A case study of silicon wafer slicing. (14th May 2007) - Chang, Che-Wei; Wu, Cheng-Ru; Lin, Chin-Tsai; Chen, Huang-Chu
Many decision-making and problem-solving tasks are too complex to be understood quantitatively; however, people succeed by using knowledge, that is, imprecise rather than precise. Fuzzy set theory resembles human reasoning in its use of approximate information and uncertainty to generate decisions. It was specifically designed to mathematically represent uncertainty and vagueness and provide formalised tools for dealing with the imprecision intrinsic to many problems. Though the purpose of AHP is to capture the expert's knowledge, the conventional AHP still cannot reflect the human thinking style. Therefore, fuzzy AHP, a fuzzy extension of AHP, was developed to solve the hierarchical fuzzy problems. The decision maker can specify preferences in the form of natural language expressions about the importance of each performance attribute. This paper establish model of fuzzy AHP, and use Simple Additive Weighting (SAW) and Grey Relation Analysis (GRA) select the evaluation outcomes and evaluate the optimal performing machine in terms of precision. Finally, results of the Exponential Weighted Moving Average (EWMA) control chart demonstrate the feasibility of the proposed fuzzy AHP-based algorithm in effectively selecting the evaluation outcomes and evaluating the precision of the optimal performing machines. So, through collected data (the quality and quantity) to judge the result by AHP, it will be the key to help the engineer to find out the manufacturing process yield quickly and effectively.