On the Evaluation of a Drone-Based Delivery System on a Mixed Euclidean-Manhattan Grid

Abstract

In this work, we investigate the use of drones in a delivery scenario formed by two contiguous areas. In one area the drones can freely fly on straight lines between any two locations (Euclidean metric), while in the other one the drones must follow the open space above the roads (Manhattan metric). We model this delivery scenario as a Euclidean-Manhattan-Grid (EM-grid). Given a set of customers to be served in an EM-grid, the objective is to find the distribution point (DP) for the drone that minimizes the overall traveled distance, considering that the drone has to do multiple round trips to/from the DP. In our view, the DP is optimized with respect to the set of customers and its computation must be light because it needs to be recomputed every time the set of customers varies. Accordingly, we define the Single Distribution Point Problem (SDPP) and devise sub-optimal timeefficient algorithms for solving it. We numerically compare the cost of our sub-optimal solutions with that of an optimal solution computed with a brute-force approach. Finally, using the BlueSky open air simulator, we compare the cost of our best solution with the cost of a solution that serves the costumers from a fixed DP, like the location of a delivery company's depot. The fixed DP can perform very poorly for some customer instances, while our solution is highly adaptive and reduces the time and the distance covered by the drone.

How the Wind Can Be Leveraged for Saving Energy in a Truck-Drone Delivery System

“…H P (7) where i, j are the indices of the sectors given by Theorem 2, μ T i and μ L j are the energy consumption in these sectors S i and S j in Q 1 or Q 4 , and H P/sin(δ T i ) and H P/sin(δ L j ) are the lengths of the trajectories candidates for being T P and P L in S i and S j , respectively. Recall δ T and δ L are given by Eqs.…”

Drones Routing with Stochastic Demand

“…They focus on designing exact algorithms, while we can not propose an exact algorithm due to the uncertainty of the demands of customers. In [53], authors want to find the distribution point for drones in the scenario that drones must follow up the open space above the road while our scenario is that drones can freely fly on straight lines between customers. In [54], authors jointly optimize the route and sensing task to minimize the energy consumption of the drone and maximize sensing reward.…”