عنوان مقاله [English]
Pre-hospital emergency is one of the most important issues of the health and treatment system of a country. Awareness of the incident, the dispatch of rescue teams to the place where the incident occurred, the conduct of primary medical care and the transfer of injured or wounded people to health centers in the shortest time and at a desirable quality level are among the most important tasks and objectives of a pre-hospital emergency system. The realization of these objectives requires an accurate estimate of the magnitude and type of the incidents, the proper deployment of emergency facilities, the availability of adequate equipment and specialized personnel at the emergency bases, comprehensive, centralized, and regional management, and reliable communication facilities among bases, rescue team, and health centers. It is obvious that the more the number of emergency bases and equipment is available in an area, the shorter the service time is achievable, and thus, a higher level of satisfaction is realized. However, some constraints and considerations exist. For example, the government budget for establishment of the bases is often limited; some of the equipments for the transfer of patients cannot be used due to urban and interurban transportation conditions, and so on. Thus, the determination of optimal location of the emergency bases, in such a way that the minimum service time would be achieved for all people is of special importance.
In this paper, a 0/1 nonlinear programming model is proposed for locating emergency bases with the goal of minimizing the mean and standard deviation of emergency service time for a given region while satisfying the relevant constraints. The simultaneous consideration of mean and standard deviation of service time, not only increases the number of successful missions, but also places this time for missions to an average time. This, in turn, can lead to relative fair justice for different people in that area. To test the validity and use of the proposed model, the model is applied for a part of the Tehran metropolis and its results are presented.