交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (6): 23-33.DOI: 10.16097/j.cnki.1009-6744.2025.06.003

• 综合交通运输体系论坛 • 上一篇    下一篇

考虑生产与班期协同的多式联运优化研究

李亚军* ,薛龙江,郑建风   

  1. 大连海事大学,交通运输工程学院,辽宁大连116026
  • 收稿日期:2025-04-30 修回日期:2025-07-24 接受日期:2025-09-18 出版日期:2025-12-25 发布日期:2025-12-23
  • 作者简介:李亚军(1976—),女,四川南充人,副教授。
  • 基金资助:
    国家自然科学基金(72371046)。

Optimization of Multimodal Transport Considering Coordination of Production and Departure Schedules

LI Yajun*, XUE Longjiang, ZHENG Jianfeng   

  1. College of Transportation Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China
  • Received:2025-04-30 Revised:2025-07-24 Accepted:2025-09-18 Online:2025-12-25 Published:2025-12-23
  • Supported by:
    National Natural Science Foundation of China (72371046)。

摘要: 针对现有研究中普遍忽视生产与运输脱节的问题,本文构建一个融合生产线分配、生产顺序与多式联运路径选择的双目标优化模型。该模型以系统总成本和总时间最小化为目标,将定时发班与定量发班两种铁路发班模式引入模型,更真实地刻画多式联运的运营特性。为求解模型,设计改进的多目标粒子群算法,并通过动态调整算法参数、结合模拟退火机制,增强全局搜索能力并加速了收敛。最后,结合釜山—汉堡/鹿特丹的实例进行数值实验,结果表明,最优调度方案使总成本降低11.4%,总时间缩短580h。与其他多目标优化算法相比,本文提出的算法在解的多样性与精度方面具有显著优势。情景分析进一步揭示,仅优化生产或仅优化班期均难实现整体最优,独立调整会导致成本上升超18%。敏感性分析显示,班期间隔与仓储单价的适度调整可在兼顾成本与时效的前提下,影响运输方式选择。

关键词: 综合运输, 生产运输联合调度, 多目标粒子群算法, 多式联运, 班期, 发班方式

Abstract: In order to solve the problem of the disconnection between production and transportation, this paper constructs a bi objective optimization model which integrates the allocation of production line, production sequence and multimodal transportation route selection. With the purpose of minimizing the total cost and time, two railway departure modes, scheduled departure and fixed-frequency departure, are introduced into the model to truly describe the characteristics of multimodal transportation. To solve the model, an enhanced algorithm of multi-objective particle swarm optimization is proposed, which improves the global search capabilities and convergence speed by dynamically adjusting the parameter of algorithm and combining with the simulated annealing mechanisms. Finally, the numerical experiments are carried out with an example from Busan to Hamburg/Rotterdam. The results show that the optimal scheduling scheme can reduce the total cost by 11.4% and the total time by 580 hours. Compared with the other multi-objective optimization algorithms, the proposed algorithm has significant advantages in the diversity and accuracy of solutions. Scenario analyses reveal that optimizing either production or transportation scheduling separately fails to achieve overall optimality, and increase costs by over 18% potentially. Sensitivity analyses indicate that the moderate adjustments of departure schedule and unit storage costs can effectively influence the selection of transportation modes under the balance cost and time.

Key words: integrated transportation, joint production and transportation scheduling, multi-objective particle swarm optimization; multimodal transportation, departure schedule, departure strategy

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