期刊
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 161, 期 1, 页码 275-284出版社
ELSEVIER
DOI: 10.1016/j.ejor.2002.09.001
关键词
forecasting; fuzzy inference system; neuro-fuzzy model; textile logistics
In order to reduce their stocks and to limit stock out, textile companies require specific and accurate sale forecasting systems. More especially, textile distribution involves different forecast lead times: mean-term (one year) and short-term (one week in average). This paper presents two new complementary forecasting models, appropriate to textile market requirements. The first model (AHFCCX) allows to automatically obtain mean-term forecasting by using fuzzy techniques to quantify influence of explanatory variables. The second one (SAMANFIS), based on a neuro-fuzzy method, performs short-term forecasting by readjusting mean-term model forecasts from load real sales. To evaluate forecasts accuracy.. our models and classical ones are compared to 322 real items sales series of an important ready to wear distributor. (C) 2003 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据