Spares Consumption Combination Forecasting Based on Genetic Algorithm and Exponential Smoothing Method

Abstract

In view of the characteristics that the linear exponential smoothing, secondary exponential smoothing, cubic exponential smoothing had different fitting degree when predicted the spares with the different consumption discipline, optimized results of these three methods through the combination prediction model, and solved it by genetic algorithm and used the obtained results with minimum error as spares consumption quota. the prediction results show that the model predicts accurately, with high utility and promotion.