Developing a robust multi-objective model for pre/post disaster times under uncertainty in demand and resource

Studies show that by the course of time, the number of natural disasters such as earthquakes is increasing. Therefore, developing a model for locating distribution centers and relief goods distribution systems in disaster times, along with appropriately locating health centers with the ease of access for transferring the casualties and saving their lives, is among the most essential concerns in relief logistics. Considering these two subjects, simultaneously, results in an increase in the quality of service in disaster zones. In this study, a multi-objective programming model is developed for locating relief goods distribution centers and health centers along with distributing relief goods and transferring the casualties to health centers, with pre/post-disaster budget constraints for goods and casualties logistics. For a better modelling of the reality, the uncertainties in demand, supply, and cost parameters are included in the model. Also, facility failure (e.g. relief distribution centers, health centers, hospitals and supply points failure) due to earthquakes is considered. The proposed model maximizes the response level to medical needs of the casualties, while targeting the justly distribution of relief goods and minimizing the total costs of preparedness and response phases. In order to handle the uncertainties, the robust optimization approach is utilized. The model is solved with ε - constraint method. For the large sized form, the MOGASA algorithm is proposed, and the results are compared to those of the NSGAII algorithm. Then the validity and efficiency of the proposed algorithm is explored based on the results of both the proposed and exact methods.