首页|基于改进Croston方法的多需求模式零备件预测

基于改进Croston方法的多需求模式零备件预测

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维修备件管理是提高产线可靠性、实现降本增效的关键.针对具备间歇性与随机性特征的维修备件需求预测问题,提出了基于改进Croston方法的备件需求预测模型.依据Syntetos准则基于间断性与波动性特征将备件需求划分为4类.针对含有波动性特征的需求,基于Croston方法主要思想将备件需求预测分解为需求发生状态预测和需求量预测两类问题,设计了 集合经验模态分解(ensemble empirical mode decomposition,EEMD)-长短期记忆网络集成(long short-term memory,LSTM)预测模型.EEMD方法将剧烈波动序列分解为若干相对平稳的分量,进而采用LSTM方法对各分量进行预测.针对含有间断性特征的需求,引入信号处理技术中的信号调制技术,将需求发生状态0-1二值序列进行连续化处理.所提方法解决了备件需求波动性强、间断性大的难题,已应用于湖北中烟武汉卷烟厂,证明了方法的优越性与可行性.
Spare Parts Prediction in Multi-demand Mode Based on Improved Croston Method
Spare parts management is the key to improving the reliability of production lines and achieving cost reduction and production efficiency.A spare parts demand forecasting model based on the improved Croston method was proposed for spare parts demand with intermittent and random characteristics.Based on the Syntetos criterion,the spare parts demand was classified into four categories by intermittent and fluctuating characteristics.For demands with fluctuating characteristics,spare parts demand prediction was decomposed into two types of problems:demand occurrence state prediction and demand quantity prediction,and an ensemble empirical mode decomposition(EEMD)-long short-term memory(LSTM)prediction model was designed for it.The EEMD method was applied to decompose the fluctuating sequence into several relatively smooth components,and then the LSTM method was used to forecast each component.For demands with intermittent characteristics,a signal modulation technique was introduced to serialize the binary sequence of occurrence states.The proposed method solves the problems of strong fluctuation and intermittency of spare parts demand.It has been applied to China Tobacco Hubei Industrial Co.,Ltd.,which proved the superiority and feasibility of the method.

spare parts demand forecastmulti-demand modeCroston methodensemble empirical modal decompositionlong short-term memory network

杨华强、熊坚、张鹏、范宜静、韩冬阳、曹蕾、夏唐斌

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湖北中烟工业有限责任公司,武汉 430020

上海交通大学机械与动力工程学院,上海 200240

备件需求预测 多需求模式 Croston方法 集合经验模态分解 长短期记忆网络

上海市"科技创新行动计划"自然科学基金湖北中烟工业有限责任公司合作项目

20ZR14286002022JSGY3SC2B033

2024

10.12404/j.issn.1671-1815.2305544

科学技术与工程
中国技术经济学会

科学技术与工程

CSTPCD北大核心
影响因子：0.338
ISSN：1671-1815
年,卷(期)：2024.24(21)