基于专家系统的网络化动车组运用计划的编制
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  • 英文篇名:Network EMU Scheduling Based on Expert System
  • 作者:陈然 ; 周磊山 ; 乐逸祥 ; 路超 ; 周宇
  • 英文作者:CHEN Ran;ZHOU Leishan;YUE Yixiang;LU Chao;ZHOU Yu;School of Traffic and Transportation,Beijing Jiaotong University;
  • 关键词:动车组运用计划 ; 网络化运行 ; 专家系统 ; 蚁群算法
  • 英文关键词:EMU scheduling;;Network operation;;Expert system;;Ant colony optimization
  • 中文刊名:ZGTK
  • 英文刊名:China Railway Science
  • 机构:北京交通大学交通运输学院;
  • 出版日期:2016-01-15
  • 出版单位:中国铁道科学
  • 年:2016
  • 期:v.37;No.146
  • 基金:铁道部科技研究开发计划项目(T14D00121);; 中央高校基本科研业务专项资金资助项目(2014JBZ2008,T15JB00050)
  • 语种:中文;
  • 页:ZGTK201601016
  • 页数:9
  • CN:01
  • ISSN:11-2480/U
  • 分类号:110-118
摘要
针对网络化运行条件下动车组运用存在多线路、多运行线、多动车组、多动车段、多修制修程等复杂情况,设计1种更直观表现网络化运行条件下的"网络化动车组运用计划图"。以检修作业包的里程和时间、动车段(动车运用所)检修能力、最小接续时间等为约束条件,构建以动车组使用数目最少为最优目标、检修次数最少为次优目标的动车组运用双目标规划优化模型;将编制动车组运用计划的经验加入专家系统,并通过设计基于专家系统指导的最大最小蚁群算法求解模型。以京沪、沪杭、宁杭、京津、沪宁、胶济线上共计432条列车运行线为例,求解动车组运用计划。结果表明:与仅用蚁群算法相比,该算法在一定程度上减少了动车组的使用数量。
        According to the complex situations of EMU operation on network,such as more lines,more operation lines,more EMUs,more EMU depots as well as more repair systems and cycles,we propose a network EMU scheduling diagram,which is clearer to display the information of EMU scheduling in network circumstance.Then a bi-objective programming optimization model for EMU operation is established with the regular running mileage and time of maintenance operation package,the repair capacity of EMU depot(EMU running shed)and the minimum connecting time as its constraints,with the minimum number of EMUs as its optimal target and with the least repair times as its suboptimal target.The experience for making EMU scheduling is added to expert system.The Max-Min Ant System(MMAS)directed by Expert System is proposed to solve the model.Based on 432 train operation lines,we solve an EMU scheduling of a network composed of Beijing-Shanghai,Shanghai-Hangzhou,Nanjing-Hangzhou,BeijingTianjin,Shanghai-Nanjing and Jinan-Qingdao lines.Results show that the solution has reduced the number of EMUs to a certain extent compared to the result by using ant colony optimization alone.
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