Following a large-scale disaster in a highly populated city, one can expect a large number of injured people to need urgent operation. If it can be assumed that the city in question is likely to experience a disaster (although the time of its occurrence is unknown), pre-disaster measures should be taken to mitigate the consequences of the disaster. This article aims to minimize the fatality rate through the proper assignment of operating room personnel to hospitals, such that both the expected value of the functioning operating rooms and the expected value of the service level are maximized. Service level is defined as a variable that has a negative relationship to the distance a patient is expected to travel in order to receive the required medical services. The probability of survival, for both personnel and operating rooms, depends on several stochastic factors. A biobjective mixed integer nonlinear model is proposed for the problem. A simulated annealing algorithm, particle swarm and a genetic algorithm are developed to solve the model using a weighted metric method. The model is applied to a possible worst-case earthquake scenario in Tehran. The results show that the proposed approach significantly improves the performance of post-disaster emergency relief.

中文翻译：

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在备灾中将医务人员分配到手术室：一种新的随机方法

在人口稠密的城市发生大规模灾难之后，可以预期会有大量受伤人员需要紧急手术。如果可以假设所涉城市很可能遭受灾难（尽管发生的时间未知），则应采取灾前措施以减轻灾难的后果。本文旨在通过向医院适当分配手术室人员来最小化病死率，从而使手术室的功能期望值和服务水平的期望值最大化。服务水平被定义为一个变量，该变量与患者为获得所需的医疗服务而预计行驶的距离呈负相关。人员和手术室的生存概率，取决于几个随机因素。针对该问题提出了双目标混合整数非线性模型。开发了模拟退火算法、粒子群算法和遗传算法来使用加权度量方法求解模型。该模型应用于德黑兰可能发生的最坏地震情景。结果表明，所提出的方法显着提高了灾后应急救援的绩效。