A two-stage stochastic programming framework for evacuation planning in disaster responses

According to the information asymmetry, this paper focuses on a two-stage stochastic programming model to evacuate the affected people to safe areas during disaster response. A min-cost flow formulation is presented to portray the evacuation process of affected people in an urban transportation network. Nevertheless, it is difficult to predict the disaster magnitude and its impact on the urban road system, and thus travel time and capacity on the link are assumed to be scenario-based random variables. Then, this paper proposes a two-stage stochastic programming model to achieve the robust a priori evacuation plan. For solution convenience, the two-stage stochastic programming model is further deduced to a single stage formulation. Finally, a Lagrangian relaxation-based heuristic approach is designed to solve the proposed model. The experimental results indicate that the algorithm is able to solve the problem of interest efficiently and effectively.