Disasters cause a huge number of injured patients in a short time while existing emergency facilities encountered devastation and cannot respond properly. Here, the importance of implementing temporary emergency management becomes clear. This study aims to locate some temporary emergency stations across the area by maximal covering after a disaster. Furthermore, a multi-mode fleet is used for transferring patients using different modes of transportation (e.g. helicopter ambulance and bus ambulance). Since the type of patients may change over periods, medical servers can displace among temporary emergency stations dynamically according to disaster severity. For this purpose, a new bi-objective dynamic location-helicopter ambulance allocation-ambulance routing model with multi-medical servers is presented. The first objective function minimizes the operational costs related to the newly designed Emergency Medical Service along with the rate of human loss. The second objective function minimizes the critical time spent before the medical treatment. To validate the developed model, the augmented ε-constraint method is used and applied for the Tehran city, which shows the applicability of the model. Finally, two meta-heuristic algorithms are customized for large-sized problems, and the related results are compared based on multi-objective algorithms’ performance comparison metrics to find the more efficient one.

灾难在短时间内造成大量受伤患者，而现有的紧急设施却遭受了破坏，无法做出适当反应。在这里，实施临时应急管理的重要性变得显而易见。这项研究旨在通过最大程度地覆盖灾后范围，在该地区找到一些临时应急站。此外，多模式车队用于使用不同的运输方式（例如，直升机救护车和公共汽车救护车）转移患者。由于患者的类型可能会随时间变化，因此医疗服务器可以根据灾难的严重程度在临时急救站之间动态移位。为此，提出了一种具有多服务器的双目标动态定位直升机救护车分配-救护车路由模型。第一个目标功能将与新设计的紧急医疗服务相关的运营成本以及人员伤亡率降到最低。第二个目标函数可以最大程度地减少医疗之前花费的关键时间。为了验证所开发的模型，使用了增强的ε约束方法并将其应用于德黑兰市，这证明了该模型的适用性。最后，针对大型问题定制了两种元启发式算法，并基于多目标算法的性能比较指标对相关结果进行了比较，以找到效率更高的算法。扩展的ε约束方法被应用于德黑兰市，证明了该模型的适用性。最后，针对大型问题定制了两种元启发式算法，并基于多目标算法的性能比较指标对相关结果进行了比较，以找到效率更高的算法。扩展的ε约束方法被应用于德黑兰市，证明了该模型的适用性。最后，针对大型问题定制了两种元启发式算法，并基于多目标算法的性能比较指标对相关结果进行了比较，以找到效率更高的算法。