  The drone-based last-mile delivery is an emerging technology designed to
automate the delivery process by utilizing drones loaded on a truck to
transport parcels to customers. In this paper, we study the next level of
drone-based last-mile delivery where autonomous vehicles (AVs) without drivers
are recruited to collaborate with drones to serve customers. We formulate the
problem of selecting AVs from a pool of available AVs and scheduling them to
serve customers to minimize the total cost as an Integer Linear Programming
(ILP). A novel greedy algorithm is proposed to solve the problem that
incorporates the real-world operational cost of AVs, traveling distances
calculated based on Google Map API, and varying load capacities of AVs.
Extensive simulations performed with numerous random delivery scenarios
demonstrate that both the optimal and greedy algorithms significantly increase
profits for the delivery company as well as the owners of AVs. Furthermore, the
results indicate that the greedy algorithm is highly effective with a
performance difference of only 2% compared with the optimal algorithm in terms
of the total amount of profits.
