考虑转运时间不确定性的多式联运多目标路径优化

doi:10.16097/j.cnki.1009-6744.2026.04.007

摘要/Abstract

摘要： 实际运输中，各节点不同运输方式的转运时间存在显著不确定性，这增加了多式联运路径优化的难度，也为承运人的决策带来挑战。因此，本文研究考虑转运时间不确定性的多式联运多目标路径优化问题，构建以运输费用、运输时间和碳排放最小为目标的多目标优化模型。为刻画转运时间的不确定性，对样本数据进行正态分布检验，采用拉丁超立方抽样和逆累积分布函数相结合的方法模拟不确定性转运时间。进一步地，针对传统NSGA-II（Non-dominated Sorting Genetic Algorithm II）算法在求解高维、多目标、多约束问题时易出现早熟收敛、种群多样性下降等情况，引入自适应交叉算子和自适应变异算子，根据种群适应度状态动态调整交叉概率与变异概率，以提高算法在不同迭代阶段的搜索灵活性，改善Pareto解集质量与收敛性能。最后，以黑龙江省对俄出口多式联运网络为算例进行仿真分析。结果表明，折中解对应运输费用为52 138.7元、运输时间为26.3 h、碳排放为4 964.8 kg；与标准NSGA-II算法相比，改进算法在运输费用和运输时间方面分别降低 1 690.5 元和 13.4 h。进一步地，相比于转运时间确定，不确定性条件下会倾向选择转运次数更少、运输时间更短的方案，但这会导致运输费用和碳排放分别增加8 705.5 元和1 675.7 kg。此外，佳木斯、富锦和同江是高频转运节点，铁-水转运是关键环节，重点优化上述节点及其转运操作，有助于提高多式联运整体运输效率。研究结果可为多式联运承运人的路径决策提供参考。

关键词: 综合交通运输, 路径优化, 第二代非支配排序遗传算法, 转运时间, 不确定性, 逆累积分布函数

Abstract: In practical transportation, the transshipment time associated with different transport mode conversions at each node exhibits significant uncertainty, which increases the complexity of multimodal transport path optimization and poses challenges to carriers' decision-making. This study investigates the multi-objective path optimization for multimodal transport considering uncertain transshipment times, and develops a multi-objective optimization model aiming to minimize transportation cost, transportation time, and carbon emissions. To characterize the uncertainty of transshipment time, sample data are first tested for normality, and uncertain transshipment times are simulated using a combination of Latin hypercube sampling and the inverse cumulative distribution function. Furthermore, to address the issues of premature convergence and loss of population diversity in the traditional NSGA-II algorithm when solving high-dimensional, the multi-objective, and multi-constraint problems, adaptive crossover and mutation operators are introduced to dynamically adjust the crossover and mutation probabilities based on the fitness status of the population. The search flexibility is improved at different evolutionary stages and the quality and convergence performance of the Pareto solution set is enhanced. A case study is conducted based on the multimodal transport network for exports from Heilongjiang Province to Russia. The results show that the compromise solution yields a transportation cost of 52 138.7 yuan, a transportation time of 26.3 hour, and carbon emissions of 4 964.8 kg. Compared with the standard NSGA- II algorithm, the improved algorithm reduces transportation cost and transportation time by 1 690.5 yuan and 13.4 hour, respectively.Moreover, compared with the deterministic transshipment time condition, the model under uncertainty tends to select solutions with fewer transshipments and shorter transportation times, albeit at the expense of increased transportation cost and carbon emissions by 8 705.5 yuan and 1 675.7 kg, respectively. In addition, Jiamusi, Fujin, and Tongjiang are identified as high-frequency transshipment nodes, and rail-to-water transshipment is a key operation. Prioritizing the optimization of these nodes and their transshipment operations can effectively improve the overall efficiency of multimodal transport. The findings provide references for path decision-making in multimodal transport.

Key words: integrated transportation, path optimization, second-generation non-dominated sorting genetic algorithm, transfer time; uncertainty, inverse cumulative distribution function

中图分类号:

U116.2

闫帅帅, 潘帅, 韩宝明. 考虑转运时间不确定性的多式联运多目标路径优化[J]. 交通运输系统工程与信息, 2026, 26(4): 79-88.

YAN Shuaishuai, PAN Shuai, HAN Baoming. Multi-objective Path Optimization for Multimodal Transport Considering Uncertainty in Transfer Time[J]. Journal of Transportation Systems Engineering and Information Technology, 2026, 26(4): 79-88.

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链接本文: http://www.tseit.org.cn/CN/10.16097/j.cnki.1009-6744.2026.04.007

http://www.tseit.org.cn/CN/Y2026/V26/I4/79

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