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Barzinpour and Esmaeili \cite{BarzinpourEsmaeili2014} study the mitigation of consequences of natural disasters on affected people. They argue the importance of location/allocation models in humanitarian supply chains that aim to provide quick relief items in the right time and the right place. In particular, they suggest a multi-objective mixed-integer linear program (MILP) solved on a grid representation of the city (using geographic information system (GIS)), which determines the location of distribution centers (DCs) and the assignment of DCs to the demand points. The authors suppose that the demand of each grid is a function of its population and its potential damage severity. To provide the necessary input for their model, they have employed a special-purposed software that assess the seismic vulnerability in cities. In their model, the authors consider various type of costs such as setup costs, holding costs, transportation costs, and shortage costs. They also consider three objective functions: 1) maximization of the cumulative coverage of population, 2) minimization of the total setup costs, and 3) minimization of the total transportation, holding, and shortage costs. To solve the problem, the authors have used the goal-programming approach. In particular, they have used expert judgments for the ideal values of the objective function. Furthermore to be able to tackle problem, the authors have assumed that the district can be divided into ten subregions, where the DC in each subregion will only serve within the subregion. This results in a significant reduction into the model size and allows the usage of standard optimization solvers. In particular, they report the result of their model for the district with 350,000 inhabitants, 10 subregions, and four types of relief products.   Liu and jiang \cite{LiuJiang2015} mentioned the importance of emergency logistics plan.   A post-disaster study is conducted by the authors on the delivering relief personnel (i.e., health personnel and aid workers) and evacuating critical population in mountainous areas. By considering uncertainty in affected area's demand of relief and critical population and transportation time between temporary facilities (TFs) and affected areas (AAs), they proposed a bi-objective robust optimization model, which determines the level of relief supplies at TFs and overall transportation plan between TFs and AAs. Due to mountainous nature of the region, the authors assume that TFs set up at the rim of the affected area and all the transportation will be done by helicopters. In their model, the authors comprised total weighted unsatisfied demand in the first objective and the sum of the total costs of mobilization of relief supplies, helicopter recruiting and transportation is included in the second objective. The authors handled lexicographic optimization technique to solve the problem, in which two sequential single objective model, with priority of the first objective, is solved. For evaluating their model they conducted a case study on data extracted from Great Sichuan earthquake.