基于灰色加权马尔可夫的备件需求预测  

Spare Parts Demand Forecasting Based on the Gray Weighted Markov Method

作　　者：王铁宁[1] 朱域[1] 陈晓晨 

机构地区：[1]装甲兵工程学院技术保障工程系 [2]65527部队

出　　处：《装甲兵工程学院学报》2015年第3期8-13,共6页Journal of Academy of Armored Force Engineering

基　　金：军队科研计划项目

摘　　要：针对备件需求预测中数据波动性大,且多集中于较低数量水平,使得预测值偏差较大的特点,首先应用备件需求量的历史统计数据,建立备件灰色预测模型,并根据数据的实际趋势提出了状态动态划分方法,然后运用加权马尔科夫方法对灰色预测模型的预测结果进行了修正,并通过实例进行了验证,结果表明:应用灰色加权马尔可夫预测方法预测备件需求是可行的。When forecasting the demand of the spare parts,it can clearly understand the demand data with the characteristics of large fluctuation,focusing on the low number,which can easily lead to great deviation between the predicted and the actual value.So,in view of the characteristics and the law of the historical demand data,according to the demand of equipment of the historical statistical data,it firstly establishes grey forecasting model,and made the division method of the state according to the real trend of the data,then,uses the weighted Markov prediction model to modify the results,to get a more accu-rate prediction results.Through the example,it proves that the grey weighted Markov prediction method is feasible to be used for spare parts demand forecasting.

关 键 词：备件 需求预测 灰色预测模型 加权马尔可夫预测模型 

分 类 号：E92[军事—军事装备学]