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.
翻译:紧急医疗系统(EMS) 提供关键的住院前护理和交通。 更快的紧急医疗系统反应时间提供更快的住院前护理,从而增加存活率。 我们通过解决两个阶段的随机性和强健线性程序,提供最佳救护车站住和路线决定,减少反应时间。 虽然对救护车系统的操作研究已经过去几十年,但在模拟中几乎没有开放源码和一致性。 我们开始通过出版OpenEMS, 与得克萨斯的奥斯汀-特拉维斯县紧急医疗系统(ATCEMS)合作, 公布OpenEMS, 以优化救护车战略决定的端到端管道。 它包括数据处理、优化和校准模拟。 我们希望这一开放源框架将促进与紧急医疗系统的未来研究。 最后, 我们提供一份关于德克萨斯奥斯汀市的详细案例研究。 我们发现, 最佳站点将增加反应时间88.02秒。 此外, 我们设计最佳战略, 以奥斯汀紧急医疗系统必须永久增加或从其车队中撤出一辆救护车。