文档介绍：摘要
备件管理是设备管理工作的一项重要内容,它与企业的生产运行绩效和经济效益
密切相关。既满足设备维护检修需要,又合理占用资金是备件库存管理的基本原则,
准确的备件需求预测对于备件库存控制与优化十分重要。
不常用备件具有使用频次低、间隔期长且需求不确定等特征,其历史需求数据的
样本量十分有限,使得该类备件需求的预测非常困难。以样本数目趋于无穷大的渐
进理论为基础的传统统计学预测方法,不能很好地解决这个问题。
基于此,本文研究了基于支持向量机的不常用备件需求预测方法。首先对不常
用备件所具有的间续性需求特征进行了描述与界定,并分析了指数平滑法、Croston
方法、Bootstrap 方法等常用的间续性需求预测方法的基本思想与不足之处。然后引
入了基于有限样本统计学习理论的支持向量机预测方法,对线性和非线性支持向量
机回归(SVR)算法进行了说明。接着,结合不常用备件的特点,提出了两种基于
SVR 的不常用备件需求预测方法:基于时间序列的 SVR 预测和基于影响因素的 SVR
预测,对其基本步骤和框架以及需求预测结果评价机制进行了探讨,并通过一个实
例研究,验证了这两种方法的有效性。最后,结合预测支持系统(FSS)的相关理论,
研究了基于 SVR 的不常用备件需求预测 FSS 系统。
关键词:不常用备件间续性需求预测支持向量机预测支持系统
I
Abstract
Spare parts management is one of the important tasks of equipment management,
which ties up with the operating performance and economical efficiency of the enterprise.
The basic principle of spare parts inventory management is not only to fulfill the
requirement of equipment maintenance and overhaul, but also to occupy reasonable
capital. Accurate forecast of spare parts demand is crucial for spare parts inventory control
and optimization.
Rarely used spare parts with intermittent demand which appears at random, with
many time periods having no demand, have very few demand data samples. So it is very
difficult to forecast the demand of spare parts of this type. Forecasting methods based on
traditional statistics can not solve this problem properly. According to this, the thesis does
some research on support vector machines(SVMs)based forecasting method for rarely
used spare parts demand.
First of all, the intermittent demand characteristics of rarely used spare parts are
described, and then the basic ideas and drawbacks of the intermittent demand forecasting
methods mon use such as exponential smoothing, Croston method and Bootstrap
method etc. are analyzed.
Secondly, the forecasting method of support vector machines which based on the
statistical learning theory is introduced. And the linear and nonlinear support vector