模糊多屬性群體決策是一個重要的研究課題。在本論文中，我們根據區間Type-2模糊集合及可能性比較關係提出五個新方法以作模糊決策及模糊多屬性群體決策。在本論文的第一個方法中，我們根據可能性比較關係提出一個新的模糊決策方法。首先，我們介紹區間可能性比較關係的觀念。然後，我們提出type-1模糊集合及區間type-2模糊集合可能性比較關係的觀念。然後，我們根據所提模糊集合的可能性比較關係及使用模糊目標提出一個排序模糊集合的新方法。最後，我們根據模糊集合的可能性比較關係及所提的模糊排序方法提出一個新的模糊決策方法。我們所提之模糊決策方法有評估值可以表示成實數、區間、type-1模糊集合、區間type-2模糊集合的優點。它可以克服目前已存在的方法不能處理區間type-2模糊集合的排序且在某些情況下無法區別模糊集合的排序之缺點。在本論文的第二個方法中，我們根據區間type-2模糊集合的排序值及算術運算，提出一個作模糊多屬性群體決策之新方法。首先，我們提出區間type-2模糊集合的算術運算。然後，我們提出一個計算區間type-2模糊集合排序值的模糊排序方法。我們也將所提方法和存在方法的排序值做一個比較。根據我們所提之模糊排序法及我們所提之區間type-2模糊集合的算術運算，我們提出一個新方法以處理模糊多屬性群體決策問題。由於我們是使用區間type-2模糊集合而不是傳統的type-1模糊集合以表示評估值及屬性的權重，我們所提之方法可以更有彈性及更有智慧的處理模糊多屬性群體決策問題。在本論文的第三個方法中，我們根據區間type-2模糊集合提出一個新的區間type-2 TOPSIS方法以處理模糊多屬性群體決策問題。我們提出一個新的模糊排序方法以計算區間type-2模糊集合的排序值。我們也使用一些範例以說明我們所提之模糊多屬性群體決策方法作模糊多屬性群體決策的過程。由於我們使用區間type-2模糊集合而不是傳統的type-1模糊集合以表示評估值及屬性的權重，我們所提之方法可以更有彈性及更有智慧的處理模糊多屬性群體決策問題。在本論文的第四個方法中，我們根據區間type-2模糊集合的算數運算及模糊偏好關係提出一個模糊多屬性階層群體決策的新方法。因為我們所提之方法的時間複雜度為O(nk)，其中n 為屬性的個數且k為決策者的個數，它比目前已存在的方法更有效率。我們所提之方法可以克服目前已存在方法之缺點此乃因它可以處理非正規區間type-2模糊集合所表示的評估值。我們所提之方法提供我們一個有用的方法以處理模糊多屬性階層群體決策問題。在本論文的第五個方法中，我們根據我們所提之區間語義標記的可能性比較關係及區間語義標記有序權重平均運算子提出一個整合區間語義標記的方法及一致性測量。首先，我們提出區間語義標記可能性比較關係的觀念。然後，我們提出區間語義標記有序權重平均運算子以整合區間語義標記。根據所提區間語義標記可能性比較關係及區間語義標記有序權重平均運算子，我們提出一個整合區間語義標記的方法及一致性測量。我們所提的方法可以克服目前已存在方法的缺點。它提供我們一個有用的方法以整合區間語義標記及作一致性測量。

Fuzzy multiple attributes group decision making is an important research topic. In this dissertation, we present five new methods for fuzzy decision making and fuzzy multiple attributes group decision making based on interval type-2 fuzzy sets and likelihood-based comparison relations. In the first method of this dissertation, we present a new fuzzy decision making method based on likelihood-based comparison relations. First, we introduce the concepts of likelihood-based comparison relations for intervals. Then, we propose the concept of likelihood-based comparison relations for type-1 fuzzy sets and interval type-2 fuzzy sets. Then, we present a new method to rank fuzzy sets by using fuzzy targets based on the proposed likelihood-based comparison relations for fuzzy sets. Finally, we present a new fuzzy decision making method based on the proposed likelihood-based comparison relations for fuzzy sets and the proposed fuzzy ranking method. The proposed fuzzy decision making method has the advantage that the evaluated values can either be represented by crisp values, intervals, type-1 fuzzy sets or interval type-2 fuzzy sets. It can overcome the drawbacks of the existing methods due to the fact that the existing methods can not deal with the ranking of interval type-2 fuzzy sets for fuzzy decision making and can not distinguish the ranking order between the alternatives in some situations. In the second method of this dissertation, we present a new method for fuzzy multiple attributes group decision making based on the ranking values and the arithmetic operations of interval type-2 fuzzy sets. First, we present the arithmetic operations between interval type-2 fuzzy sets. Then, we present a new fuzzy ranking method to calculate the ranking values of interval type-2 fuzzy sets. We also make a comparison of the ranking values of the proposed method with the existing methods. Based on the proposed fuzzy ranking method and the proposed arithmetic operations between interval type-2 fuzzy sets, we present a new method to handle fuzzy multiple attributes group decision making problems. The proposed method provides us with a useful way to handle fuzzy multiple attributes group decision making problems in a more flexible and more intelligent manner due to the fact that it uses interval type-2 fuzzy sets rather than traditional type-1 fuzzy sets to represent the evaluating values and the weights of attributes. In the third method of this dissertation, we present a new interval type-2 TOPSIS method to handle fuzzy multiple attributes group decision making problems based on interval type-2 fuzzy sets. We present a new fuzzy ranking method to calculate the ranking values of interval type-2 fuzzy sets. We also use some examples to illustrate the fuzzy multiple attributes group decision making process of the proposed method. The proposed method provides us with a useful way to handle fuzzy multiple attributes group decision making problems in a more flexible and more intelligent manner due to the fact that it uses interval type-2 fuzzy sets rather than traditional type-2 fuzzy sets to represent the evaluating values and the weights of the attributes. In the fourth method of this dissertation, we present a new method for fuzzy multiple criteria hierarchical group decision making based on arithmetic operations and fuzzy preference relations of interval type-2 fuzzy sets. Because the time complexity of the proposed method is O(nk), where n is the number of criteria and k is the number of decision-makers, it is more efficient than the existing methods. Moreover, the proposed method can overcome the drawback of the existing method due to the fact that it can handle evaluating values represented by nonnormal interval type-2 fuzzy sets. The proposed method provides us with a useful way to handle fuzzy multiple criteria hierarchical group decision making problems. In the fifth method of this dissertation, we present a new method for interval linguistic labels aggregation and consensus measure for autocratic decision making using group recommendations based on the likelihood-based comparison relations of interval linguistic labels and the proposed Interval Linguistic Labels Ordered Weighted Average (ILLOWA) operator. First, we propose the concepts of likelihood-based comparison relations of interval linguistic labels. Then, propose the ILLOWA operator to aggregate interval linguistic labels. Based on the likelihood-based comparison relations of interval linguistic labels and the proposed ILLOWA operator, we propose a new method for interval linguistic labels aggregation and consensus measure for autocratic decision making using group recommendations. The proposed method can overcome the drawbacks of existing methods. It provides us with a useful way for interval linguistic labels aggregation and consensus measure for autocratic decision making using group recommendations.

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