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考虑Pareto最优的列车运行图与维修天窗协调优化

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列车运行图与维修天窗之间的冲突始终无法避免,且维修天窗开设时间的长短显著影响列车总运行时间.针对此问题,综合考虑维修天窗对列车运行造成的限速约束、车站到发线数量约束等,建立列车总运行时间最小,以及维修天窗实际开设时长与理想时长总偏差最小的双目标混合整数规划模型;对困难约束设置中间辅助变量将模型线性化以提高求解效率,并设计约束转换算法求解双目标模型的Pareto最优;微观化处理铁路线,将站内资源和站间资源细化为一系列行车资源单元,得到更加符合实际旅客运输需求的运行图.以某地区铁路线夜间开行列车及维修天窗开设计划为研究背景,调用商业软件求解双目标函数模型的Pareto最优,并对双目标模型的最小支配解和最优支配解进行对比分析;针对最优支配解下的列车进入、离开行车资源单元的时间、停站作业时间及维修天窗的开始时间及开设时长,绘制列车运行图.求解结果表明:模型在满足维修天窗最小开设时长的同时,能够兼顾列车运行总时间最小和维修天窗开设时长更充裕.基于最优支配解绘制的列车运行图表明:微观路网下的列车运行时刻表优化结果更符合实际旅客运输生产作业需要.研究结果可为铁路运营管理部门进一步优化列车运行图编制与维修天窗开设提供参考.
Coordinated optimization of train timetable and maintenance skylight considering Pareto optimality
The conflict between train timetable and maintenance skylight was inevitable,and the duration of maintenance skylight significantly impacts train operation time.To address this,a dual-objective mixed integer programming model was proposed.It could minimize the total train operation time and the overall deviation between the actual and ideal durations of maintenance skylight while considering various constraints such as speed restrictions imposed by maintenance skylight and the number of arrival-departure lines.Intermediate auxiliary variables were introduced to improve solution efficiency.A constraint transformation algorithm was designed to obtain the Pareto optimality for the model.The railway line was micro-managed by refining station resources and inter-station resources into a series of train resource units,aiming to obtain a more suitable timetable with transportation needs.Under the background of nighttime train operations and maintenance skylight scheduling on a specific railway line,a commercial software was employed to obtain the Pareto optimality.Comparative analysis was performed between minimum and optimal dominant solutions.Based on the optimal dominant solutions,the timetable was depicted,considering train arrivals and departures,station dwell times,and maintenance skylight schedules.The results can demonstrate satisfied constraints,minimize operation time,and increase maintenance skylight duration.The generated timetable aligns better with actual passenger transportation needs.These findings can provide valuable insights for optimizing train timetable compilation and maintenance skylight scheduling in railway operations and management.

railway transportationtrain timetablemaintenance skylightthe number of arrival-departure linesconstraint transformation algorithmPareto optimality

张哲铭、何世伟、李光晔、赵子琪、王攸妙、周汉

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北京交通大学 综合交通运输大数据应用技术交通运输行业重点实验室,北京 100044

铁路运输 列车运行图 维修天窗 到发线数量 约束转换算法 Pareto最优

中央高校基本科研业务费专项国家自然科学基金中国国家铁路集团有限公司科技研发计划

2022JBQY00662076023K2021X002

2024

10.19713/j.cnki.43-1423/u.T20230820

铁道科学与工程学报
中南大学 中国铁道学会

铁道科学与工程学报

CSTPCD北大核心EI
影响因子：0.837
ISSN：1672-7029
年,卷(期)：2024.21(3)
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