隨著時代的變遷，現實環境中所面臨的決策問題也逐漸擴大，往往包含了多個評估的屬性與可行方案，而屬性的評估方式不再侷限於明確數值的給定，決策者可以透過模糊數或是語意字詞評估該屬性的表現分數，此為模糊多屬性決策(Fuzzy Multiple Attribute Decision Making；FMADM)之問題。然而，面對複雜的決策問題，通常需要一群專家共同參與該決策，群體決策(Group Decision Making；GDM)於是應運而生。由於組織型態的演變，現實環境中之決策團隊往往有階級之分，形成了專家意見重要度相異的情況。根據此類問題，本研究將發展一套方法，用以解決模糊多屬性之決策問題，且適用於階層式群體決策之環境下，並運用模糊集合理論的概念，建構一整合專家意見之兩階段研究流程，第一階段為方案的評估，各專家針對各方案之主觀屬性做評估，並將其模糊性資料做處理，以便後續之整合，第二階段為專家意見之整合，針對各項屬性決定專家之權重，並考慮該專家與各階層專家之意見相似程度，找出整合係數以做為各專家意見的權重，最後計算求得專家意見之整合值。本研究將套用範例，演算各流程的步驟，以驗證本研究所提之方法的可行性，並可以廣泛適用於其他類型之決策問題。

In the real world, decision makers are often faced with complicated decision problems where many attributes and alternatives are considered. An alternative with respect to an attribute can be assessed by crisp data, but decision making usually includes subjective opinions. Experts can use linguistic terms to express their real opinions, and these tend to be fuzzy rather than precise. As a result, various fuzzy multiple attribute decision making (FMADM) methods, which are suitable for group decision making (GDM) problems in a fuzzy environment, have been developed. In this field, we consider a heterogeneous group of experts in a hierarchical structure, and develop a rational fuzzy opinion aggregation model.
In the proposed model, an aggregation technique for a hierarchical group of experts is employed which deals with experts’ fuzzy opinions about the subjective attributes of a decision problem. The aggregation model includes three major states, the initial state, first state and second state. The purpose of the initial state is to form a committee of experts, then identify attributes and alternatives. In the first state, each expert gives performance ratings about alternatives with regard to each subjective attribute. After rating, the fuzzy data should be converted into a standardized form so that it can be calculated later on. In the second state, an aggregation method for group of experts is employed. Two factors are considered in this method. One is the weight of each expert, and it is different for each attribute. Another is the similarity of the opinions between experts. Finally, we combine these two factors linearly and find the aggregation coefficient, which helps to obtain the aggregation result of the fuzzy opinions.
To verify the rationality and feasibility of the proposed method, two cases are used to demonstrate every step in each state. The results show that the proposed model is flexible and can be applied widely to different group decision making problems.

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