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考虑接续可靠性的动车组运用计划优化方法 被引量:6

Optimum Circulation Model for Electrical Multiple UnitsConsidering Reliability of Their Operational Connections
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摘要 提高动车组运用计划的接续可靠性可有效降低运营成本,更好适应不断变化的运营环境。基于动车组运用网络图,考虑动车组初始任务、空车调拨等运用条件和检修里程、检修能力等约束条件,分析任务晚点条件下动车组完成相邻任务的能力,进一步提出交路段和整个运用日计划的接续可靠性定量化方法。在此基础上建立以运营总惩罚费用最小为目标,考虑检修和热备任务的动车组运用计划优化模型,设计改进的蚁群算法求解模型,并利用算例验证。算例表明:在不改变基本运营条件下,适当增加接续时间可有效提高动车组运用计划的接续可靠性,且有效降低因接续失效而带来的高风险。 The enhancement of the connection reliability of Electrical Multiple Units(EMU)operation plan can significantly reduce the risk of operational costs with a strong ability adapting to operational condition changes.According to the network diagram for EMU operations considering the initial tasks of EMUs,maintenance mileage,maintenance ability and empty runs as constraint conditions,the implementation capabilities of the adjacent services connections between trains were investigated as connection reliability when the delay risk existed.The quantitative method was proposed to evaluate the connection reliability over the routings or whole daily circulations.Moreover,an optimum model for EMU operational circulation taking into account maintenance and standby activities was established to minimize total operational penalty costs.An improved ant colony algorithm was developed to solve the proposed model.The numerical case study shows that an appropriate increment of connecting time is convenient for the improvemt of the connection reliability of EMU operation schedules and for the reduction of the risks from invalidation of connections.
作者 仝硕 陈绍宽 刘葛辉 刘爽 毛保华 TONG Shuo;CHEN Shaokuan;LIU Gehui;LIU Shuang;MAO Baohua(Beijing Transport Institute,Beijing 100073,China;MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport,Beijing Jiaotong University,Beijing 100044,China)
出处 《铁道学报》 EI CAS CSCD 北大核心 2020年第4期27-34,共8页 Journal of the China Railway Society
基金 国家自然科学基金(71571015)。
关键词 动车组运用计划 运用网络图 列车接续可靠性 改进蚁群算法 operation scheduling for EMU circulation network diagram connection reliability improved ant colony algorithm
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