交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (4): 65-78.DOI: 10.16097/j.cnki.1009-6744.2026.04.006

• 综合交通运输体系 • 上一篇    下一篇

不确定通关条件下跨境多式联运路径优化

杨扬*1,周斯加1,尹继娇2   

  1. 1. 昆明理工大学,交通工程学院,昆明 650500;2. 广西警察学院,交通管理工程学院,南宁 530029
  • 收稿日期:2026-03-18 修回日期:2026-05-04 接受日期:2026-05-06 出版日期:2026-08-25 发布日期:2026-08-21
  • 作者简介:杨扬(1974— ),男,江苏常州人,教授,博士
  • 基金资助:
    国家自然科学基金 (72264017)

Optimization of Cross-border Multimodal Transport Routes Under Uncertain Clearance Conditions

YANG Yang*1, ZHOU Sijia1, YIN Jijiao2   

  1. 1. Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China; 2. School of Traffic Management Engineering, Guangxi Police College, Nanning 530029, China
  • Received:2026-03-18 Revised:2026-05-04 Accepted:2026-05-06 Online:2026-08-25 Published:2026-08-21
  • Supported by:
    National Natural Science Foundation of China (72264017)

摘要: 国际运输框架下,跨境多式联运受到通关时间和关税征收幅度波动性的制约。本文引入三角模糊数刻画通关时间与关税税率的不确定性,以运输成本、运输时间和碳排放量为目标,构建多式联运路径优化模型,并基于模糊机会约束规划理论,将不确定性模型转化为易求解的混合整数规划模型;设计一种改进的多目标进化算法求解模型,减少优质解丢失,提升算法鲁棒性;最后,以重庆—新加坡覆盖8个国家15个节点的跨境运输为算例进行分析。计算结果表明:所提方法能够合理编制解决不确定跨境条件的多式联运路径优化方案集,与其他先进的多目标进化算法相比,其超体积指标提升13.21%,稳定性指标提升69.63%,本文所提方法性能提升效果显著,能够很好地为跨境多式联运相关承运主体提供优质的路径决策支持。

关键词: 综合交通运输, 多目标路径优化, 进化算法, 多式联运, 不确定性, 跨境运输

Abstract: Under the framework of international transportation, cross-border multimodal transport is restricted by customs clearance time and the volatility of tariff levy rates. This paper employs triangular fuzzy numbers to characterize the uncertainties inherent in customs clearance time and tariff rates. A path optimization model for multimodal transport is constructed with the objectives of minimizing transportation cost, transport time, and carbon emissions. Based on the fuzzy chance-constrained programming theory, the uncertain model is transformed into a tractable mixed-integer programming model. An improved multi-objective evolutionary algorithm is designed to solve the model, which mitigates the loss of high-quality solutions and enhances the robustness of the algorithm. Finally, a case study is conducted on cross-border transportation from Chongqing to Singapore, covering 15 nodes across 8 countries. The computational results demonstrate that the proposed method can reasonably generate a set of optimized multimodal transport schemes addressing uncertain cross-border conditions. Compared with other state-of-the-art multi-objective evolutionary algorithms, the hypervolume indicator is improved by 13.21% and the stability indicator by 69.63%, which indicates a significant performance enhancement of the proposed method. Therefore, it can effectively provide high-quality path decision support for relevant carriers engaged in cross-border multimodal transport.

Key words: integrated transportation, multi-objective route optimization, evolutionary algorithms, multimodal transportation; uncertainty, cross-border transportation

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