Optimization of Multimodal Transport Paths Considering a Low-Carbon Economy Under Uncertain Demand

Aiming at the uncertainty in cargo demand in the transportation process, the multimodal transportation path optimization problem is studied from the perspective of a low-carbon economy, and the robust optimization modeling method is introduced. Firstly, a robust optimization model for multimodal transportation is built using the multimodal transportation path optimization model under demand certainty, and the total transportation cost is then calculated by taking into account not just only the cost of transportation and trans-shipment but, additionally, the price of waiting because of schedule restrictions on trains and airplanes. Secondly, carbon emissions are added into the model as a constraint or cost by converting four different low-carbon policies. Then, the simulated annealing mechanism is introduced to improve the ACO algorithm. Finally, solomon calculus is used for the solution. The outcomes demonstrate that the improved annealing ant colony hybrid algorithm simulation can essentially improve the multimodal transportation path optimization problem with uncertain demand and promote multimodal transportation emission reduction. Among the four carbon emission policies, the mandatory carbon emission policy means are tough, and the greatest impact comes from reducing emissions and using less energy. Energy conservation and emission reduction have the second-best impact, while the three policy tools of carbon taxes, carbon trading and carbon payment are more modest.