Order Forecast of Cigarette Distribution Center

摘要：

The thesis forecasts order of Cigarette Distribution Center by period,brand,district,from several dimensions.The thesis uses different models and analyses forecast results. When forecasting year s order amount,the mean accuracy of Logarithm Regression Model is highest,gets 98.45％.When forecasting month s order amount of cigarette,the thesis uses Genetic Algorithm (GA)to optimize BP neural networks,and overcomes the shortcomings that Neural Networks apt to be trapped in local optimum when searching values of weights 1 . The thesis also uses Regression Models,Grey Model and adaptive Secondary Exponent Smooth Model,The mean accuracy of Linear Regression Model whose effect of forecasting is better is 96.9％. Considering the practical work period of Cigarette Distribution Center and the seasonal influences,when forecasting day s order amount,the thesis introduces PROPERTY_TAG to revise the forecast,improve the accuracy of forecast and easy to realize programming.GA Optimized BP Neural Network Model gets the expected accuracy which is 98％.The mean accuracy of Proportion Model Based-on Lunar Calendar,Regional Trend Model and ARMA Model are 90.87％,95.61％,96.61％ respectively. When forecasting week s order amount of Cigarette,the accuracy of Accumulation of Mean Order Amount per Day Based-on PROPERTY_TAG is 94.15％and cost time is 14s. The thesis develops Order Forecast Web System of Cigarette Distribution Center using Struts framework and makes it possible to share demand information for whole tobacco supply chain. Index Terms -Cigarette Order Forecast;BP Neutral Networks; Self-adaptive Secondary Exponent Smooth Model,The mean accuracy of Linear Regression Model whose effect of forecasting is better is 96.9％. Considering the practical work period of Cigarette Distribution Center and the seasonal influences,when forecasting day s order amount,the thesis introduces PROPERTY_TAG to revise the forecast,improve the accuracy of forecast and easy to realize programming.GA Optimized BP Neural Network Model gets the expected accuracy which is 98％.The mean accuracy of Proportion Model Based-on Lunar Calendar,Regional Trend Model and ARMA Model are 90.87％,95.61％,96.61％ respectively. When forecasting week s order amount of Cigarette,the accuracy of Accumulation of Mean Order Amount per Day Based-on PROPERTY_TAG is 94.15％and cost time is 14s. The thesis develops Order Forecast Web System of Cigarette Distribution Center using Struts framework and makes it possible to share demand information for whole tobacco supply chain.

关键词： Cigarette Order Forecast BP Neutral Networks Genetic Algorithm Struts Framework

作者: Yaohua Wu Zuoling Song Danyu Zhang

作者单位: The Logistics Institute Shandong University Jinan,Shandong Province,China The school of Management Shandong University Jinan,Shandong Province,China

会议类型: 国际会议

会议名称: 2007 IEEE International Conference on Automation and Lofistics

会议地点: 山东济南

会议语种:英文

在线出版日期: 2007-08-18（万方平台首次上网日期，不代表论文的发表时间）