A hybrid robust optimization and simulation model to establish temporary emergency stations for earthquake relief

Earthquakes pose a constant threat to human communities. A key step in improving preparedness against such disasters is to determine the optimal location of Temporary Emergency Stations (TESs) and allocate them to affected areas. Decisions in the preparedness phase ensure optimal performance by TESs and minimize potential delays in rescue operations. During crises, TESs have a significant role in minimizing human causalities. In this research, a robust simulation-optimization approach is proposed to ensure appropriate planning in the preparedness phase. We develop a mathematical model for simultaneous and hierarchical location-allocation of the injured to the available medical facilities under disaster conditions. Since natural disasters are inherently unpredictable, the uncertainty of the data should inevitably be taken into account. We thus employ a Robust Optimization (RO) technique to tackle the uncertainty in the number of the injured and use simulation to create the first seven days of the crisis and determine the optimal capacities of medical facilities. The findings indicate that by eliminating the unnecessary transfer of mildly-injured victims to high-level medical facilities, the model causes a 15% reduction in treatment costs.