高铁夕发朝至列车开行与天窗设置协同优化

徐长安; 李晟东; 李斯涵; 倪少权

doi:10.3969/j.issn.0258-2724.20191205

高铁夕发朝至列车开行与天窗设置协同优化

doi: 10.3969/j.issn.0258-2724.20191205

徐长安^1,,
李晟东¹,
李斯涵¹,
倪少权^1,2,3, ,

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）

详细信息

作者简介:
徐长安（1991—），男，博士研究生，研究方向为铁路运输组织优化，E-mail：xu_chang_an@163.com

通讯作者:
倪少权（1967—），男，教授，博士，研究方向为铁路运输组织优化与系统工程，E-mail：shaoquanni@163.com

中图分类号: U292.41
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- 被引次数: 0
出版历程
- 收稿日期: 2020-01-19
- 修回日期: 2020-05-19
- 网络出版日期: 2020-09-29
- 刊出日期: 2021-08-15

Collaborative Optimization for Overnight Train Operation and Maintenance Window Setting of High-Speed Railways

摘要

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.
- 高速铁路 /
- 夕发朝至列车 /
- 天窗 /
- 协同优化 /
- 自适应大邻域搜索
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.
- high speed railway /
- overnight trains /
- maintenance window /
- collaborative optimization /
- adaptive large neighborhood search algorithm

HTML全文

图 1 高速铁路夕发朝至列车开行模式

Figure 1. Overnight train operation modes of high-speed railways

下载: 全尺寸图片幻灯片

图 2 冲突情形下列车模式选择

Figure 2. Mode selection of train in case of conflict

下载: 全尺寸图片幻灯片

图 3 不同高速铁路夕发朝至列车开行模式下的天窗设置方案

Figure 3. Maintenance window setting scheme under different overnight train operation modes of high-speed railways

下载: 全尺寸图片幻灯片

表 1 最优天窗方案

Table 1. Optimal maintenance window scheme

天窗分段	天窗时间
北京—石家庄	00∶00—04∶00
石家庄—郑州	00∶00—04∶00
郑州—武汉	02∶00—06∶00
武汉—长沙	02∶00—06∶00
长沙—郴州	00∶00—04∶00
郴州—广州	00∶00—04∶00

下载: 导出CSV

表 2 京广高铁夕发朝至列车最优解对应的到发时刻表

Table 2. Optimal arrival and departure time table of Beijing−Guangzhou high-speed railways

车次	北京西	石家庄	郑州东	武汉	长沙南	郴州西	广州南
D901	19∶02	20∶12/20∶15	21∶51/21∶51	00∶01/00∶01	01∶30/01∶30	04∶00/04∶00	05∶35
D902	05∶19	04∶07/04∶10	01∶30/01∶30	23∶18/23∶18	21∶51/21∶51	20∶31/20∶31	19∶00
D903	19∶07	20∶17/20∶20	22∶01/22∶06	00∶06/00∶06	01∶35/01∶35	04∶05/04∶05	05∶40
D904	05∶24	04∶12/04∶15	01∶35/01∶35	23∶23/23∶23	21∶56/22∶02	20∶36/20∶36	19∶05
D909	19∶12	20∶22/20∶25	21∶01/21∶06	00∶11/00∶11	01∶40/01∶40	04∶10/04∶10	06∶34
D910	05∶29	04∶17/04∶22	01∶40/01∶40	23∶28/23∶28	22∶01/22∶07	20∶41/20∶41	19∶10
D921	19∶17	20∶27/20∶30	22∶06/22∶11	00∶16/00∶16	01∶45/01∶45	04∶15/04∶15	06∶39
D922	06∶10	04∶32/04∶35	01∶45/01∶45	23∶33/23∶33	22∶12/22∶12	20∶46/20∶46	19∶15
D923	19∶22	20∶32/20∶35	22∶11/22∶16	00∶21/00∶21	01∶50/01∶50	04∶20/04∶20	06∶55
D924	06∶15	04∶37/04∶40	01∶50/01∶50	23∶38/23∶38	22∶17/22∶23	20∶51/20∶51	19∶20
D927	19∶27	20∶37/20∶40	22∶16/22∶21	00∶26/00∶26	01∶55/01∶55	04∶25/04∶25	07∶00
D928	06∶51	04∶42/04∶45	01∶55/01∶55	23∶43/23∶43	22∶28/22∶34	20∶57/20∶57	19∶25
D939	19∶32	20∶42/20∶45	22∶21/22∶26	00∶31/00∶31	02∶00/02∶00	04∶30/04∶30	07∶05
D940	06∶56	04∶50/04∶50	02∶00/02∶00	23∶48/23∶48	22∶33/22∶39	21∶02/21∶02	19∶30

下载: 导出CSV

表 3 不同列车开行模式下的目标函数值

Table 3. Objective function values under different overnight train operation modes

列车开行模式	f₁/min	f₂/列	计算时间/s
等线模式	9482	76/0	141
下线模式	9780	38/41	187
下线-上线模式	9360	45/32	432
混合模式	9240	43/38	784

下载: 导出CSV

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