Large-Scale Package Deliveries With Unmanned Aerial Vehicles Using Collective Learning

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. 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 a novel 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 10,000 customers. To demonstrate our methodology, we applied our highly flexible approach to a depot in Heathrow Airport, London. We show that our coordinated approach, in which the UAVs collectively determine their flight paths, leads to similar operational costs as those of the ant colony optimization technique and outperforms it for large-scale package deliveries.