Scheduling UAV-Based Deliveries With an Approximation Algorithm

The need for environmentally conscious alternatives to traditional road transport is progressively growing as digital marketplaces continue to integrate more deeply into our daily lives. Employing drones for parcel delivery is a possible solution to mitigating road traffic and pollution. Nevertheless, designing an efficient drone delivery system comes with numerous challenges, including planning efficient scheduling of delivery tasks and coping with the strong impact that weather conditions have on drones’ mobility. We consider a drone delivery service based on a distributed infrastructure of service stations that a fleet of drones can use for battery replacement, allowing long-distance deliveries. We formulate the problem of scheduling the drone fleet’s operations so as to minimize the completion time of the delivery tasks and propose an approximation algorithm called RedMat. Our algorithm enables a straightforward extension to consider real-time demands and variable wind conditions, based on a thorough mathematical model, and that we simulate in our experiments. We simulate our solutions under numerous experimental settings, including dynamic scenarios with online delivery demand arrivals, and variable wind conditions, and compare the achieved performance against other existing solutions. Our experiments show the superior efficiency of our algorithm, which provides a remarkable improvement in the makespan (i.e., the maximum delivery time) and average delivery completion time, respectively up to 40% and up to 24.5% with respect to the state-of-the-art solutions.