Optimization of multimodal transport path considering carbon policy

The study of multimodal transport path optimization considering carbon policy is of great theoretical and practical significance in the situation of high-quality development. By establishing a multi-objective multimodal transportation path optimization base model considering transportation cost, time cost and carbon emission cost, and a model considering three carbon policies: mandatory carbon emission, carbon tax and carbon trading, and designing an improved simulated annealing genetic algorithm to analyze the cases to compare the multimodal transportation solutions and costs under different carbon policies. The results show that: 1) the improved simulated annealing genetic algorithm can achieve the lowest cost and lowest carbon emission than the traditional genetic algorithm in terms of time search and effect search and can obtain more appropriate transportation solutions for intermodal transport operators. 2) The comparison of carbon policies by example shows that the mandatory carbon emission policy has a strong constraint effect, while the carbon tax and carbon trading have relatively loose constraints. 3) The choice of different carbon policies can achieve the goal of controlling carbon emissions and reducing total costs. The model and algorithm proposed in this paper can provide a theoretical basis for administrative departments and logistics service providers to optimize transportation solutions.