期刊文献+

基于方差的保障时间不确定性分析 被引量:1

Variance-based uncertainty analysis methods of logistic support time
下载PDF
导出
摘要 装备的保障过程由于受到保障活动参数的影响存在着较大的不确定性.针对保障过程的不确定性问题,建立了基于方差的保障活动不确定性重要度作为保障过程全局敏感性分析指标;根据保障过程工期的GERT(Graphic Evaluation and Review Technique)模型,设计了一种基于方差的保障时间不确定分析方法来计算保障活动的时间不确定性重要度,并对该方法进行解析求解,给出了具体步骤.最后,将舰载机航空保障作为案例,验证了方法的有效性. Uncertainty in logistics support process is present under the influence of support activities pa rameters. In this work, global sensitivity indicator was introduced which looks at the influence of input uncer tainty on the entire output distribution without reference to a specific moment of the output and which can be defined also in the presence of correlations among the parameters. Variance-based uncertainty analysis method with the graphic evaluation and review technique (GERT) model of logistic support process duration was de signed. And then, an analytic algorithm to calculate the uncertainty analysis was designed with GERT. At last, the analytic algorithm was used to analysis the aircraft support activities uncertainty analysis. Given the limited support resources, the uncertainty analytic algorithm is helpful for a decision maker to identify the most important input parameters that control the uncertainty in the model output.
出处 《北京航空航天大学学报》 EI CAS CSCD 北大核心 2013年第11期1455-1459,共5页 Journal of Beijing University of Aeronautics and Astronautics
基金 国家自然科学基金资助项目(71171008)
关键词 保障过程工期 全局敏感性分析 不确定性分析 图示评审技术 logistic support process duration global sensitivity analysis uncertainty analysis GERT
  • 相关文献

参考文献18

  • 1王学望,康锐,张侦英,黄兆东,程海龙.任务驱动的保障活动时间优化[J].计算机集成制造系统,2010,16(10):2227-2232. 被引量:3
  • 2Apostolakis G E. How useful is quantitative risk assessment.[J].Risk Analysis,2004,(03):515-520.
  • 3文佳,康锐,刘瑞,贾治宇.基于保障活动流程的保障设备需求量计算模型[J].系统工程与电子技术,2010,32(9):1903-1906. 被引量:9
  • 4Qiao Liu,Toshimitsu Homma. A new computational method of a moment-independent uncertainty importance measure[J].Reliability Engineering & System Safety,2009,(01):1205-1211.
  • 5Campolongo F,Tarantola S,Saltelli A. Tackling quantitatively large dimensionality problems[J].Computer Physics Communications,1999,(02):75-85.doi:10.1016/S0010-4655(98)00165-9.
  • 6Morris M D. Factorial sampling plans for preliminary computational experiments[J].Technometrics,1991,(02):161-174.
  • 7Saltelli A,Tarantola S,Campolongo F. Sensitivity analysis as an ingredient of modeling[J].Statistical Science,2000,(04):377-395.
  • 8Saltelli A. Sensitivity analysis for importance assessment[J].Risk Analysis,2002,(03):579-590.
  • 9Helton J C,Davis F J,Johnson J D. A comparison of uncertainty and sensitivity analysis results obtained with random and Latin hypercube sampling[J].Reliability Engineering & System Safety,2005,(03):305-330.
  • 10Patelli E,Pradlwarter H J,Schu(e)ller G I. Global sensitivity of structural variability by random sampling[J].Computer Physics Communications,2010,(12):2072-2081.

二级参考文献42

  • 1陈永龙,王玉泉,李世英.使用保障资源的确定方法探讨[J].装甲兵工程学院学报,2003,17(3):59-62. 被引量:13
  • 2郭红芬,刘福胜.利用排队模型优化保障设备数量[J].装甲兵工程学院学报,2005,19(1):29-31. 被引量:18
  • 3陈军 陈永革 王程.基于神经网络的装备保障资源评估.军事交通学院学报,2008,10(4):46-48.
  • 4中国航空工业发展研究中心海军装备部飞机办公室.国外舰载机技术发展:气动、起降、材料、反潜、直升机预警[M].北京:航空工业出版社,2008.
  • 5Waldemar K. Dynamic scheduling state of the art report[R]. SCIS Technical Report T2002:28, 2002.
  • 6Moser I, Hendtlass T. Solving dynamic single-runway aircraft landing problems with extremal optimisation[C]// Proceedings of the 2007 IEEE Symposium on Computa tional Intelligence in Scheduling. 2007:206- 211.
  • 7Malaek S M B, Naderi E. A new scheduling strategy for aircraft landings under dynamic position shifting[C]// Aerospace Conference. 2008 : 1- 8.
  • 8Kouiss K, Pierreval H, Mebarki N. Using multi-agent architecture in FMS for dynamic scheduling[J]. Journal of Intelligent Manufacturing, 1997, 8(1): 41-47.
  • 9Scott J M, Kasin O. Scheduling complex job shops using disjunctive graphs: a cycle elimination procedure[J]. International Journal of Production Research, 2003, 41(5) :981 -994.
  • 10Zhang X D, Wang Q, Li X P. Multi-agent based framework for dynamic scheduling system[C]//Proceedings of the Sixth International Conference on Machine Learning and Cybernetics. 2007:3838 -3843.

共引文献32

同被引文献3

引证文献1

二级引证文献6

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部