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\section{Optimal team deployment in urban search and rescue}  Chen and Miller-Hooks \cite{Chen&Miller-Hooks2012} proposed a multi stage stochastic program for the problem of Urban Search and Rescue Team Deployment Problem (USAR-TDP), where search and rescue team are deployed to disaster areas in post-disaster phase and sites to serve and order in which they are visited are determined.   proposed a scenario based, Multi-stage stochastic program with column generation for the Urban Search and Rescue Team Deployment Problem USAR-TDP, in order to get the optimal search and rescue team deployment. Having the maximization of the expected number of saved lives as their objective, they could establish the routes and deployment of search teams, considering that the number of teams varies with period, the demand depends on diminishing likelihood of survival and building damage, and newly devastated sites are discovered and enter the model following a Poisson process.    The problem of optimally deploying urban search and rescue (USAR) teams to disaster sites in post-disaster circumstances is formulated as a multistage stochastic program (MSP). A portion of sites requiring assistance arrive dynamically over the decision horizon and key problem characteristics are known only with uncertainty a priori. The problem seeks to identify a set of tours for USAR teams so as to maximize the total expected number of people that can be saved by attending to all or a subset of disaster sites within the disaster region. Decisions are taken dynamically over the decision horizon as situational awareness improves and survival likelihood diminishes with the aim of increasing the expected number of saved lives. To overcome the expensive computational effort associated with solving a MSP, a column generation-based strategy that consists of solving a series of interrelated