乡村物流的“最后一千米”配送一直是制约物流效率与成本优化的关键所在。随着农村地区物流配送需求的快速增加，针对单纯利用卡车开展乡村物流的配送效率低、成本高等问题，首先提出了一种结合卡车与无人机的选址-配送一体化优化模型，旨在最大化覆盖需求点的同时，通过优化无人机配送中心选址、卡车路径规划和无人机任务分配来最小化配送总成本。然后，基于这种协同配送模式，采用了遗传算法结合免疫优化机制，形成多重嵌套式遗传算法来求解。最后，以景德镇市浮梁县为例，通过仿真实验验证了所提模型在乡村物流场景中的可行性，并提出了最佳的全局配送方案。进一步的灵敏度分析表明，模型能够有效适应不同无人机类型的参数设置，选取的三款市售物流无人机在实际运行中均表现出良好的收敛效果，收敛值显著优于传统假设条件。

The "last mile" distribution of rural logistics has always been considered the key to optimizing logistics efficiency and cost. With the rapid increase in the demand for logistics and distribution in rural areas, a site-distribution integration optimization model that combines trucks and drones was proposed, aiming to maximize the coverage of demand points while minimizing the total distribution cost by optimizing the site selection of drone distribution centers, truck route planning, and drone task allocation. Based on this collaborative distribution model, a genetic algorithm combined with an immune optimization mechanism was applied to form a multiple nested genetic algorithm for solving the problem. The feasibility of the proposed model in rural logistics scenarios was verified through simulation experiments, and an optimal global distribution scheme was identified by taking Fuliang County in Jingdezhen City as an example. Additionally, sensitivity analysis is conducted, demonstrating that the model is effectively adaptable to the parameter settings of different UAV types. The three selected commercially available logistics UAVs are observed to exhibit good convergence effects in actual operations, with convergence values significantly better than the traditional assumption conditions.