Improving post-disaster road network accessibility by strengthening links against failures

We study a network improvement problem to increase the resilience of a transportation network against disasters. This involves optimizing pre-disaster investment decisions to strengthen the links of the network structurally. The goal is to improve the expected post-disaster accessibility. We first propose a new dependency model for random link failures to predict the post-disaster status of the network. We show that the probability of any network realization can be computed using a Bayesian network representation of the dependency model. As the computational effort grows with the network size, we use our proposed dependency model in a network sampling algorithm. We then estimate an accessibility measure, namely, the expected weighted average distance between supply and demand points by checking pregenerated short and dissimilar paths in the sample. We minimize this measure and decide on the links that should be strengthened in a two-stage stochastic programming framework. As the failure probability of a strengthened link decreases, the discrete scenario probabilities depend on the first-stage decisions. To tackle this challenge, we develop an efficient tabu search algorithm. We apply our methods to a case study of Istanbul under the risk of an earthquake, both to illustrate the use of the methods and to derive insights for decision makers. (C) 2018 Elsevier B.V. All rights reserved.