OpenEMS: an open-source Package for Two-Stage Stochastic and Robust Optimization for Ambulance Location and Routing with Applications to Austin-Travis County EMS Data

TL;DR

OpenEMS is an open-source framework that optimizes ambulance stationing and routing using stochastic and robust linear programming, demonstrated through a detailed case study in Austin, Texas, aiming to improve EMS response times.

Contribution

This paper introduces OpenEMS, the first comprehensive open-source pipeline for ambulance location and routing optimization, integrating data handling, optimization, and simulation.

Findings

Optimal stationing increases response efficiency by 88 seconds.

Designed strategies for fleet size adjustments.

Provides an end-to-end open-source EMS optimization tool.

Abstract

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.