Improving Healthcare Systems' Performance during Mass Casualty Incidents using a Simulation, Optimization, and Machine Learning approach

洪水、地震、化学品泄漏或流行病等灾难事件通常在世界各地发生。此类事件在短时间内造成大量人员受伤。因此，对卫生服务的需求将会很高。在这些事件中，医疗系统产生的沉重工作量扰乱了医疗系统的功能，使得医院等医疗中心无法为患者/伤者提供适当的医疗和护理服务。这危及人类健康，甚至可能导致生命损失。在本研究项目中，我们以科学的方式认识这一问题，使用量化的方法来提高卫生中心的绩效。我们期待这项研究的结果将改善卫生服务的提供，防止灾害事件中的生命损失。

Disaster events such as floods, earthquakes, chemical spills, or epidemics usually take place all aroundthe world. Such events lead to the injury of a large number of people in a short amount of time. As aresult, there will be a high level of demand for health services. The heavy workload created in thehealthcare system during these incidents disrupts the function of the healthcare systems so that thehealth centers such as hospitals cannot provide medical treatment and care services to thepatients/injured people properly. This endangers human health and may even lead to the loss of humanlives. In this research project, we address this problem in a scientific way of knowing, using quantitativemethods to improve the performance of health centers. We expect the results of the study to improvethe delivery of health services and prevent loss of life during disaster incidents.