不同碳政策下考虑多重不确定的多式联运路径优化OA

In response to the uncertainties in demand,transport,and transshipment time caused by emergency replenishment and severe weather,triangular and trapezoidal fuzzy functions were adopted for characterization.Under the policies of carbon mandates,carbon taxes,carbon trading,and carbon offsets,carbon emission costs were quantified,and multi-objective models aiming to minimize the total cost and time were constructed,respectively.The uncertain models were processed based on fuzzy chance-constrained programming.In light of the characteristics of the models,a fuzzy controller was introduced,and an improved fuzzy adaptive non-dominated sorting genetic algorithm(FANSGA-Ⅱ)was designed for the solution.Case verification results show that the proposed FANSGA-Ⅱexhibits superior performance,and different carbon policies exert significant impacts on the optimal paths:Carbon mandates tend to prioritize cost optimization;carbon taxes favor time efficiency optimization,while carbon trading achieves the best balance among cost,efficiency,and emission reduction.This study provides support for low-carbon multimodal transport path optimization under uncertain environments and indicates that there is no single optimal carbon policy.Among these policies,the carbon trading mechanism,by virtue of its market-oriented incentive advantages,holds the greatest potential in promoting sustainable emission reduction in the industry and can offer references for policy formulation.