针对农村物流长期存在的配送效率低、运营成本高等系统性难题，提出了一种货车、公交与无人机的联合配送模式。首先，在考虑客户的需求属性差异以及各种配送工具的协同机理基础上，以最小化综合配送成本为目标构建了混合整数规划模型，目标涵盖货车运输和发车、无人机运输和起降、公交网络共享成本等关键要素。然后，设计自适应大邻域搜索算法对其求解，结合问题特点提出了混合车型协同优化的“破坏-修复”组合邻域结构。之后，通过算例仿真实验，对比CPLEX求解器和算法的求解结果，结果显示，模型能有效优化货车、公交和无人机的联合配送路径，证明算法具有良好的稳定性和精确性。最后，以浙江省威坪镇为实际案例开展研究，结果显示：货车、公交、无人机三网联合配送模式下，农村公交2趟班次有效覆盖5个偏远村级物流节点，无人机的部署满足了高时效需求，较传统货车配送节约49.7%的运营成本；相较于“货车+公交”“货车+无人机”双模式协同，分别实现成本节约7.6%和41.9%。采用“货车-公交-无人机”联合配送路径规划，为破解农村物流“最后一公里”困境提供了创新解决方案，也为多模式的物流网络规划提供了参考。

To address the persistent challenges of low delivery efficiency and high operating costs in rural logistics, a collaborative delivery model integrating trucks, buses, and drones was proposed. First, a mixed-integer programming model was formulated to minimize total delivery costs, incorporating key components such as truck transportation and dispatching expenses, drone transportation and takeoff/landing costs, and bus network sharing fees. This model was based on considering the differences in customer demand attributes and the collaborative mechanisms among various delivery vehicles. Next, an adaptive large neighborhood search algorithm was designed to solve the model, introducing a destruction-repair combined neighborhood structure optimized for multi-vehicle coordination. Instance simulation results demonstrated that the proposed model effectively optimized joint delivery routes for trucks, buses, and drones, with the algorithm exhibiting superior stability and accuracy compared to CPLEX solver results. Further validation through a real-world case study in Weiping Town, Zhejiang Province, revealed that the integrated three-network delivery model achieved a 49.7% reduction in operating costs compared to traditional truck-only delivery. The model also outperformed "truck+bus" and "truck+drone" dual-mode collaboration by 7.6% and 41.9% reduction in operating costs, respectively. Specifically, two scheduled rural bus routes efficiently covered five remote village-level logistics nodes, while the deployment of drones met the time-sensitive demands. The proposed "truck-bus-drone" collaborative path planning framework not only provides an innovative solution to the "last-mile" dilemma in rural logistics but also offers methodological insights for multi-modal logistics network optimization.