A Scenario-Based Robust Optimization Model for Flexibility-Enhanced Resilience in a Multi-Period Intermodal Hub Location Problem with Modular Capacities and Due Dates

Abstract Intermodal freight transport represents a critical strategy in the hub location problem (HLP) to reduce operational costs. Although the integration of container consolidation strategies and additional operational modules within intermodal networks presents novel operational challenges that increase vulnerability to disruptions, it has not been thoroughly examined in the literature. This study presents a new bi-objective mixed-integer linear programming (MILP) model to minimize both total network costs and tardiness/earliness in a capacitated multiple allocation hub location problem (CMAHLP). The proposed model incorporates a resilience budget to manage preparedness and recovery actions, thereby enhancing system flexibility. A promising robust optimization (RO) technique, along with the AUGMECON2 algorithm, is employed to handle possible disruption scenarios and solve the bi-objective mathematical formulation. A widely used Australian Post (AP) dataset, ranging from 10 to 50 nodes, is applied to validate the model and solution procedure. On AP 50-node instances, the AUGMECON2 algorithm achieved a 0.62% decrease in average network costs and a 0.5% reduction in tardiness/earliness compared to the epsilon-constraint method. The impacts of the coefficients of the RO model on both objective functions are analyzed. Finally, various scenarios are defined as part of a sensitivity analysis to evaluate the performance of the mathematical model and the proposed solution procedure.