铁道运输与经济2025，Vol.47Issue(7)：23-33,11.DOI:10.16668/j.cnki.issn.1003-1421.2025.07.03
混合时间窗下考虑模糊需求的多式联运路径优化
Multimodal Transport Path Optimization Considering Fuzzy Demand under Mixed Time Window
摘要
Abstract
In multimodal transport,the uncertainty of freight demand and the existence of mixed time window constraints often become key factors leading to issues such as rising logistics costs and increased carbon emissions.From the perspective of multimodal transport carriers,trapezoidal fuzzy numbers were used to represent the uncertainty of demand,and a low-carbon multimodal transport path optimization model under carbon trading policies was constructed.At the same time,the fuzzy expected value method and fuzzy chance-constrained programming were introduced to defuzzify the model,and an improved adaptive simulated annealing genetic algorithm was designed to solve the model.Finally,taking a certain multimodal transport system as an example,the effectiveness of the model and algorithm was verified.The results indicate that the fuzzy expected value method and fuzzy chance-constrained programming theory can effectively address the problem of uncertain freight demand in the model and improve the accuracy and reliability of transport plans.Compared to traditional genetic algorithms and simulated annealing genetic algorithms,the improved adaptive simulated annealing genetic algorithm reduces the total cost by 48%and 12%,respectively,making it more suitable for solving this model.It ensures sufficient transport and transfer capacity at each node to effectively reduce the impact of uncertainty factors in actual transport.Reasonably setting carbon trading price coefficients is beneficial for carriers to choose environmentally friendly transport solutions.关键词
多式联运/模糊需求/混合时间窗/碳交易/遗传算法Key words
Multimodal Transport/Fuzzy Demand/Mixed Time Window/Carbon Trading/Genetic Algorithm分类
交通工程引用本文复制引用
胡永仕,吴浩杰,简文良,李泽浓..混合时间窗下考虑模糊需求的多式联运路径优化[J].铁道运输与经济,2025,47(7):23-33,11.基金项目
教育部人文社会科学基金项目(24YJA790014) （24YJA790014）
福建省自然科学基金项目(KY310420) （KY310420）
