中转时间不确定下冷藏集装箱多式联运路径优化研究

doi:10.16097/j.cnki.1009-6744.2025.05.011

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (5): 124-134.DOI: 10.16097/j.cnki.1009-6744.2025.05.011

• 综合交通运输体系论坛 • 上一篇下一篇

中转时间不确定下冷藏集装箱多式联运路径优化研究

王彦^*1，王则恺¹，宋美霞²，方力萱¹

1. 大连海事大学，交通运输工程学院，辽宁大连116026；2.大连科技学院，交通与电气工程学院，辽宁大连116052

收稿日期:2025-05-16 修回日期:2025-06-26 接受日期:2025-07-07 出版日期:2025-10-25 发布日期:2025-10-25
作者简介:王彦(1982—)，男，吉林梨树人，讲师。
基金资助:
辽宁省社会科学规划基金一般项目(L22BGL011)。

Path Optimization of Reefer Container Intermodal Transportation Under Transfer Time Uncertainty

WANG Yan^*1, WANG Zekai¹, SONG Meixia², FANG Lixuan¹

1. College of Transportation Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China; 2. College of Transportation and Electrical Engineering, Dalian Institute of Science and Technology, Dalian 116052, Liaoning, China

Received:2025-05-16 Revised:2025-06-26 Accepted:2025-07-07 Online:2025-10-25 Published:2025-10-25
Supported by:
Social Science Foundation of Liaoning Province(General Program)(L22BGL011)。

摘要/Abstract

摘要： 伴随冷链物流市场快速发展与冷藏集装箱多式联运重要性提升，针对冷链物流中冷藏集装箱多式联运的中转时间不确定性问题，本文构建一个以运输总成本最小化和时间效率比最大化为目标的多目标路径优化模型。采用三角模糊数表征中转时间不确定性，通过机会约束规划实现模糊模型向确定性模型的转化。此外，本文设计了一种自适应交叉与变异概率的改进非支配排序遗传算法(NSGA-II)对模型进行求解，并与传统的NSGA-II算法进行对比。结果表明：本文模型能够有效降低运输成本，提高运输时效性，改进的NSGA-II算法在解集规模和收敛速度方面表现出显著优势，较传统算法分别提升16.2%和21.7%。进一步对铁路运价进行灵敏度分析显示：当铁路运价降低至40%时，运输方式全部转为铁路运输，此时运输总成本降低19.8%，时间效率比提升49.0%。本文为冷链物流企业提供了科学的路径选择决策支持，有助于应对中转时间不确定性挑战，优化冷藏集装箱多式联运路径。

关键词: 综合运输, 路径优化, 改进的第二代非支配排序遗传算法, 冷藏货物, 中转时间不确定

Abstract: Accompanied by the rapid development of cold chain logistics market and the increase in the importance of intermodal transport of reefer containers, a multi-objective path optimization model is constructed with the objectives of minimizing the total transportation cost and maximizing the ratio of time efficiency, which aims at the uncertainty problem of transit time during the intermodal transport of reefer containers in cold chain logistics. A triangular fuzzy number is used to characterize the uncertainty of transit time, and then the transformation of a fuzzy model to a deterministic model is realized through the opportunity constrained planning. In addition, an improved NSGA-II algorithm with adaptive crossover and variance probabilities is designed to solve the model and further compared with the traditional NSGA-II algorithm. The results show that the constructed model can effectively reduce the transportation cost and improve the transportation timeliness. The improved NSGA-II algorithm shows significant advantages in terms of solution set size and convergence speed, which are 16.2% and 21.7% higher than those of the traditional algorithm, respectively. Further the sensitivity analysis of railroad tariffs shows that when the railroad tariffs are reduced to 40%, all transportation modes would switch to railroad transportation. The total cost of transportation would be reduced by 19.8% and the time efficiency ratio is improved by 49.0% at that time. This study provides scientific decision support for cold chain logistics enterprises to choose the path, which helps to meet the challenge of transit time uncertainty and optimizes the intermodal transport path of reefer containers.

Key words: integrated transportation, path optimization, improved second-generation non-dominated sorting genetic algorithm; refrigerated cargo, transfer time uncertainty

中图分类号:

U116.2

王彦, 王则恺, 宋美霞, 方力萱. 中转时间不确定下冷藏集装箱多式联运路径优化研究[J]. 交通运输系统工程与信息, 2025, 25(5): 124-134.

WANG Yan, WANG Zekai, SONG Meixia, FANG Lixuan. Path Optimization of Reefer Container Intermodal Transportation Under Transfer Time Uncertainty[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(5): 124-134.

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参考文献

[1] 刘松, 邵毅明, 彭勇. 转运限制下的冷藏集装箱多式联运路径优化[J]. 计算机应用与软件, 2020, 37(7): 37-42. [LIU S, SHAO Y M, PENG Y. Optimization of refrigerated container multimodal transportation path under transfer constraints[J]. Computer Applications and Software, 2020, 37(7): 37-42.]

[2] MENG X, SUN J, WANG H. Cost analysis of multimodal transport on refrigerated container[J]. IOP Conference Series: Earth and Environmental Science, 2021, 791(1): 012078.

[3] 杨华龙, 于果. 碳交易价格不确定下冷链多式联运路径优化[J]. 交通运输系统工程与信息, 2024, 24(6): 5-14. [YANG H L, YU G. Cold chain multimodal transportation path optimization under uncertain carbon trading prices[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(6): 5-14.]

[4] 蒋琦玮, 林艺, 冯芳玲. 模糊时间下考虑碳税值变化的多式联运路径优化问题研究[J]. 工业技术经济, 2020, 39(4): 81-88. [JIANG Q W, LIN Y, FENG F L. Research on multimodal transportation path optimization considering carbon tax variation under fuzzy time[J]. Journal of Industrial Technological Economics, 2020, 39(4): 81-88.]

[5] 尹传忠,方颢蓉,武中凯,等.随机机会约束的多式联运多目标优化[J]. 铁道科学与工程学报, 2021, 18(6): 1595-1603. [YIN C Z, FANG H R, WU Z K, et al. Multi-objective optimization of multimodal transportation with stochastic chance constraints[J]. Journal of Railway Science and Engineering, 2021, 18(6): 1595-1603.]

[6] 邓明君,代玉珍,李响.需求不确定下低碳多式联运路径鲁棒优化[J]. 工业工程, 2023, 26(4): 104-113. [DENG M J, DAI Y Z, LI X. Robust optimization of low-carbon multimodal transportation path under demand uncertainty[J]. Industrial Engineering Journal, 2023, 26(4): 104-113.]

[7] 邱叶,胡军红,郭缙烨,等.不确定需求下考虑货物时间价值的多式联运路径选择方法[J]. 铁道科学与工程学报, 2024, 21(3): 994-1003. [QIU Y, HU J H, CUO J Y, et al. Multimodal transportation route selection method considering cargo time value under uncertain demand[J]. Journal of Railway Science and Engineering, 2024, 21(3): 994-1003.]

[8] ABBASSI A, ALAOUI A E H, BOUKACHOUR J. Robust optimisation of the intermodal freight transport problem: Modeling and solving with an efficient hybrid approach [J]. Journal of Computational Science, 2019, 30: 127-142.

[9] 刘倚玮,赵章荣.考虑碳排放的多式联运路径优化算法比较与分析[J]. 工业工程与管理, 2022, 27(5): 53-59. [LIU Y W, ZHAO Z R. Comparison and analysis of multimodal transportation route optimization algorithms considering carbon emissions[J]. Industrial Engineering and Management, 2022, 27(5): 53-59.]

[10] 黄琴, 张惠珍,马良,等.混合哈里斯鹰优化算法求解带模糊需求的低碳多式联运路径规划问题[J]. 计算机应用研究, 2023, 40(10): 2978-2983, 2999. [HUANG Q, ZHANG H Z, MA L, et al. Hybrid Harris Hawks optimization algorithm for solving low-carbon multimodal transportation path planning problem with fuzzy demand [J]. Application Research of Computers, 2023, 40(10): 2978-2983, 2999.]

[11] ZHAO B, GUI H, LI H, et al. Cold chain logistics path optimization via improved multi-objective ant colony algorithm[J]. IEEE Access, 2020, 8: 142977-142995.

[12] DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.

[13] 李靖.基于多目标优化的易腐货物集装箱多式联运路径选择研究[D]. 北京: 北京交通大学, 2021. [LI J. Research on route selection of perishable containerized cargo multimodal transport based on multi-objective optimization [D]. Beijing: Beijing Jiaotong University, 2021.]

[14] WANG Q Z, CHEN J M, TSENG M L, et al. Modelling green multimodal transport route performance with witness simulation software[J]. Journal of Cleaner Production, 2020, 248: 119245.

[15] 李艳玲, 吕永波. 考虑碳排放和时间成本的多式联运经济性研究[J]. 综合运输, 2023, 45(6): 143-147. [LI Y L, LIYU Y B. Economic research on multimodal transportation considering carbon emissions and time costs[J]. China Transportation Review, 2023, 45(6): 143-147.]

[16] 吴暖, 王诺, 于安琪,等.基于柔性靠泊的港口疏船调度多目标优化及最优解选择[J]. 计算机集成制造系统, 2021, 27(5): 1531-1540. [WU N, WANG N, YU A Q, et al. Multi-objective optimization and optimal solution selection for port vessel evacuation scheduling based on flexible berthing[J]. Computer Integrated Manufacturing Systems, 2021, 27(5): 1531-1540.]

中转时间不确定下冷藏集装箱多式联运路径优化研究

Path Optimization of Reefer Container Intermodal Transportation Under Transfer Time Uncertainty

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