The Study of Optimizing Multi-response Problems with Intuitionistic Fuzzy Set

• Main Authors: Jen-pin Peng, 彭仁賓
• Other Authors: Wei-ching Yeh
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/79486084912217317899

博士 === 國立中央大學 === 機械工程學系 === 103 === The Taguchi method provides an effective framework for improving quality in industry. However, it determines the optimal setting of process parameters according to only single response. For the sake of optimizing multi-response problems, multiple criteria decision making (MCDM) methods have been extensively utilized in recent years. In considering an engineer's opinion in optimizing a multi-response problem, it must be paid to vagueness and hesitancy in revealing his or her perceptions of a fuzzy concept such as 'importance' or 'excellence'. Recently, the notion of intuitionistic fuzzy sets (IFSs) has been found to be more effective than that of fuzzy sets for dealing with vagueness and hesitancy. This thesis focuses on state systems and explores optimization of multi-response problems with IFSs, in which the importance of each response is given by an engineer as IFS. In the proposed methods, the TOPSIS method, VIKOR method and the similarity measure method are proposed for optimizing multi-response problems, where the weight of various responses are assessed in terms of IFSs. This scheme can eliminate the need for complicated intuitionistic fuzzy arithmetic operations and increase the efficiency of solving multi- response optimization problems in intuitionistic fuzzy environments. Two case studies of plasma-enhanced chemical vapor deposition (PECVD) and double-sided surface mount technology electronic assembly operation are used to demonstrate the effectiveness of the proposed methods. These case studies show that the proposed methods are useful schemes to efficiently determine the optimal factor-level combination. The proposed methods differ from previous approaches for optimizing multi-response problems, not only in that the proposed methods use IFSs rather than fuzzy sets, but also in that the calculations are more efficient.