Unmanned aerial vehicles (UAVs) have significant practical advantages for delivering packages, and many logistics companies have begun deploying UAVs for commercial package deliveries. To deliver packages quickly and cost-effectively, the routes taken by UAVs from depots to customers must be optimized. This route optimization problem, a type of capacitated vehicle routing problem, has recently attracted considerable research interest. However, few papers have dealt with large-scale deliveries, where the number of customers exceed 1000. We present an innovative, practical package delivery model wherein multiple UAVs deliver multiple packages to customers who are compensated for late deliveries. Further, we propose an innovative methodology that combines a new plan-generation algorithm with a collective-learning heuristic to quickly determine cost-effective paths of UAVs even for large-scale deliveries up to 10000 customers. Specialized settings are applied to a collective-learning heuristic, the Iterative Economic Planning and Optimized Selections (I-EPOS) in order to coordinate collective actions of the UAVs. To demonstrate our methodology, we applied our highly flexible approach to a depot in Heathrow Airport, London. We show that a coordinated approach, in which the UAVs collectively determine their flight paths, leads to lower operational costs than an uncoordinated approach. Further, the coordinated approach enables large-scale package deliveries.

翻译：无人机在包裹配送方面具有显著的实用优势，许多物流公司已开始部署无人机进行商业包裹配送。为了快速且经济高效地配送包裹，必须优化无人机从仓库到客户的飞行路径。这一路径优化问题属于容量受限车辆路径问题的一种，近年来引起了广泛的研究兴趣。然而，很少有论文涉及客户数量超过1000的大规模配送。我们提出了一种创新且实用的包裹配送模型，其中多架无人机向客户配送多个包裹，并对延迟配送进行补偿。此外，我们提出了一种新颖的方法论，该方法将一种新的计划生成算法与集体学习启发式算法相结合，以快速确定无人机经济高效的路径，即使对于多达10000个客户的大规模配送也适用。我们将特定设置应用于集体学习启发式算法——迭代经济规划与优化选择（I-EPOS），以协调无人机的集体行动。为展示我们的方法论，我们将这种高度灵活的方法应用于伦敦希思罗机场的一个仓库。我们证明，与不协调的方法相比，无人机集体确定飞行路径的协调方法能降低运营成本。此外，协调方法能够实现大规模包裹配送。