Applying HBMO-based SOM in Predictingthe Taiwan Steel Demand Fluctuation

• Main Authors: Yu-Ting Wang, 王鈺婷
• Other Authors: 邱垂昱
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/69tj57

碩士 === 國立臺北科技大學 === 工業工程與管理系碩士班 === 101 === The steel industry is an important basis for industrial development, and it’s characteristics are technology-intensive, capital-intensive and energy-intensive, and closely link with its upstream and downstream industries.　Covered the important raw material for livelihood industries. All of those fields are intimately with steel industry. However, Taiwan is the island-oriented country. Although being provided with the geographical advantage, but Taiwan lacks of natural resources.　The majority of steel-making raw materials dependent on imports. With rising transportation cost of raw materials trading around the worldwide markets, this situation will trigger the problem of oil price-hike, leading to the production costs of the steel industry stays high. Many factors will affect steel price, including production cost relationship between supply and demand, and market environment; but dismiss the impact of the environment, the production cost is a direct factor of the price fluctuation. More important, the relationship between supply and demand of steel products is the main factors to influence the trend of the involving prices. The purpose of this study is to combine honey-bee mating algorithms and neural network, then establishing a set of demand fluctuation forecast about steel finished goods. We expect the certainty of the future of the steel product demand situation. Not only to protect the domestic steel market from foreign low-priced competitive strategy, but also to guarantee to the domestic steel of supply chain from middle to downstream. Finally, to create ALL-WIN for whole supply chain of the both sides；to be more precisely, making Taiwan''s iron and steel industry more competitive. To compared HBMO-based SOM with SOM. The experimental results show that HBMO-based SOM with factors selection is better than SOM with all factors, and the model of divided-HBMOSOM can more accurate the Taiwan steel demand fluctuation. Shows different data pre-processing mode will definitely affect the steel demand fluctuation forecast accuracy.