In an emergency medical system, the locations of ambulance stations has a direct impact on response time. In this paper, two location models are presented in combination with the hypercube queuing model to maximize coverage probability. In the first model, the locations of free and busy ambulances are considered in the system states, and the hypercube model can be analyzed accurately. The model contains a large number of states, and cannot be used for large-sized problems. For this reason, the second model is presented with the same assumptions as in the first model, except that the locations of busy ambulances are not included in the system state, but approximated based on the arrival rates. Both models are offline and dynamic, in which an ambulance does not necessarily return to the station from which it has been dispatched. Two strategies are defined for returning ambulances to the stations from the customer’s location. In the first strategy, the ambulance is returned to the nearest station after completion of its mission, and in the second strategy, it returns to the empty station that covers the highest demand rate. For evaluation of the performance of the proposed models, small-sized examples are solved for both return strategies using the GAMS software. A simulation-optimization approach combined with a simulated annealing algorithm and a discrete-event simulation are used for solving large-sized problems. Moreover, real data from a case study are used to demonstrate the performance of the models in the real world.

在紧急医疗系统中，救护站的位置直接影响响应时间。在本文中，结合超立方体排队模型提出了两个位置模型，以最大化覆盖率。在第一个模型中，在系统状态下考虑了忙碌的救护车的位置，并且可以对超立方体模型进行准确的分析。该模型包含大量状态，不能用于大型问题。由于这个原因，第二个模型的假设与第一个模型相同，只是繁忙的救护车的位置不包括在系统状态中，而是根据到达率来近似。两种模型都是离线的和动态的，其中救护车并不一定要返回派出它的车站。定义了两种策略，用于将救护车从客户所在地带回车站。在第一种策略中，救护车在完成任务后返回到最近的车站，在第二种策略中，它返回到覆盖率最高的空站。为了评估所提出模型的性能，使用GAMS软件解决了两种退货策略的小例子。结合模拟退火算法和离散事件模拟的模拟优化方法用于解决大型问题。此外，来自案例研究的真实数据被用来证明模型在现实世界中的性能。救护车在完成任务后返回到最近的车站，在第二种策略中，它返回到覆盖率最高的空车站。为了评估所提出模型的性能，使用GAMS软件解决了两种退货策略的小例子。结合模拟退火算法和离散事件模拟的模拟优化方法用于解决大型问题。此外，来自案例研究的真实数据被用来证明模型在现实世界中的性能。救护车在完成任务后返回到最近的车站，在第二种策略中，它返回到覆盖率最高的空车站。为了评估所提出模型的性能，使用GAMS软件解决了两种退货策略的小例子。结合模拟退火算法和离散事件模拟的模拟优化方法用于解决大型问题。此外，来自案例研究的真实数据被用来证明模型在现实世界中的性能。结合模拟退火算法和离散事件模拟的模拟优化方法用于解决大型问题。此外，来自案例研究的真实数据被用来证明模型在现实世界中的性能。结合模拟退火算法和离散事件模拟的模拟优化方法用于解决大型问题。此外，来自案例研究的真实数据被用来证明模型在现实世界中的性能。