  Aerial drones offer a distinct potential to reduce the delivery time and
energy consumption for the delivery of time-sensitive and small products.
However, there is still a need in the relevant industry to understand the
performance of drone-based delivery under different business needs and drone
operating conditions. We studied a drone deployment optimization problem for
direct delivery of goods to customers maintaining a specified time window. This
paper presents a new mathematical optimization-based decision-making
methodology to help business owners optimally route their drone fleet
minimizing the total energy consumption, required fleet size, and the required
number of additional batteries. A realistic feature of the optimization method
is 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 on
battery replacements accounting for the drone routing and the user-specified
minimum required battery energy. Numerical results based on real drone flight
tests and delivery data provide insights into the effect of different drone
operating parameters on the energy consumption, required fleet size, and the
required number of battery replacements. Results from a case study show that
the total energy consumption, required fleet size, and the required number of
battery replacements increase by 72.22%, 22.2%, and 200%, respectively, as the
drones fly over the road networks compared to flying in a straight path.
Additionally, results show that using a mixed fleet of hexacopter and
quadcopter drones reduces the total energy consumption by 48.52% compared to
using a homogeneous fleet of only hexacopters.
