| 高铁夕发朝至列车开行与天窗设置协同优化 |
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| 引用本文： | 徐长安,李晟东,李斯涵,倪少权.高铁夕发朝至列车开行与天窗设置协同优化[J].西南交通大学学报,2021,56(4):744-751. |
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| 作者姓名： | 徐长安 李晟东 李斯涵 倪少权 |
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| 基金项目： | 国家重点研发计划（2017YFB1200702）；国家自然基金（61703351，71971182）；中国铁路总公司科技研究计划（P2018T001，P2018X001，N2018X006-01）；四川省科技计划（2020YFH0035，2020YJ0268，2020YJ025，2020JDRC0032） |
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| 摘 要： | 高速铁路夕发朝至列车开行与天窗设置存在动态影响关系，将两者进行协同优化有助于满足旅客夜间出行需求，提升铁路运力资源配置. 以通道型高速铁路为对象，在分析高速铁路夕发朝至列车与天窗设置影响关系的基础上，以高速铁路夕发朝至列车总的旅行时间最少和高速铁路夕发朝至列车的开行对既有列车运行图的影响最小为目标，建立了列车开行模式未定情形下高速铁路夕发朝至列车与天窗协同优化的非线性混合整数规划模型. 结合问题特性，提出了双目标转换和约束线性化处理等模型约简策略，设计了基于自适应大邻域搜索的启发式求解算法. 最后，以京广高速铁路走廊为例，对模型算法的有效性进行了验证. 结果表明:算法经过40次左右迭代便可收敛至最优解，耗时784 s.
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| 关 键 词： | 高速铁路 夕发朝至列车 天窗 协同优化 自适应大邻域搜索 |
| 收稿时间： | 2020-01-19 |
Collaborative Optimization for Overnight Train Operation and Maintenance Window Setting of High-Speed Railways |
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| XU Chang’an,LI Shengdong,LI Sihan,NI Shaoquan.Collaborative Optimization for Overnight Train Operation and Maintenance Window Setting of High-Speed Railways[J].Journal of Southwest Jiaotong University,2021,56(4):744-751. |
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| Authors: | XU Chang’an LI Shengdong LI Sihan NI Shaoquan |
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| Abstract: | There is a coupling relationship between overnight train operation and maintenance window setting of high-speed railways. Their collaborative optimization contributes to meeting passenger travel demands at night and improves the allocation of railway transportation resources. Taking the corridor-type high-speed railways as the object, the analysis of the dynamic relationship between overnight train operation and maintenance window setting is performed, with the goal of minimizing the total travel time of all overnight trains and the impact of overnight train operation on the existing train timetable. A nonlinear mixed integer programming model is established to realize the collaborative optimization of overnight train operation and maintenance window in the condition of the undetermined train operation mode. Given the complexity of the problem, dimension reduction strategies such as dual-objective transformation and constraint linearization are proposed; then a heuristic algorithm based on the adaptive large neighborhood search is designed to solve the problem. Finally, a case study of Beijing?Guangzhou railway corridors was conducted to verify the method. The results show that the algorithm can converge to the optimal solution after about 40 iterations at a cost of 784 s. |
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