Drone flight scheduling under uncertainty on battery duration and air temperature

Commercial drones are expected to be widely used in the near future. They are generally powered by batteries to fly aerial areas. Flying time performance is known to change depending on air temperature. Hence, this paper proposes a robust optimization approach to find the optimal flight schedule (i.e., the number of drones and flight paths) in the flight network considering uncertain battery duration. A regression model is first developed to estimate battery duration as a function of air temperature. Three flight duration uncertainty sets (polyhedral, box, and ellipsoidal) are explored based on the regression model, and the robust optimization model is solved for each of the three sets. A decision tool is developed to analyze performance of the sets, and to help a decision maker select an appropriate uncertainty set that is most appropriate for a specific application of interest. The tool considers both minimizing the total operating cost and minimizing the probability of not completing the scheduled flights. Numerical results are presented to illustrate the proposed method.