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Unmanned aerial vehicles (UAVs) have significant practical advantages fordelivering packages, and many logistics companies have begun deploying UAVs forcommercial package deliveries. To deliver packages quickly andcost-effectively, the routes taken by UAVs from depots to customers must beoptimized. This route optimization problem, a type of capacitated vehiclerouting problem, has recently attracted considerable research interest.However, few papers have dealt with large-scale deliveries, where the number ofcustomers exceed 1000. We present an innovative, practical package deliverymodel wherein multiple UAVs deliver multiple packages to customers who arecompensated for late deliveries. Further, we propose an innovative methodologythat combines a new plan-generation algorithm with a collective-learningheuristic to quickly determine cost-effective paths of UAVs even forlarge-scale deliveries up to 10000 customers. Specialized settings are appliedto a collective-learning heuristic, the Iterative Economic Planning andOptimized Selections (I-EPOS) in order to coordinate collective actions of theUAVs. To demonstrate our methodology, we applied our highly flexible approachto a depot in Heathrow Airport, London. We show that a coordinated approach, inwhich the UAVs collectively determine their flight paths, leads to loweroperational costs than an uncoordinated approach. Further, the coordinatedapproach enables large-scale package deliveries.

Large-scale Package Deliveries with Unmanned Aerial Vehicles using Collective Learning