a stochastic multi-objective model based on the classical optimal search model for searching for the people who are lost in response stage of earthquake

Although after an earthquake, the injured person should be equipped with food, shelter, and hygiene activities, before anything must be searched and rescued. however, disaster management (dm) has focused heavily on emergency logistics and developing an effective strategy for search operations has been largely ignored. in this study, we suggest a stochastic multi-objective optimization model to allocate resource and time for searching the individuals who are trapped in disaster regions. since in disaster conditions, the majority of information is uncertain, our model assumes ambiguity for the locations where the missing people may exist. fortunately, the suggested model fits nicely into the structure of the classical optimal search model as it uses a stochastic dynamic programming approach to solving this problem. on the other hand, through a computational experiment, we observed that the model needed heavy computation. therefore, we reformulated the suggested search model for a multi-criteria decision making (mcdm) problem and employed two efficient mcdm techniques, namely topsis and copras, to tackle the ranking problem. consequently, the computational effort significantly decreased and a promising solution was achieved.