Applying GA-based Time Series in Predicting the Taiwan Steel Demand Fluctuation

• Main Authors: Pei-Chun Cheng, 鄭佩君
• Other Authors: 邱垂昱
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/c3xkf4

碩士 === 國立臺北科技大學 === 工業工程與管理系碩士班 === 102 === Development of steel industry affected by global markets, are the basis of economic and industrial development, although a direct factor causing changes in steel prices is the cost of production, but the relationship between supply and demand is an important factor affecting the price trend. Therefore, the primary task of the domestic steel industry, which is to maintain the supply-demand balance of steel, the only certainty on the future of the steel situation analysis in order to protect the domestic steel industry from low marketing strategy abroad, while middle and lower reaches of the domestic demand for steel also can borrow this is guaranteed, and create mutual benefits of both supply and demand situation, so that Taiwan''s steel industry more competitive. The experimental results show that our proposed multi-attribute genetic algorithm combined with the weight of the moving average method is superior to the right than the prediction model prediction model and a single attribute moving average single attribute genetic algorithm combined with the weighted moving average method, and enhance 4 % to 5% of the prediction error; while in the second phase of multiple properties combined with the genetic algorithm results and weight of the moving average method right after the interval and Price comparison accuracy literature, in the hot-rolled stainless steel coils and steel, the interval study are 45.00% accuracy rate, and superior results literature. Shows that the accuracy rate is calculated in a linear fashion up the decline of demand for steel products, thereby enhancing the accuracy range of steel products demand.