理想解相似度順序偏好法(Technique for Order Preference by Similarity to Ideal Solution；TOPSIS)是一項常被決策者所使用的多屬性決策方法，透過衡量可行方案於屬性下之評估值與理想解的距離，對可行方案作出排序。而在過去文獻中，大多是以給予明確值或語意變數作為評估的方式，再將其轉換成模糊數(Fuzzy number)。再者，隨著決策範圍擴大，過去單一決策者分析決斷的模式已漸漸不敷使用，此外，TOPSIS決策過程中，專家、評估屬性的權重衡量方式與正、負理想解之定義眾說紛紜。為能更充分的表達專家意見之不確定資訊，本研究在多屬性決策問題中加入群體決策的概念，並利用直覺式模糊數做為專家衡量可行方案於屬性下之評估值，而建構出直覺式模糊環境下群體TOPSIS決策模式。藉由直覺式模糊數中的正向資訊、負向資訊及猶豫資訊，來模擬人類思維中不確定性的部分，以提高專家評估上的真實性。
本研究使用直覺式模糊數作為評估值，並將模式分為三個部分。初始階段專家討論決定出可行方案與屬性後，各自對可行方案與屬性進行評估，再根據評估矩陣計算專家間共識程度；前整合部分，先進行整合專家意見，透過熵值(Entropy)的概念分別計算出專家之權重後，再整合專家意見，最後進行TOPSIS評選程序，利用得到得接近係數對可行方案排序，以評選出最佳方案；後整合部分，專家分別先進行TOPSIS評選程序，得到各專家之接近係數，再利用專家權重進行整合，最後以整合後接近係數對可行方案進行排序，挑選出最佳方案。最後以本研究之決策模式進行範例演算，再與Boran et al.(2009)進行比較，本研究之決策模式在決策過程提供較多資訊且得到合理之排序結果，此外，利用專家間共識程度高低判斷出決策模式前整合與後整合適合的使用時機。

The “Technique for Oder Preference by Similarity to Ideal Solution (TOPSIS)” is a useful tool for dealing with multi-attribute decision-making (MADM) problems. Most of the related studies use crisp values or linguistic variables to describe the experts’ opinions that are needed in TOPSIS, and then transform these linguistic variables into fuzzy numbers. In this study we use intuitionistic fuzzy numbers to increase the accuracy of this process. In addition, when the scope of decision-making broadens, relying on the opinion of only one decision-maker can produce inadequate results. To address this issue, this research adds a group decision-making approach into MADM and constructs a group decision-making model under an intuitionistic fuzzy environment.
This model consists of the following three parts: (1) Using the group decision method to collect the experts’ opinions and calculate the degree of consensus among them. (2) Applying model 1 (the first aggregation): aggregating the experts’ linguistic opinions before carrying out TOPSIS process. (3) Applying model 2 (the last aggregation): the aggregating experts’ closeness coefficients after the TOPSIS process. Four examples are then used to demonstrate the rationality and superiority of the proposed model by comparing it with the method used in Boran et al. (2009). The proposed method can provide more information for use in the decision-making process, and obtained more reasonable results. In addition, we can determine which model is more suitable for use with a particular problem based on the degree of consensus among experts.

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