交通运输系统工程与信息

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (2): 282-292.DOI: 10.16097/j.cnki.1009-6744.2025.02.026

• 系统工程理论与方法 • 上一篇    下一篇

省域铁路成网条件下列车开行方案优化

龚帅宇1a,1b，徐行方*1a,1b，鲁玉2a,2b   

  1. 1. 同济大学，a.道路与交通工程教育部重点实验室，b.上海市轨道交通结构耐久与系统安全重点实验室，上海201804； 2. 阜阳师范大学，a.商学院，b.区域物流规划与现代物流工程安徽省重点实验室，安徽阜阳236037
  • 收稿日期:2024-11-26 修回日期:2025-02-11 接受日期:2025-02-12 出版日期:2025-04-25 发布日期:2025-04-20
  • 作者简介:龚帅宇(1999—)，男，浙江台州人，博士生。
  • 基金资助:
    浙江省交通运输厅科研计划项目(2021057)。

Optimization of Train Scheme for Provincial Railway Network

GONG Shuaiyu1a,1b,XU Xingfang*1a,1b,LU Yu2a,2b   

  1. 1a. The Key Laboratory of Road and Traffic Engineering, Ministry of Education, 1b. Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Tongji University, Shanghai 201804, China; 2a. Business School, 2b. Anhui Provincial Key Laboratory of Regional Logistics Planning and Modern Logistics Engineering, Fuyang Normal University, Fuyang 236037,Anhui, China
  • Received:2024-11-26 Revised:2025-02-11 Accepted:2025-02-12 Online:2025-04-25 Published:2025-04-20
  • Supported by:
    Zhejiang Provincial Department of Transportation Research Project (2021057)。

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摘要： 省域铁路成网条件下列车开行方案涉及线路制式、等级以及列车种类等多因素影响，叠加客流选择的多样性，使优化问题更加复杂化。为刻画网络条件下客流和列车流的耦合，利用深度优先搜索(Depth First Search, DFS)算法构建客流径路备选集；基于列车开行方案的编制原则建立列车径路备选集，以列车运行成本和旅客出行总时间最小为优化目标，构建双目标非线性优化模型，设计NSGA-II(Non-dominated Sorting Genetic Algorithm-II)算法进行求解；从列车运行、旅客出行和企业运营这3个维度建立多准则评价指标体系，利用熵权-TOPSIS(Technique for Order Preference by Similarity to Ideal Solution)方法比选Pareto前沿面上的典型解，选取相对接近度最高的解作为建议方案。依托Z省铁路网进行大规模实例研究，结果表明：模型求解得到的Pareto前沿面收敛性和分布性较好，具有较强鲁棒性，与多目标粒子群优化算法(Multi-objective Particle Swarm Optimization, MOPSO)相比取得的结果更佳。通过熵权-TOPSIS方法多准则评价比选，得到方案II为该省推荐列车开行方案，省域范围内开行列车660对·d-1，相较优化前，旅客出行时间成本和列车运行成本显著降低，线路利用率过低或过高区段比例大幅减少，运能紧张区段得到有效疏解。

关键词: 铁路运输, 列车开行方案, 多目标优化, 多层次铁路网, 备选集, 多准则评价

Abstract: In a railway network, the train operation schedule involves multiple factors such as line specifications, grades, and types of trains, which are further complicated by the diversity of passenger flow choices. To depict the coupling between passenger flow and train flow in the network context, this paper used a depth-first search (DFS) algorithm to construct a set of alternative passenger flow routes, and established a set of alternative train flow routes based on the principles of train operation schedule preparation. The optimization objective is to minimize the total running cost of trains and the total travel time of passengers, and a bilevel nonlinear optimization model is established. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) algorithm is used to solve the model, and a multi-criteria evaluation index system is established from the perspectives of train operation, passenger travel, and enterprise operation. The Pareto frontier is evaluated using the entropy weight-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, and the solution with the highest relative proximity is selected as the recommended solution. Based on a large-scale example of the Z province railway network, the results show that the Pareto frontier obtained by the model has good distribution and convergence, which has strong robustness and better solution effect than the multi objective particle swarm optimization algorithm (MOPSO). The recommended solution II is obtained through the entropy weight TOPSIS multiple criteria method. 660 train pairs per day are operated in the province, and the travel time and operating cost of passengers and trains are significantly reduced compared to before optimization. The proportion of sections with low or high utilization rates is greatly reduced, and the congested sections are effectively alleviated.

Key words: railway transportation, train operation plan, multi-objective optimization, multi-level railway network, alternative set, multi-criteria evaluation

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