在本論文中，我們根據猶豫模糊語義詞集合的聚合運算及模糊集合之α-切割運算提出一個新的多準則語義決策方法，其中專家針對每個候選者以前後文無關文法產生對每一個評估準則的語義評價，且一個轉換函式將語義評價轉換成猶豫模糊語義詞集合。首先，我們針對各個猶豫模糊語義詞集合中的模糊集合，將其聚合成一個模糊集合。然後，我們針對每一個被聚合的模糊集合做α-切割運算，以得到各個被聚合的模糊集合之α-切割後之區間。然後，我們針對每個候選者的區間執行最小化運算。最後，我們針對每個候選者對於各準則所得之區間計算其偏好程度。如果候選者的偏好程度越大，則其偏好順序越佳。本論文中所提的方法比目前已存在之以猶豫模糊語義詞集合作多準則語義決策的方法更簡單且更有彈性。

In this thesis, we present a new method for multicriteria linguistic decision making based on the aggregation of hesitant fuzzy linguistic term sets and the α-cuts of fuzzy sets. A context-free grammar is used by the expert to produce linguistic assessments of alternatives with respect to criteria. The linguistic expressions are transformed into hesitant fuzzy linguistic term sets by a transformation function. First, the fuzzy sets in each hesitant fuzzy linguistic term set are combined into a fuzzy set. Then, the system performs the α-cut operations to these aggregated fuzzy sets to get intervals, respectively, where α∈(0, 1]. Then, for each alternative, the system performs the minimum operations among the intervals obtained by the α-cuts of the aggregated fuzzy sets to get a derived interval of each alternative, where α∈(0, 1]. Finally, for each alternative, the system calculates the likelihood p(X ≥ [0, 1]) of X≥[0,1], where X is a derived interval of the alternative. The larger the likelihood value of the alternative, the better the preference order of the alternative. The proposed method is simpler and more flexible than the existing methods for multicriteria linguistic decision making based on hesitant fuzzy linguistic term sets.

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