改进麻雀算法求解带模糊需求的低碳路径优化

目的 针对低碳背景下带模糊需求的低碳多式联运规划问题（Low-carbon Multimodal Transportation Planning Problem with Fuzzy Demand, LCMTPP-FD），以成本最小化构建数学模型。同时，结合现有的强制碳排放、碳税、碳交易和碳补偿等政策对LCMTPP-FD进行模型转换，研究不同低碳政策对物流成本和碳排放量的影响。方法 主要根据模型的特征，设计一种t分布麻雀搜索算法，对不同低碳政策下的模型进行求解，将迭代次数作为t分布的自由度来提高麻雀算法的性能。结果 将改进算法及多个模型应用于实际运输案例中，改进的麻雀算法能在较短时间内获得最优解，并且在强制碳排放下碳排放量最少为9 522.28，在碳交易和碳补偿政策下成本分别降低了11.41%、17.24%。结论 改进的麻雀搜索算法具有较好的收敛性和搜索能力。强制碳排放能有效地降低碳排放量，碳交易和碳补偿能有效降低企业成本，适合于低碳运输的推广。

Improved Sparrow Algorithm for Low-carbon Routing Optimization with Fuzzy Demand

For low-carbon multimodal transportation planning problem with fuzzy demand (LCMTPP-FD) under the low-carbon background, the work aims to construct a mathematical model to minimize the cost, and transform the LCMTPP-FD by combining existing policies, such as mandatory carbon emission, carbon tax, carbon trading and carbon offset, so as to study the impact of different low-carbon policies on logistics costs and carbon emissions. According to the characteristics of the model, a sparrow search algorithm with t distribution was designed to solve the model under different low-carbon policies, and the number of iterations was taken as the degree of freedom of t distribution to improve the performance of the sparrow algorithm. The improved algorithm and several models were applied to a real transportation case. The improved sparrow algorithm could obtain the optimal solution in a short time, and the minimum carbon emission under the mandatory carbon emission was 9 522.28. The costs under the carbon trading and carbon offset policies were reduced by 11.41% and 17.24%, respectively. The experimental results show that the improved sparrow search algorithm has high convergence and search ability. Moreover, mandatory carbon emission can effectively reduce carbon emissions. Carbon trading and carbon offset can reduce the total costs, which are suitable for the promotion phase of low-carbon transportation.