Emergency medical service (EMS) systems have two main goals when sending ambulances to patients: rapidly responding to patients and sending the right type of personnel to patients based on their health needs. We address these issues by formulating and studying a Markov decision process model that determines which type of ambulances (servers) to send to patients in real-time. The base model considers a loss system over a finite time horizon, and we provide a model variant that considers an infinite time horizon and the average reward criterion. Structural properties of the optimal policies are derived. Computational experiments using a real-world EMS dataset show that the optimal policies inform how to dynamically dispatch ambulance types to patients. We propose and evaluate three classes of heuristics, including a static constant threshold heuristic, a greedy heuristic, and a dynamic greedy threshold heuristic. Computational results suggest that the greedy threshold heuristic closely approximates the optimal policies and reduces the complexity of implementing dynamic policies in real settings.

紧急医疗服务 (EMS) 系统在向患者派遣救护车时有两个主要目标：快速响应患者和根据患者的健康需求向患者派遣合适类型的人员。我们通过制定和研究马尔可夫决策过程模型来解决这些问题，该模型确定将哪种类型的救护车（服务器）实时发送给患者。基本模型考虑了有限时间范围内的损失系统，我们提供了一个考虑无限时间范围和平均奖励标准的模型变体。导出最优策略的结构特性。使用真实世界 EMS 数据集的计算实验表明，最佳策略告知如何向患者动态调度救护车类型。我们提出并评估了三类启发式方法，包括静态恒定阈值启发式、贪婪启发式和动态贪婪阈值启发式。计算结果表明，贪婪阈值启发式算法非常接近最优策略，并降低了在实际环境中实施动态策略的复杂性。