A Study of Evaluation Model for Introducing New Equipments under Fuzzy Environment

• Main Authors: FU-HSIUNG CHEN, 陳富雄
• Other Authors: Tom M. Y. Lin
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/14434554763132409668

博士 === 國立臺灣科技大學 === 管理研究所 === 100 === Recently, the product life cycles are becoming shorter due to the rapid development of science and technology. In order to enhance the competitiveness, customer satisfaction and profitability, the enterprises always take a great effort to evaluate and select the optimal investment proposals. Although new technology products can be used to improve productivity, customer satisfaction and profitability, the decisions for introducing new equipments need to be more careful. Selection of new equipment is one of problem decision-making, involving the company's operating costs and benefits. It will affect the growth of the enterprise in a competitive environment. The traditional decision-making, rather than considering the cost, cannot satisfy the quality of strategy goal. This study applies the concept of simple additive weighting under fuzzy environment, a method of multi-attribute decision-making. We propose a model to evaluate the new equipment proposal take a balance between quality and cost. The model studied considers the cost of investment, and also combines the corporate strategy objectives. The model makes the overall evaluation and support the decision-maker to choose the best investment project. This study also proposes a case study, introducing a metropolitan fiber optic network. The judgmental decision-making process is divided into three stages: (1) Feasibility of the alternatives: to measure the number of feasible solutions. (2) Attribute selection and weight allocation: the decision makers have different preferences for different attributes, each attribute will be allocated with different weight and finally we normalize the weights. (3) Selection: According to different distance transmitted, we select the optimal solution. This study proposes a decision-making process, which closer to the actual situation. The process used is more direct, effective, and can be extended to other problems such as personnel selection, supplier evaluation and other issues.