高速综合检测列车检测区域划分方法研究

doi:10.16097/j.cnki.1009-6744.2025.01.030

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (1): 319-330.DOI: 10.16097/j.cnki.1009-6744.2025.01.030

高速综合检测列车检测区域划分方法研究

许旻昊^*1，薛凯肇¹，帅斌²，王宇³，杨乔礼¹，左静¹，张雁鹏¹

1. 兰州交通大学，自动化与电气工程学院，兰州730070；2.西南交通大学，交通运输与物流学院，成都611756；3. 大连交通大学，交通运输工程学院，辽宁大连116028

收稿日期:2024-12-08 修回日期:2024-12-23 接受日期:2025-01-06 出版日期:2025-02-25 发布日期:2025-02-24
作者简介:许旻昊(1995—)，男，四川成都人，讲师，博士。
基金资助:
中国国家铁路集团有限公司科技研究开发计划重点课题(N2024G040)；甘肃省高校教师创新基金项目(2025B-063)；辽宁省自然科学基金面上项目(2023-MS-273)。

Network Districting Plan Optimization for Comprehensive Inspection Trains

XU Minhao^*1, XUE Kaizhao¹, SHUAI Bin², WANG Yu³,YANG Qiaoli¹,ZUO Jing¹, ZHANG Yanpeng¹

1. School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China; 2. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China; 3. School of Transportation Engineering, Dalian Jiaotong University, Dalian 116028, Liaoning, China

Received:2024-12-08 Revised:2024-12-23 Accepted:2025-01-06 Online:2025-02-25 Published:2025-02-24
Supported by:
China National Railway Group Corporation Science and Technology Research and Development Program Key Project (N2024G040)；Innovative Foundation for Universities Teachers of Gansu Province, China (2025B-063)；Natural Science Foundation of Liaoning Province, China (2023-MS-273)。

摘要/Abstract

摘要： 为解决传统人工经验模式划分高速综合检测列车(CIT)检测区域，难以保证合理性和公平性的不足，本文提出以任务均衡性和结构紧凑性为双目标的混合整数线性规划模型为核心的检测区域划分方法。该模型综合考虑覆盖完整性、达速检测、线-车特殊属性匹配及检测区域连通等关键约束；基于多商品流模型，构建适用于具有复杂异质性和向下兼容且非唯一匹配关系的连通性判定约束；以各目标单独求解对应最优解的倒数作为目标权重，将原模型转为与各目标总实现程度最大化的单目标混合整数线性规划模型；最后，设计不同场景的计算实验，得出影响分区方案质量的关键因素，并将所提方法运用于实际规模的线网进行测试。实验结果表明：CIT异质性对划分方案的质量与求解速度影响最为显著；本文提出的检测区域划分方法可有效解决人工编制模式无法兼顾均衡检测负荷与减少异地出差的局限；使用10列CIT时，划分方案与理想解总偏差仅为2.84%，显著优于人工方案。本文可为指导检测资源配置提供决策依据。

关键词: 铁路运输, 区域划分方案, 混合整数规划模型, 综合检测列车, 分区问题, 异质性

Abstract: To address the limitations of the traditional manual experience-based districting of Comprehensive Inspection Train (CIT) inspection areas, this paper proposes an optimization method for the districting plans based on a mixed-integer linear programming (MILP) model with the objectives of task balance and structural compactness. The method incorporates key constraints such as coverage integrity, inspections under up-to-speed conditions, line-vehicle attribute matching, and inspection area connectivity. The adjudication constraints are defined for connectivity with complex heterogeneity and downward compatible and non-unique matching relationship based on multi-flow model. Based on the ideal point method, the inverse of each objective's optimal solution is used as a weight, converting the original multi-objective model into a single-objective MILP to maximize the overall performance. Computational experiments are conducted across various scenarios to identify the key factors influencing the districting plan's quality. The proposed method is then applied to an actual large-scale network to provide decision support for inspection resource allocation. Experimental results indicate that CIT heterogeneity significantly affects both the quality of the districting plan and solution speed. The proposed districting method effectively mitigates the limitations of manual methods by balancing inspection loads and reducing off-site travel. With 10 CITs, the total deviation from the ideal solution is 2.84%, demonstrating a marked improvement over existing manual method.

Key words: railway transportation, districting plan, mixed-integer programming model, comprehensive inspection train, districting problem, heterogeneity

中图分类号:

U292.8

许旻昊, 薛凯肇, 帅斌, 王宇, 杨乔礼, 左静, 张雁鹏. 高速综合检测列车检测区域划分方法研究[J]. 交通运输系统工程与信息, 2025, 25(1): 319-330.

XU Minhao, XUE Kaizhao, SHUAI Bin, WANG Yu, YANG Qiaoli, ZUO Jing, ZHANG Yanpeng. Network Districting Plan Optimization for Comprehensive Inspection Trains[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(1): 319-330.

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http://www.tseit.org.cn/CN/Y2025/V25/I1/319

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高速综合检测列车检测区域划分方法研究

Network Districting Plan Optimization for Comprehensive Inspection Trains

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