铁道科学与工程学报2025，Vol.22Issue(3)：979-990,12.DOI:10.19713/j.cnki.43-1423/u.T20241760
中断条件下高铁列车运行调整优化模型与算法研究
Optimization model and algorithm for high-speed railway train operation adjustment under disruptions conditions
摘要
Abstract
To address the issue of train service disruptions caused by unexpected incidents during high-speed railway(HSR)operations,an integrated adjustment optimization method combining common scheduling strategies was proposed.These strategies include train cancellations,coupling of short trains,and the deployment of standby train-sets.In addition to considering the basic constraints of the train timetable,the method incorporated constraints related to train-set utilization and passenger demand.This could lead to the construction of an integrated adjustment optimization model for both the train timetable and train-set circulation,aiming to optimize the train timetable while also accounting for train-set scheduling.On this basis,a genetic algorithm enhanced by deep learning was designed,improving the selection mechanism for crossover and mutation.Deep learning was used to derive the optimal crossover and mutation selection strategy under different input conditions,thereby enhancing the quality and efficiency of the genetic algorithm.An experiment involving all 23 stations on the Beijing-Shanghai HSR was conducted to validate the effectiveness and practicality of the proposed model and algorithm.Taking the 120 minutes'disruption duration as an example,the algorithm took 1971 seconds to solve,and the weighted objective function was calculated to be 12 151 079.Compared to the traditional genetic algorithm,the proposed algorithm reduced the total passenger delay time by 11.7%and the computational time by 10.8%.This article set different disruption duration and analyzed the adjustment of train operation under different disruption conditions subsequently,and set different combinations of scheduling methods to compare and analyze the differences of these methods in the optimization effect on the objective function and solving time,providing different optional solutions for railway operation departments.The results suggest that the genetic algorithm enhanced by deep learning can efficiently and effectively optimize train service disruptions,providing high-quality and feasible solutions for dispatchers while ensuring the effective use of railway resources.关键词
高速铁路/列车运行调整/列车运行图/深度学习/改进的遗传算法Key words
high-speed railway/train operation adjustment/train timetable/deep learning/improved genetic algorithm分类
交通运输引用本文复制引用
赵文强,周磊山,白紫熙,韩昌..中断条件下高铁列车运行调整优化模型与算法研究[J].铁道科学与工程学报,2025,22(3):979-990,12.基金项目
北京市自然科学基金-丰台轨道交通前沿研究联合基金资助项目(L231026) （L231026）
北京市教育委员会科研计划项目(KM202410037001) （KM202410037001）
