交通运输工程与信息学报2025，Vol.23Issue(2)：136-149,14.DOI:10.19961/j.cnki.1672-4747.2024.05.004
基于列生成算法的高速铁路动车组运用调整优化
A column generation approach for high-speed railway rolling stock rescheduling
摘要
Abstract
[Background]A scientific and effective rolling stock(RS)rescheduling is critical for en-suring the quality of rescheduling plans and facilitating the rapid restoration of high-speed railway operations in the event of complete blockages.[Objective]Minimize the weighted sum of penalties associated with trip cancellations,deviations from the original RS operation plan,deviations from the yard inventory,and RS operating costs.[Methods]A railway RS connection network was con-structed,incorporating rescheduling strategies such as train cancellations,RS deadheading,coupling/decoupling,and connection adjustments.Constraints related to RS maintenance,yard capacity,and end-of-day yard inventory states were also considered.An integer linear programming model,based on RS unit paths,was then developed to address the rescheduling of RS operation plans.A column generation algorithm was designed to solve the model.[Data]To validate the effectiveness of the proposed method,real-life instances were designed based on RS data from the railway network of the China Railway Zhengzhou Bureau Group.[Results]The proposed model and algorithm success-fully addressed disruptions of varying sections and durations,solving real-world instances efficiently.The column generation algorithm produced optimal rescheduling plans for all test cases within 6 s,meeting the requirements for real-time dispatching and providing valuable decision support for on-site dispatchers.关键词
铁路运输/动车组运用计划实时调整/列生成算法/动车组/区间完全中断Key words
railway transportation/real-time rolling stock rescheduling/column generation algo-rithm/rolling stock/complete blockages of sections分类
交通工程引用本文复制引用
李沁洋,彭其渊,张永祥,李登辉,钟庆伟,冯涛,姚志洪..基于列生成算法的高速铁路动车组运用调整优化[J].交通运输工程与信息学报,2025,23(2):136-149,14.基金项目
国家重点研发计划项目(2022YFB4300502) （2022YFB4300502）
国家自然科学基金项目(72201218,72201268) （72201218,72201268）
四川省自然科学基金项目(2023NSFSC0901) （2023NSFSC0901）
