The development of green logistics puts forward new requirements for the distribution planning of complex transportation networks. The purpose of multimodal transportation path optimization considering carbon emissions is to find the optimal transport path for goods and the corresponding modes of transport in terms of transport, economy and environment. Aiming at the uncertainty of the transportation process, the transportation volume, inter-node transport time and node transit time are considered as random fuzzy variables. A bi-objective optimization model is established to minimize the total cost of multimodal transportation and carbon emissions. A hybrid intelligent algorithm model is designed which combines random fuzzy simulation with improved genetic algorithm. The case, designed based on the transportation logistics network, verifies the validity of the model, and analyzes the sensitivity of parameters. The optimization results have certain guiding significance for the decision-makers to choose the transportation plan.

Multimodal transportation; random fuzzy variables; carbon emissions