Intermodal Routing Problem for High-Value Goods Under Capacity Information Uncertainty and Carbon Tax Policy

The authors explored an intermodal routing problem for high-value goods, aiming to minimize the total costs, including those associated with transportation, storage, delays, damages, and carbon tax. To avoid transportation interruption when using the planned route in practice, they formulated the network capacity information uncertainty using triangular fuzzy numbers. A fuzzy nonlinear optimization model was built to address the problem. Under the goods owners' cautious attitude toward transportation organization, they used chance-constrained programming with a necessity measure to make the proposed model crisp and further conducted model linearization to enable the problem to be easily solved. A numerical case study shows that improving the confidence level to enhance the reliability increases the total costs of intermodal transportation, as well as further verifies that the carbon tax policy with a high carbon tax rate can achieve green intermodal transportation. It also indicates the advantages of considering intermodal transportation, damage costs, and a soft time window in routing high-value goods.