摘要
针对铁路列车运行图优化模型精确解求解困难的问题,结合累积流变量模型的特点,提出基于累积流变量的列车运行图优化0-1整数规划模型,设计拉格朗日松弛求解算法,将复杂的列车组合优化问题转化为单列车的最短路径问题集合,从而降低求解难度。针对拉格朗日松弛子问题,设计具有状态空间的时空网络,实现车站作业方式的差异化处理。模型与算法以武广高铁为背景进行验证和分析。
The research established a cumulative flow variable-based binary programming model for train time-table optimization integrated with the characteristics of cumulative flow variable in order to reduce the difficulty of solving the train timetabling problem. A Lagrangian relaxation algorithm was designed to decrease the difficulty of solving the problem by transforming the complicated schedule problem into a set of time-space shortest path problems of independent trains. In response to the subproblems of Lagrangian relaxation, a time-space network with state space was designed to achieve the differentiated treatment of station operation mode. The model and algorithm were analyzed and verified by a numerical experiment on Wuhan-Guangzhou high speed railway.
出处
《铁道学报》
EI
CAS
CSCD
北大核心
2016年第9期1-8,共8页
Journal of the China Railway Society
基金
国家自然科学基金(71571012)
轨道交通控制与安全国家重点实验室自主课题(RCS2014ZT25)
北京交通大学基本科研业务费(I16JB00080)
关键词
列车运行图
累积流变量
整数规划
拉格朗日松弛
train timetable
cumulative flow variable
integer programming
Lagrangian relaxation
