An integrated model proposal and an application for the determination of temporary shelter areas to be opened after the disaster and their allocation to the disaster victims

As a result of destructions or damage to houses due to disasters, the predetermination of temporary shelters and planning their use in order to meet the sheltering needs of people affected by the disaster is a part of disaster management. Accordingly, after a disaster, it is very important to quickly place the victims of the disaster in the designated temporary accommodation areas in order to ensure the transition to normal life. In this study, an integrated model is proposed that allows to determine which temporary shelters will be opened for use after a possible disaster, and which temporary shelters will be assigned to the use of the disaster victims. It is a two-stage model consisting of the solution of the Goal Programming model that will enable the survivors to be assigned to temporary shelter areas afterwards. In the first stage, temporary accommodation areas are evaluated with the determined criteria with the Analytical Network Process and the evaluation scores of the relevant areas are created. In the second stage, the target values for the purposes of opening the lowest number of temporary shelters with the highest score are determined, and the victims are assigned to the most suitable temporary shelter areas with a Goal Programming model that also takes into account the distance constraint. In the study, the data related to the city center of Eskişehir were used, and different samples were generated according to the number of population affected by the disaster on the basis of the neighborhood and the distances, and the model was tested. 40 samples were generated with 10 different degree of exposure sets and 4 different distance values for the furthest temporary accommodation areas to which neighborhoods can be assigned. With the proposed model, it has been observed that the most appropriate assignments with the highest capacity utilization rates are made to the lowest number of areas with the highest score.