近幾年來，有許多處理群體決策問題之方法被提出。在本論文中，我們根據OWA運算子及可能性比較關係提出了兩個新方法以處理群體決策問題。在本論文所提之第一個方法中，我們根據OWA運算子、相似度測量及相關係數以處理群體決策問題。首先，我們結合專家的語意詞並計算各專家的偏好值。然後，計算專家偏好值與群體偏好值的相似程度。然後，計算每個專家的優先順序與群體優先順序之間的相關係數，以分別改變各專家之權重，直到群體共識度大於或等於一個預定的門檻值為止。此方法提供我們一個有用的方式以處理群體決策問題。在本論文所提之第二個方法中，我們根據區間語意詞與可能性比較關係以處理群體決策問題。首先，我們結合所有專家的區間語意詞以構建一個集體的語意矩陣。然後，根據可能性比較關係為每個專家的語意矩陣及建構之集體的語意矩陣分別為各個專家建構偏好值矩陣及集體的偏好值矩陣。然後，根據構建好之集體偏好值矩陣，計算每個候選者的分數，其中得分越高的候選者其偏好順序越佳。然後，根據每個專家的偏好值矩陣及已建構好的集體偏好值矩陣為每個專家構建共識矩陣。然後，根據為每個專家所建構之共識矩陣計算共識程度。然後，根據每個專家所獲得的共識程度，計算所有專家的群體共識度。如果群體共識度小於預定之門檻值，則必須修改其中一些專家的意見。透過重複執行上述過程，直到所有專家的群體共識度大於或等於預定之門檻值。此方法提供我們一個有用的方式以處理群體決策問題。

In recent years, some methods have been presented for dealing with group decision making problems. In this thesis, we present two new methods for autocratic decision making using group recommendations based on the order weighted averaging (OWA) operator and likelihood-based comparison relations. In the first method of this thesis, we present a new method for autocratic decision making using group recommendations based on the OWA operator, similarity measures and correlation coefficients. First, the proposed method aggregates linguistic terms and calculates preference values with respect to each expert. Then, it calculates the similarity degree between the aggregated preference values of the alternatives with respect to all experts and the preference values of the alternatives with respect to each expert, respectively. Then, it calculates the correlation coefficient between the preference order of the alternatives for all experts and the preference order of the alternatives for each expert, respectively, for changing the weights of experts until the group consensus degree is larger than or equal to a predefined consensus threshold. It provides us with a useful way for autocratic decision making using group recommendations based on the OWA operator, similarity measures and correlation coefficients. In the second method this thesis, we present a new method for autocratic decision making using group recommendations based on intervals of linguistic terms and likelihood-based comparison relations. Firstly, our method aggregates the interval linguistic preference matrices of each expert to construct a collective interval linguistic preference matrix. Then, based on the interval linguistic preference matrix of each expert and the constructed collective matrix, it uses likelihood-based comparison relations of interval linguistic terms to construct a preference matrix for each expert and to construct a collective preference matrix for all experts, respectively. Then, based on the constructed collective preference matrix for all experts, it calculates the score of each alternative. The larger the score, the better the preference order of the alternative. Then, based on the constructed collective preference matrix for all experts and the constructed preference matrix of each expert, it constructs a consensus matrix for each expert. Then, based on the constructed consensus matrix for each expert, it calculates the consensus degree for each expert. Then, based on the obtained consensus degree for each expert, it calculates the group consensus degree for all experts. If the group consensus degree is smaller than a predefined threshold value between zero and one, then it modifies some of the intervals of linguistic terms in the interval linguistic preference matrix of the expert whose consensus degree is smaller than the group consensus degree. The above process is performed repeatedly, until the group consensus degree for all experts is larger than or equal to the predefined consensus threshold value. It provides us with a useful way for autocratic decision making using group recommendations based on intervals of linguistic terms and likelihood-based comparison relations.

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