王清朝. GM(1,1)模型在铁路装卸运输综合生产效率预测中的应用[J]. 油气储运, 2013, 32(11): 1198-1201. DOI: 10.6047/j.issn.1000-8241.2013.11.011
引用本文: 王清朝. GM(1,1)模型在铁路装卸运输综合生产效率预测中的应用[J]. 油气储运, 2013, 32(11): 1198-1201. DOI: 10.6047/j.issn.1000-8241.2013.11.011
Wang Qingchao. Application of GM (1, 1) in comprehensive production efficiency prediction for railway loading, unloading and transportation[J]. Oil & Gas Storage and Transportation, 2013, 32(11): 1198-1201. DOI: 10.6047/j.issn.1000-8241.2013.11.011
Citation: Wang Qingchao. Application of GM (1, 1) in comprehensive production efficiency prediction for railway loading, unloading and transportation[J]. Oil & Gas Storage and Transportation, 2013, 32(11): 1198-1201. DOI: 10.6047/j.issn.1000-8241.2013.11.011

GM(1,1)模型在铁路装卸运输综合生产效率预测中的应用

Application of GM (1, 1) in comprehensive production efficiency prediction for railway loading, unloading and transportation

  • 摘要: 综合生产效率是影响铁路油气装卸与运输系统安全、经济和高效运行的重要因素。为了对铁路装卸与运输系统的综合生产效率进行预测,以洛阳石化铁路装卸运输系统2006-2012年生产年度报表的统计数据为基础数据,进行一次累加生成处理;在此基础上建立灰色动态模型GM(1,1),对求解得到的结果进行累减还原处理,得到灰色预测函数;运用预测函数对2013-2014年的综合生产效率进行预测,并对预测结果进行精确度检验计算,检验精度达到1级;给出了2013-2014年针对性的预测数据结论,以及运用GM(1,1)模型在不增加数据长度条件下减少误差的预测方法。实例应用证明:该方法具有预测模型简单、预测精度高等优点,对实际生产管理具有较强的指导意义。

     

    Abstract: Comprehensive production efficiency is an important factor that affects the safe, efficient and economic operationof oil and gas railway loading, unloading and transportation system. In order to predict the comprehensive production efficiency of railway loading, unloading and transportation system, this paper takes the statistical data in annual productionreport of Luoyang Petrochemical Engineering Corporation from 2006 to 2012 as the basic data and sums them up. On thatbasis, this paper builds a gray dynamic model GM (1, 1) and solves the model, then processes the data by regressive reductiontreatment and the grey prediction function is obtained. The annual comprehensive production efficiency from 2013 to 2014was predicted by the predictive function and the accuracy of predictive result was tested which reaches the first rate. In thispaper, the predictive data conclusion of 2013 to 2014 is listed out and the predictive method by GM (1, 1) for reducing errorwithout increasing data length is also proposed. Case study has proved that this method, with simple predictive model andhigh prediction accuracy, is instructive to practical production management.

     

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