Logistics for a fleet of drones for medical item delivery: A case study for Louisville, KY

Unmanned Aerial Vehicles, commonly referred to as drones, have been widely acknowledged as a promising technology for the delivery of medical and aid packages in humanitarian and healthcare logistics. In this study, we develop an optimization model to optimize the logistics for a fleet of drones for timely delivery of medical items (e.g., medicines, test kits, and vaccines) to hard-to-access locations (i.e., rural and suburban areas). We propose a novel timeslot formulation that schedules and sequences a set of trips to serve several demand locations. On each trip, a drone starts from an urban provider, visits one or more charging platforms (if required), serves a clinic in rural or suburban areas, and returns to the corresponding provider. The problem consists of selecting locations for charging stations, assigning clinics to providers, and scheduling and sequencing the trips such that the total completion time to serve all demand points is minimized. To improve the computational efficiency of the solution method, we use a preprocessing procedure to reduce the solution space by eliminating the dominated trips from the pool of trips. A set of numerical experiments is performed on simulated instances and on a case study in Louisville, KY. The results unveil interesting insights into the logistics of the proposed drone delivery system.