A multi-tiered spare parts inventory forecasting system

被引:0
|
作者
Xie, Zhuoqing [1 ]
Xie, Hongzhi [2 ]
Liu, Shuhui [2 ]
Huang, Yijing [1 ]
Deng, Xiaolin [2 ]
Liu, Biwei [1 ]
机构
[1] Shenzhen Univ, Coll Comp Sci & Software Engn, Natl Engn Lab Big Data Syst Comp Technol, Shenzhen 518060, Peoples R China
[2] China Nucl Power Operat Co Ltd, Spare Parts Ctr, Shenzhen 516545, Peoples R China
关键词
inventory forecasting; spare parts forecasting; deep learning; quantitative techniques; multi-layer; INTERMITTENT DEMAND;
D O I
10.1504/IJSSC.2023.133248
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
To achieve intelligent nuclear power management, we propose a multilevel nuclear power spare parts prediction method. We combine qualitative prediction methods with various cutting-edge quantitative prediction methods to forecast the overall inventory of nuclear power spare parts and individual categories, enabling enterprises to minimise costs and prevent stockouts. Specifically, we classified the spare parts according to their usage characteristics and developed a hybrid model that integrates the CNN+BiLSTM and DLinear models (Zeng et al., 2022), taking into account expert opinions for each category. Experimental results show a significant improvement in accuracy compared to traditional methods.
引用
收藏
页码:165 / 172
页数:9
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