The mixed truck-drone delivery systems have attracted increasing attention for last-mile logistics, but real-world complexities demand a shift from single-agent, fully connected graph models to multi-agent systems operating on actual road networks. We introduce the multi-agent flying sidekick traveling salesman problem (MA-FSTSP) on road networks, extending the single truck-drone model to multiple trucks, each carrying multiple drones while considering full road networks for truck restrictions and flexible drone routes. We propose a mixed-integer linear programming model and an efficient three-phase heuristic algorithm for this NP-hard problem. Our approach decomposes MA-FSTSP into manageable subproblems of one truck with multiple drones. Then, it computes the routes for trucks without drones in subproblems, which are used in the final phase as heuristics to help optimize drone and truck routes simultaneously. Extensive numerical experiments on Manhattan and Boston road networks demonstrate our algorithm's superior effectiveness and efficiency, significantly outperforming both column generation and variable neighborhood search baselines in solution quality and computation time. Notably, our approach scales to more than 300 customers within a 5-minute time limit, showcasing its potential for large-scale, real-world logistics applications.

翻译：混合卡车-无人机配送系统在最后一公里物流中日益受到关注，但现实世界的复杂性要求从单智能体、全连接图模型转向在实际道路网络上运行的多智能体系统。我们提出了道路网络上的多智能体飞行伙伴旅行商问题，将单卡车-无人机模型扩展为多卡车系统，每辆卡车携带多架无人机，同时考虑完整的道路网络以处理卡车限制和灵活的无人机路径。针对这一NP难问题，我们提出了一个混合整数线性规划模型和一个高效的三阶段启发式算法。我们的方法将MA-FSTSP分解为可管理的单卡车带多无人机子问题。随后，算法计算子问题中无无人机的卡车路径，这些路径在最终阶段作为启发式信息，用于同步优化无人机和卡车路径。在曼哈顿和波士顿道路网络上进行的大量数值实验表明，我们的算法在求解质量和计算时间上均显著优于列生成和变邻域搜索基线方法，展现出卓越的有效性和效率。值得注意的是，我们的方法能在5分钟时限内扩展到超过300个客户点，彰显了其在大规模现实物流应用中的潜力。