Assignment of Medical Staff to Operating Rooms in Disaster Preparedness: A Novel Stochastic Approach

Abstract

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.