  Out-of-hospital cardiac arrest (OHCA) claims over 400,000 lives each year in
North America and is one of the most time-sensitive medical emergencies.
Drone-delivered automated external defibrillators (AEDs) have the potential to
be a transformative innovation in the provision of emergency care for OHCA. In
this paper, we propose a simulation-optimization framework to minimize the
total number of drones required to meet a pre-specified response time goal,
while guaranteeing a sufficient number of drones are located at each base. To
do this, we develop a location-queuing model that is based on the p-median
architecture, where each base constitutes an explicit M/M/d queue, and that
incorporates estimated baseline response times to the demand points. We then
develop a reformulation technique that exploits the baseline response times,
allowing us to solve real-world instances to optimality using an off-the-shelf
solver. To test our model, we develop a two-stage machine learning approach to
simulate both the locations and baseline response times for future OHCAs. We
demonstrate the application of our framework using eight years of real data
from an area covering 26,000 square kilometres around Toronto, Canada. A modest
number of drones are required to significantly reduce response times in all
regions. Furthermore, an objective function focused on improving the 90th
percentile is well-suited for use in practice because the model reduces the
entire response time distribution, while providing equitable coverage in both
cities and rural areas. Overall, this paper provides a realistic framework that
can be leveraged by healthcare providers seeking to implement a drone network.
