Developing a Hybrid Model for Alternatives Ranking of Plastic Injection Molded Products

• Main Authors: Rou-Jun Ye, 葉柔君
• Other Authors: 車振華
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/ndh3ta

碩士 === 國立臺北科技大學 === 工業工程與管理系碩士班 === 100 === In recent years, the energy resources shortage and the effect of greenhouse is paid attention gradually. Injection plastic products are subjects to frequent variability due to the environment is fast changed, and the products life cycle is constantly short for the global industry. The enterprises must provide the new products to fulfill the present and future consumer market constantly, to obtain profits and competitiveness. For this reason, the study focuses on the neural network to solve the problem concerned with cost, carbon content and recycling estimation for new products for the plastic injection molding industry. First, the study the Pearson Product-Moment Correlation was used to solve the problem concerned with to find the relatedness of Original variable data in order find the optimum forecast model of neural network. The utilization of optimum forecast model of neural network was aimed at precise estimation of production cost, carbon content and recycling / reuse for plastic injection molding, expected to timely provide quickly correct information for the business. Second, in order to understand the new product''s performance evaluation program, this study focuses on the Decision Ball Models to develop the performance evaluation model for the new product. With the analysis provided above, We expect to help enterprises to select the appropriate product which can fulfill the green environmental protection and economic benefits simultaneously.