The Rail–Road Intermodal Network Design for Sustainable Hazardous Material Transportation Under Node Disruption

Safe and efficient rail–road intermodal transportation of hazardous materials is threatened by uncertain node disruptions. A risk assessment model integrating the Gaussian plume model is developed to estimate casualty risks on arcs and at nodes. Treating node disruptions as stochastic train service delays, a multi-objective scenario-based robust optimization model is formulated to minimize the total risk, cost, and carbon emissions, and the model is solved via the augmented ε-constraint method according to the complexity of the proposed model. The key innovation lies in embedding node disruption uncertainties into a robust optimization framework for hazmat intermodal transportation and constructing a detailed risk assessment. A real-life problem in China validates the proposed model and approach. The results show that disruption scenarios cause significantly later delivery times compared to deterministic conditions, and the trade-offs among risk, cost, and carbon emissions shift notably. Sensitivity analyses indicate that disruption probability, disruption scale, and risk deviation weight coefficient substantially affect the optimal plan. Computational tests on different scales demonstrate the stability of the proposed approach. These findings offer practical guidance for railway authorities and hazmat carriers.