Scheduling Short-Term Recovery Activities to Maximize Transportation Network Resilience

Abstract

A stochastic, time-dependent integer program with recursive functions is proposed for the problem of assessing a rail-based freight transportation system’s resilience to disaster events. This work adds to this notion of resilience by explicitly considering that only limited resources will be available to support recovery activities, and their simultaneous implementation assumed in the prior work may not be possible. That is, the order in which recovery actions are taken can greatly affect gains achieved in capacity recovery over time. By developing an optimal schedule for a set of chosen recovery actions for each potential disaster scenario, the proposed model provides a more accurate depiction of the system’s resilience to disaster. Two solution methods are presented, both employing a decomposition approach that eliminates the need for recursive computations. The first is an exact decomposition with branch-and-cut methodology, and the second is a hybrid genetic algorithm that evaluates each chromosome’s fitness based on optimal objective values to the time-dependent maximum flow subproblem. Algorithm performance is assessed on a test network.