摘要
安全库存是一种额外持有的库存,它作为一种缓冲器用来补偿在订货提前期内实际需求超过期望需求量或实际提前期超过期望提前期所产生的需求。在服装企业中一般凭经验来设定安全库存,但实际效果不佳。应用人工神经网络,建立BP神经网络模型,用多个影响安全库存的指标及安全库存对网络进行训练,以达到对安全库存量预测的目的。经验证和预测效果十分理想。
Safety-goods-stockpile is a buffer to compensate the storage when actual needs exceed anticipant demands. Some dressing corporations set up Safety Goods Stockpile based on the experience, which leads to an undesirable effect. The goal of forecast Safety Goods Stockpile can be achieved by using Artificial Neural Networks to structure BP model and training the net with Safety Goods
Stocknile and many elements which affect the Safety Goods Stockpile. Application to this experiment is very effective.
出处
《计算机工程与设计》
CSCD
北大核心
2005年第12期3453-3455,共3页
Computer Engineering and Design
关键词
人工神经网络
BP模型
安全库存
artificial neural networks
BP model
safety goods stockpile
