Applying Fuzzy Multi-Attribute Decision Making to Select Wet Process Equipment for TFT-LCD Manufacturing

• Main Authors: Meng-Hsiu Yu, 余孟修
• Other Authors: 徐旭昇
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/56103563385012718641

碩士 === 元智大學 === 工業工程與管理學系 === 99 === In the past decades, TFT-LCD manufacturing is one of the most important high-tech industries in Taiwan. To increase competitiveness, every individual corporation strives to promote its market share and total profit. Using high quality equipment for the TFT-LCD manufacturing process is crucial in achieving this goal, since good equipment will ensure efficiency and reliability of the production process. The wet process in TFT-LCD manufacturing is an important stage that determines product quality. Selection of wet process equipment is essential, since the equipment is costly, subject to long-term use, and an imperative part of the production line. In this research, we study a wet process equipment purchase decision problem for a major LCD manufacturing company in Taiwan, and employ fuzzy multi-attribute decision making method to solve the problem. The decision process integrates two methods: fuzzy analytic hierarchy process (FAHP) and fuzzy TOPSIS, where FAHP is applied to determine the relative weights between essential attributes in purchasing equipments, and fuzzy TOPSIS is employed to evaluate each candidate’s equipment performance. In the study, the information obtained from five department managers, 30 senior engineers, and relevant articles are used to establish a two-level attribute hierarchical structure. Three sample suppliers were selected for the case study. Supplier candidate 1 is a Korean company, candidate 2 is a Japanese company, and candidate 3 is a Taiwanese company. The hierarchical structure has five major attributes (consideration factors): quality, cost, service, techniques, and safety; each major attribute comprises three to four sub-attributes. Each department manager will respectively determine his own weights for major attributes and sub-attributes, and evaluate the performance of each candidate supplier. The factory director is requested to assign his weighted values on the five managers’ opinions. As a consequence, each candidate supplier will receive a fuzzy score for his overall equipment performance evaluation. Five defuzzification methods are used to rank three candidates. Four of the methods conclude the same rank, and one method can only conclude rank 1 candidate, but is inconclusive in ranking the remaining two candidates.