隨著全球化時代來臨，決策問題範圍的擴大，以往單憑個人知識或經驗判斷而行的決策方式已不敷使用，群體決策應運而生。決策問題通常包含決策者主觀的意見，而專家在評斷對於方案的偏好時，常會因為本身的文化背景不同而使用不同的評估方式，然而傳統的語意變數表示法，在轉換過程中常會造成資訊的遺漏，導致作出不完善的決策。此外，在群體決策中，常常存在著不同決策者對此決策扮演著不同重要程度的角色，其中更會存在著一個影響此決策最重要的決策者。針對上述問題，本研究在群體偏好決策問題中，導入二元模糊語意變數來有效整合多位決策人員的異質評估資訊，並考量在群體決策中有一位主要的決策者，比較其他決策者與主要決策者的偏好，藉由此方法先過濾出與最重要的決策者相去甚遠的意見，並利用模糊語意量子導引之循序權重平均運算子整合各專家一致化後之偏好資訊與權重加以運算，以協助企業決定對本身最佳的方案順位。最後代入範例演算以及將本模式與Xu (2004b, 2005) 模式相比較，證明本模式可更廣泛且準確的應用於群體決策偏好資訊處理。

In most of the group decision making problems, the preference information provided by experts is represented in the same format. However, with multiple individuals in decision situations, each one has his own knowledge on the alternatives of the decision problem. The use of information assessed in different domains usually happens in the real world. To make the preference information uniform, the traditional way “linguistic label representation” is adopted. But in the transforming phase, it may cause information loss. Besides, usually there is a leader among the group decision makers, each expert’s preference information in the group must be within a specified deviation degree.
In order to consider the above problems, firstly we apply the 2-tuple fuzzy linguistic representation model to deal with non-homogeneous information, which can be represented as values belonging to domains with different nature as linguistic, numerical and internal valued. In the second, the deviation measures of preference relations are used to screen each expert’s preference information to make sure that all the experts’ information is acceptable. After the preference information is unified and within deviation degree, FLOG-OWA is used to perform the aggregation process. Therefore, the rank of all the alternatives can be determined.
Finally, to evaluate the proposed model, we compare with Xu (2004b, 2005). The results show that the proposed model can be applied widely and precisely in preference information.

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