田口方法（Taguchi Method）提供企業一個可以有效提昇品質的模式。然而，多數田口方法的應用只能針對單一品質特性實施參數設定的最佳化。近年來，多準則決策（Multiple Criteria Decision Making, MCDM）方法被廣泛用來解決多重品質特性最佳化（Multi-Response Optimization）問題。 在考量工程人員進行多重品質最佳化問題選擇時，會有如「重要」或「優秀」模糊概念選擇所產生含糊和猶豫的情況。近年來，直覺模糊集合的概念已被發現在處理含糊和猶豫情況比起模糊集合有效。 本文重點在研究系統方法及探索在直覺模糊環境下多重品質最佳化問題，而當中的每個品質回應值的重要程度，由工程師選擇後運用直覺模糊推論所給定。 本研究所提出的方法，用於處理多重品質最佳化問題，可評估各品質參數回應值的直覺模糊集合數據，包含理想解類似度順序偏好法(TOPSIS)、多準則妥協排序法(VIKOR)及相似性測度方法。這些解決方案可減少直覺模糊運算時的複雜度，並提高在直覺模糊環境下的多重品質最佳化問題的效率。 文中提供電漿輔助化學汽化沈積（PECVD）製程和雙邊表面黏著技術電子組裝作業兩個案例，用來驗證研究方法的有效性。這些研究案例顯示所提出的研究方法對於確定最佳因子水準組合是有效率的方案，其不同於過去所提出的多重品質最佳化方法，不僅使用的直覺模糊推論優於模糊推論，在計算速度上也比過去研究更有效率。 ;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.