理想解相似度順序偏好法(Technique for Order Preference by Similarity to Ideal Solution; TOPSIS) 是一種常被決策者使用之多屬性決策方法，其透過衡量可行方案與理想解之距離，將所有可行方案進行排序，以求得最佳方案。直覺式模糊數(Intuitionistic fuzzy number)包含正、負向資訊及猶豫資訊，若將其應用於TOPSIS決策模式上，能較傳統直接給予明確數值或模糊數的方式得到更多資訊，讓所得到之評估值更能貼近專家們的實際想法。隨著決策問題範圍擴大與複雜性增加，需要多位決策者才能做適當的分析，此外，在TOPSIS決策過程中，專家、屬性權重與正、負理想解之衡量方式眾說紛紜。因此，本研究將建構出應用於直覺式模糊環境下之群體TOPSIS決策方法，提高決策結果的真實性與合理性。
本研究將使用直覺式模糊數作為評估值，並將決策模式分為兩個部分。初始整合階段為尋找參與決策之專家，接著透過專家集體討論的方式，共同決定評估屬性與可行方案，並各自對方案與屬性進行評估，然後利用熵值(Entropy)求得個別專家之權重，再進行專家意見整合；直覺式模糊TOPSIS決策階段則利用整合後之決策矩陣計算各屬性之權重，並將屬性權重與專家意見整合，然後進行TOPSIS決策程序，先使用雙重兩極性決定正、負理想解，再利用投影法對可行方案進行排序，找到最佳方案。最後以本研究之決策模式進行範例演算，分別與Boran et al.(2009)和Xu and Hu(2010)進行比較與分析，其中包含敏感度分析與穩健性之測試，最後得知本研究之決策模式能得到較合適之決策結果，以供未來決策者參考。

Technique for Order Preference by Similarity to Ideal Solution(TOPSIS) is a kind of multiple-attribute decision-making method often used by decision-makers. If intuitionistic fuzzy numbers, which contain positive, negative, and hesitation information are applied to a TOPSIS decision-making model, more information can be obtained and the evaluation results can be closer to the actual ideas of experts. In addition, since decision-making problems have become more complex, multiple decision-makers are required to make an appropriate analysis. Therefore, this study constructs a group TOPSIS decision-making method for an intuitionistic fuzzy environment to improve the authenticity and rationality of decision-making results.
This research adopts intuitionistic fuzzy numbers in a two-phase decision-making model.In the initial integration phase, experts collectively decide and then evaluate the attributes and alternatives. Then, entropy is used to obtain the weights of individual experts, and expert opinions are integrated. In the intuitionistic fuzzy TOPSIS decision-making phase, an integrated decision matrix is used to calculate the weights of individual attributes. The attribute weights are integrated with expert opinions, and then dual bipolar are used to determine the positive and negative ideal solutions. The projection method is adopted to rank the alternatives and find the best solution. Two examples are presented to illustrate the procedure of the proposed method and to compare the sorting results obtained using various models.

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