently, when HERIs are available, the CI with lower unit costs is prioritized. Figure 12(b) reveals that the average number of vehicles in operation decreases as the CI capacity transitions from \"insufficient\" to \"moderate\" and then stabilizes as the capacity further increases to \"sufficient\". Similarly, Figure 12(c) shows that the average distance cost follows the same trend, highlighting the coupling relationship between the CI capacity and the operational factors of electric logistics vehicle fleets. These results substantiate that the HACO algorithm's response to CI capacity fluctuations is not a simplistic unilateral adjustment (either increasing or decreasing specific costs), but rather an intelligent, self-adaptive mechanism that holistically optimizes fleet size, routing plans, and all cost components in a coordinated manner based on operational requirements. When the CI capacity is limited, increasing the number of operating vehicles becomes an alternative to meet customer distribution demands, alongside battery swapping. As the depot is distant from customer locations and energy replenishment infrastructures, additional vehicle routes inevitably raise distance costs. While the CI capacity expands to an appropriate level, existing vehicles can harness the synergistic benefits of HERI, completing distribution tasks after mid-route energy replenishment. This reduces the need for additional vehicles, thereby simultaneously decreasing both vehicle counts and travel distances. However, with further increases in CI availability, the algorithm, driven by a greedy attribute, prioritizes the more cost-effective but time-consuming charging method over battery swapping, as the energy replenishment cost is part of the objective function.