Modeling Location Allocation Problem in Earthquake crisis Situation and Solving by Metaheuristic Algorithm

In this study, a location-allocation problem is proposed regarding capacity factor in critical situation of an earthquake. Output is the selection of the best places for temporary shelters and optimized arrangement of the casualties in those places somehow minimizing casualties and damages. In the following, efficient allocation of the casualties to the medical centers will be discussed. Reaching these goals, a mathematics model proportionate to the problem conditions and constraints is presented. In literatures, location-allocation problem has been classified as a NP-Hard Problem. For these problems, metaheuristic algorithm were proposed. In this research, Imperialist Compeitive Algorithm (ICA) and Genetic Algorithm (GA) are used and the results comprised with each other. Based on the results of research, in such cases, ICA can be an opponent for Genetic Algorithm, because of the average of the solution obtained by this algorithm is rather better than Genetic Algorithm. However the GA convergence is faster than ICA. Case study is performed on region 3 of Tehran. Using available information of this region, the most fitted places for sheltering are extracted from GIS science and ARC GIS software. ICA is implemented to solve the problem. At the end, the number of optimized shelters and arrangement of inhabitants in these places and also arrangement of casualties to available medical centers in the region are presented.