摘要
该文针对电力物资仓储管理中存在的物资储备决策难题,提出一种基于深度学习技术的智能电力物资储备方案编制工具。该工具采用EIQ-ABC分类法和储备定额模型,结合深度学习算法,对物资储备方案进行系统化设计和管理。通过在某供电公司区域仓的试点应用,与传统物资储备管理方法相比,新工具在提高需求预测精度和库存控制策略的有效性方面展现出显著优势。研究结果证实:EIQ-ABC分类法能为仓储管理提供坚实的数据支撑,且基于时间序列的深度学习预测模型显著提升储备方案的预测准确性。这一新型方法有助于推动电力物资仓储管理向更加智能化及合理化的方向发展。
This article proposes an intelligent power material reserve planning tool based on deep learning technology to address the problem of material reserve decision-making in power material warehousing management.This tool adopts the EIQ-ABC classification method and reserve quota model,combined with deep learning algorithms,to systematically design and manage material reserve plans.Through the pilot application of a regional warehouse of a power supply company,the new tool has shown significant advantages in improving the accuracy of demand forecasting and the effectiveness of inventory control strategies compared to traditional material reserve management methods.The research results confirm that the EIQ-ABC analysis method can provide solid data support for warehouse management,and the deep learning prediction model based on time series significantly improves the accuracy of reserve plan prediction.This new method will help promote the development of more intelligent and rational management of power material warehousing.
作者
柴利达
刘江
田行健
CHAI Lida;LIU Jiang;TIAN Xingjian(Guizhou Power Grid Co.,Ltd.,Guiyang 550002,China)
出处
《数字通信世界》
2024年第11期180-183,共4页
Digital Communication World