A framework to managing disruption risk in rail-truck intermodal transportation networks

Rail-truck intermodal transportation plays a vital role in freight transportation in North America, and hence a crucial issue is to ensure continuity and minimize the adverse impacts from disruption. We propose a framework based on optimization and regression analysis for recovery from random disruptions of rail intermodal terminals. Two mixed-integer programming models are developed to simulate the normal and post-disruption operations, and the outputs are used in a predictive analytics model to identify the determinants of terminal criticality. The proposed framework is applied to a case study built using the realistic infrastructure of a rail-truck intermodal network in the United States. The resulting analyses underscore the importance of implementing the mitigation strategy at a marginal increase in pre-disruption cost, which in turn not only improves network resiliency but also results in significant cost savings by not using the more expensive recovery strategies. We further show that intermodal train service design resulting in a more balanced distribution of freight in the network could also help reduce terminal criticality, and that it makes sense to have backup rental capacities for some terminals.