集装箱海运网络在多源异构环境扰动因子的交互作用下，呈现出高度的非稳态需求波动。这种时变需求剖面与非线性运价弹性及船舶航路固定性之间产生深刻矛盾，导致传统基于稳态假设的航线优化模型陷入决策适应性困境，表现为运力供需失配、航线经济性劣化及客户服务水平衰减。为解决上述问题，本文构建考虑波动需求的集装箱船舶航线优化算法。分析船舶航线的不同航段货运量与港口对间需求量的相关性，根据不同航段货运量可确定集装箱需求量。构建集装箱航线双层优化模型，下层模型以集装箱船舶航线收益最大化为目标函数，设定运输成本、时间、集装箱波动需求等约束条件，由此确定航线收益；上层模型根据航线收益选择最优航线。实验结果显示，该算法在不同货运量条件下的集装箱需求量计算误差控制在5%以内；能够显著降低不同航线的航程、航行时间与成本，实现航线收益提升的目的，说明该算法能适应复杂波动需求场景，具实际推广价值。
Container shipping networks experience highly volatile demand fluctuations under the influence of multiple heterogeneous disturbances. A significant contradiction exists between this time-varying demand profile, the non-linear elasticity of freight rates, and the fixed nature of shipping routes. This mismatch renders traditional route optimization models-based on steady-state assumptions-poorly adaptable, leading to imbalances in capacity supply and demand, reduced route profitability, and declining service levels. To address these issues, this paper proposes a container ship route optimization algorithm that accounts for demand fluctuations. The method analyzes the correlation between segment-specific cargo volumes and port-to-port demand to estimate container requirements. A bi-level optimization model is constructed: the lower-level model maximizes route revenue subject to constraints including transportation costs, time windows, and fluctuating container demand, thereby determining the potential revenue of a given route; the upper-level model selects the optimal route based on the computed revenues. Experimental results show that the proposed algorithm limits the estimation error of container demand to within 5% under varying cargo volume conditions. It significantly reduces the voyage distance, sailing time, and cost for different routes, thereby effectively increasing route revenue. These findings demonstrate the algorithm's strong adaptability to complex, fluctuating demand scenarios and its practical value for real-world application.
2026,48(4): 201-205 收稿日期：2025-11-12
DOI：10.3404/j.issn.1672-7649.2026.04.031
分类号：U675
基金项目：2023年度福建省交通运输科技项目（XY202302）
作者简介：阮毅(1982-),男,硕士,副教授,研究方向为航线规划、航运安全、智慧物流、智能航运
参考文献：
[1] 蒙经淼, 杨鹏, 李金成, 等. 基于内外场匹配法的20000 TEU集装箱船非线性波浪载荷研究[J]. 中国船舶研究, 2025, 20(2): 214-226
MENG J M, YANG P, LI J C, et al. Analysis on nonlinear wave loads of a 20 000 TEU container ship based on IORM[J]. Chinese Journal of Ship Research, 2025, 20(2): 214-226
[2] 李振福, 王婷婷, 邱嘉欣. 考虑自愿速度损失和货主满意度的北极东北航线船舶航速优化[J]. 上海海事大学学报, 2024, 45(2): 53-61+74
LI Z F, WANG T T, QIU J X. Ship speed optimization for Northern aea route considering voluntary speed loss and cargo owner satisfaction[J]. Journal of Shanghai Maritime University, 2024, 45(2): 53-61+74
[3] 黄国良, 周毅, 郑坤, 等. 基于改进蚁群算法的全局船舶路径规划方法[J]. 船海工程, 2023, 52(2): 97-101+136
HUANG G L, ZHOU Y, ZHENG K, et al. Ship path planning and cllision avoidance based on improved ant colony algorithm[J]. Ship & Ocean Engineering, 2023, 52(2): 97-101+136
[4] 任晓玲, 赵涓涓, 任佳丽. 混合自适应布谷鸟算法的物流配送路径优化[J]. 计算机仿真, 2024, 41(5): 168-171+241
REN X L, ZHAO J J, REN J L. Optimization of logistics delivery path based on hybrid adaptive cuckoo algorithm[J]. Computer Simulation, 2024, 41(5): 168-171+241
[5] 叶嘉宁, 谢博祎, 孙俊锋, 等. 面向导航服务的水网地区船舶航线规划[J]. 中国航海, 2024, 47(4): 60-65+72
YE J N, XIE B Y, SUN J F, et al. Navigation service-oriented ship route planning for water network areas[J]. Navigation of China, 2024, 47(4): 60-65+72
[6] 先梦瑜. 一种基于Dijkstra的物流配送路径优化算法设计[J]. 电子设计工程, 2023, 31(2): 20-24
XIAN M Y. A Dijkstra based optimization algorithm design for logistics distribution path[J]. Electronic Design Engineering, 2023, 31(2): 20-24
[7] 陈慧敏, 窦培林, 程晨, 等. 基于Bi-RRT和TEB算法的风电水域多目标点路径规划[J]. 船海工程, 2024, 53(4): 130-136.
CHEN H M, DOU P L, CHENG C , et al. Multi-objective point path planning for wind turbine waters based on Bi-RRT and TEB algorithms[J]. Ship & Ocean Engineering, 2024, 53(4): 130-136.
[8] 肖笛, 李俊, 温想, 等. 支线集装箱船舶航线规划与配载协同优化[J]. 中国航海, 2023, 46(4): 54-60
XIAO D, LI J, WEN X, et al. Joint optimization in route planning and stowage for feeder container ships[J]. Navigation of China, 2023, 46(4): 54-60
