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【背景】科学有效的动车组运用调整方法，能确保调整计划的质量，有利于在高速铁路区间完全中断情况下列车运行秩序的快速恢复。【目标】以列车停运惩罚、动车组运用计划偏离惩罚、动车所库存偏离惩罚以及动车组运行成本的加权和最小为优化目标。【方法】基于构建的动车组接续网络，采用停运列车、空驶、重联/分解、改变接续以及备用车启用等调整措施，考虑动车组检修、动车所容量以及动车所日终库存状态等实际约束，建立基于动车组单元路径的整数线性规划模型，并设计列生成算法求解。【数据】选取郑州局集团公司配属动车组运用数据，构造实际算例验证所提方法的有效性。【结果】所提模型和算法能高效求解不同区间、不同时长中断下的实际案例，且算法能最快在6 s内求解得到所有算例的最优调整方案，满足实时调度需要，可为现场调度员提供辅助决策支持。
Abstract：[Background] A scientific and effective rolling stock(RS) rescheduling is critical for ensuring 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 constructed, 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 successfully 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 onsite dispatchers.
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基本信息:
DOI：10.19961/j.cnki.1672-4747.2024.05.004
中图分类号:U292
引用信息:
[1]李沁洋,彭其渊,张永祥等.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(02):136-149.DOI:10.19961/j.cnki.1672-4747.2024.05.004.
基金信息:
国家重点研发计划项目（2022YFB4300502）; 国家自然科学基金项目（72201218,72201268）; 四川省自然科学基金项目（2023NSFSC0901）