A stochastic programming model for emergency supply planning considering transportation network mitigation and traffic congestion

Abstract

How to conduct effective and efficient emergency supply planning is a challenging task. In this paper, we tackle a general emergency supply planning problem. The problem not only integrates the decisions of transportation network mitigation and emergency supply pre-positioning before disasters, but also considers post-disaster dynamic transportation planning with traffic congestion effects incorporated. We formulate this problem as a two-stage stochastic programming model, which aims to minimize the expected total cost related to various disaster mitigation, preparedness, and response decisions. A variant of the model is optimally solved by applying a generalized Benders decomposition algorithm, which significantly outperforms state-of-the-art global optimization solvers. Finally, a case study for a hurricane threat in the southeastern U.S. is conducted to demonstrate the advantages of our model and to illustrate insights on the optimal network mitigation and pre-positioning plan as well as the transportation plan. It is shown that considering traffic congestion effects and dynamic transportation plans brings about spatial and temporal flexibility for achieving better emergency supply plans.