  This article analyzes two classes of job selection policies that control how
a network of autonomous aerial vehicles delivers goods from depots to
customers. Customer requests (jobs) occur according to a spatio-temporal
stochastic process not known by the system. If job selection uses a policy in
which the first job (FJ) is served first, the system may collapse to
instability by removing just one vehicle. Policies that serve the nearest job
(NJ) first show such threshold behavior only in some settings and can be
implemented in a distributed manner. The timing of job selection has
significant impact on delivery time and stability for NJ while it has no impact
for FJ. Based on these findings we introduce a methodological approach for
decision-making support to set up and operate such a system, taking into
account the trade-off between monetary cost and service quality. In particular,
we compute a lower bound for the infrastructure expenditure required to achieve
a certain expected delivery time. The approach includes three time horizons:
long-term decisions on the number of depots to deploy in the service area,
mid-term decisions on the number of vehicles to use, and short-term decisions
on the policy to operate the vehicles.
