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Aerial drones offer a distinct potential to reduce the delivery time andenergy consumption for the delivery of time-sensitive and small products.However, there is still a need in the relevant industry to understand theperformance of drone-based delivery under different business needs and droneoperating conditions. We studied a drone deployment optimization problem fordirect delivery of goods to customers maintaining a specified time window. Thispaper presents a new mathematical optimization-based decision-makingmethodology to help business owners optimally route their drone fleetminimizing the total energy consumption, required fleet size, and the requirednumber of additional batteries. A realistic feature of the optimization methodis that instead of replacing the drone battery after each return to the depot,it keeps track of the remaining energy in the drone battery and decides onbattery replacements accounting for the drone routing and the user-specifiedminimum required battery energy. Numerical results based on real drone flighttests and delivery data provide insights into the effect of different droneoperating parameters on the energy consumption, required fleet size, and therequired number of battery replacements. Results from a case study show thatthe total energy consumption, required fleet size, and the required number ofbattery replacements increase by 72.22%, 22.2%, and 200%, respectively, as thedrones fly over the road networks compared to flying in a straight path.Additionally, results show that using a mixed fleet of hexacopter andquadcopter drones reduces the total energy consumption by 48.52% compared tousing a homogeneous fleet of only hexacopters.

Drone Deployment Optimization for Direct Delivery with Time Windows and Battery Replacements