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基于BP方程算法的多机型机组恢复时空网络模型 被引量:4

Crew recovery time-space network model with various types of airplanes based on BP equation algorithm
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摘要 航空公司工作中的一个重要部分就是不正常机组排班恢复,为减少机组排班不正常对航班运行计划的影响,以航空公司资源浪费最小为优化目标,在分析不正常机组排班要满足的客观约束条件下,建立了多机型不正常机组排班恢复的时空网络数学模型,并针对国内某航空公司的实际运营数据运用该模型进行实例分析,利用最小顶点覆盖(MDS)和BP方程法求解。结果表明:用MDS和BP方程法不仅加速了机组排班恢复的时间,更增加了机组排班恢复的鲁棒性。该方法利用完全相关结构,当遇到某些突发情况时,机组排班能自动随之调整,操作起来方法简便,适用面广,并且系统性强,便于普及和推广。 Aircrew scheduling recovery is an important part of airlines work. To abate the effect on flight operation planning from abnormal aircrew scheduling, various types of airplane crew recovery time-space network mode~ is built up. Before the establishment, the objective constraint according to abnormal aircrew scheduling is analyzed. Meanwhile, the minimum of airlines operation aircrew recovery of multi-types of airplanes based on time-space network model and heuristic binary search algorithm on costs is the optimization goal. These models are applied to the actual operational data of some domestic airline to carry on the instance analysis. Minimum vertex cover and BP(Bethe-Peierls) equation algorithm are used to solve it. Results showed that the usage of minimum vertex cover and BP equation algorithm accelerate the aircrew scheduling recovery and increase the robustness of aircrew scheduling recovery. They adjust automatically when some factor changes. Whole-related structure is employed to help aircrew scheduling automatically adjust to urgent change of factors, This model is simple to operate and with strong systematicness to be used widely.
出处 《中国民航大学学报》 CAS 2017年第5期30-35,共6页 Journal of Civil Aviation University of China
基金 国家自然科学基金项目(6157399) 天津市自然科学基金项目(14JCYBJC18700) 中央高校基本科研业务费专项(3122015C025)
关键词 BP方程 最小顶点覆盖 机组排班恢复 能量函数 时空网络模型 BP equation algorithm minimum vertex cover aircrew scheduling recovery energy function time-spacenetwork model
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  • 1王立新.模糊系统与模糊控制[M].北京:清华大学出版社,2003..
  • 2SimonHaykin 叶世伟 史忠植译.神经网络原理[M].北京:机械工业出版社,2004..
  • 3X Wang,L.Solving fuzzy relational equations through network training[C].Proc,2nd IEEE Inern.Conf.on Fuzzy Systems,San Francisco,1993.956-960.
  • 4W Pedrycz.Processing in relational structures:fuzzy relational equations[J].Fuzzy Sets and Systems,40,no.1,1991.77-106.
  • 5H Robbins,T Monro.A stochastic approximation method[J].Annals of Mathematical Statistics,1951,22:400-407.
  • 6L Ljung.Analysis of recursive stochastic algorithms[J].IEEE Transaction on Automatic Control,1977,AC-22:551-575.
  • 7C Darken and J Moody.Towards faster stochastic gradient search[J].Advances n Neural Information Processing System,San Mateo,CA:Morgan Kaufmann,1992,4:1009-1016.
  • 8Ollero A, Boverie S, Goodall R, et al. Mechatronics, robo- tics and components for automation and control-IFAC milestone report[J].Annual Reviews in Control, 2012, 30 (1): 41-54.
  • 9Torgny Brogardh. Present and future robot control develop- ment-an industrial perspective [J]. Annual Reviews in Con- trol, 2011, 31 (1): 69-79.
  • 10De Luca A, Mattone R. An identification scheme for robot ac- tuator faults [J]. IEEE/RSJ International Conference on Intel- ligent Robots and Systems, 2013, 8 (6): 1127-1131.

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