  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.
