Stochastic network models for logistics planning in disaster relief.

•We propose a new dynamic two-stage stochastic network flow model for disaster relief.•The stochastic mixed-integer program supports preparedness and response decisions.•We devise scenarios to account for the magnitude of any disaster.•We model risk aversion via minimax, semideviation, and Conditional Value-at-Risk approaches.•A two-phase heuristic is developed as an alternative for solving larger instances.