  Emergency Medical Systems (EMS) provide crucial pre-hospital care and
transportation. Faster EMS response time provides quicker pre-hospital care and
thus increases survival rate. We reduce response time by providing optimal
ambulance stationing and routing decisions by solving two stage stochastic and
robust linear programs. Although operational research on ambulance systems is
decades old, there is little open-source code and consistency in simulations.
We begin to bridge this gap by publishing OpenEMS, in collaboration with the
Austin-Travis County EMS (ATCEMS) in Texas, an end-to-end pipeline to optimize
ambulance strategic decisions. It includes data handling, optimization, and a
calibrated simulation. We hope this open source framework will foster future
research with and for EMS. Finally, we provide a detailed case study on the
city of Austin, Texas. We find that optimal stationing would increase response
time by 88.02 seconds. Further, we design optimal strategies in the case where
Austin EMS must permanently add or remove one ambulance from their fleet.
