Reliable Intermodal Freight Network Expansion with Demand Uncertainties and Network Disruptions

This paper develops a robust Mixed-Integer Linear Program (MILP) to assist railroad operators with intermodal network expansion decisions. Specifically, the objective of the model is to identify critical rail links to retrofit, locations to establish new terminals, and existing terminals to expand, where the intermodal freight network is subject to demand and supply uncertainties. Additional considerations by the model include a finite overall budget for investment, limited capacities on network links and at intermodal terminals, and time window constraints for shipments. A hybrid Genetic Algorithm (GA) is developed to solve the proposed MILP. It utilizes a column generation algorithm to solve the freight flow assignment problem and a multi-modal shortest path label-setting algorithm to solve the pricing sub-problems. An exact exhaustive enumeration method is used to validate the GA results. Experimental results indicate that the developed algorithm is capable of producing optimal solutions efficiently for small-sized intermodal freight networks. The impact of uncertainty on network configuration is discussed for a larger-sized case study.