The intuitionistic fuzzy distance and similarity measures for the informational importance of attributes

• Main Authors: Young Bin Wang, 王洋彬
• Other Authors: T. Y. Chen
• Format: Others
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/80909871664409059807

碩士 === 長庚大學 === 企業管理研究所 === 98 === In Multiple Attribute Decision Making (MADM), how to assess the attribute weight properly is very important. Because different weight distribution would cause totally different decision result. In recent years, the IFS was applied to solve MADM problems for getting accurate information, but it makes our data and decision matrix get more complicated and contain more uncertainty. Therefore, it is such an important issue to make sure of the credibility of data and make correct judgement by itself. However, the research on MADM with the credibility of data is little in the past. In our research, we test Entropy Method, which is an objective weight method that being utilized most under intuitionistic fuzzy environment. We utilize the nature of IF entropy to assess the attribute weight based on the distance and similarity measures instead of standard step of information. According to the experiment result, we discover that there has no effect on the weight value among different measures although it seems complex between two formulas. Besides, we always think more complicated decision matrix would cause larger different result, because of the experiment, we understand this perspective is not correct. There has entirely different situation in the decision matrix which is composed of some specific attributes and alternatives, even in the small size.