決策乃為生活中重要的一部分，隨著時代的進步，問題的複雜度也隨之上升，單一決策者雖然能快速產生決策，但評估結果可能會過於主觀，且可能沒辦法考量到問題的所有面向，此時群體決策模式便提供一個適當的解決方法；同樣地，問題愈趨複雜使得單一準則的決策漸漸不敷使用，因此必須透過多準則決策來輔以進行決策；而過往在決策進行時評估值往往採用的是明確值，但由於在評估時往往會涉及專家或評估者自身的主觀想法，這些主觀想法往往會含有不確定性與模糊性，因此本研究讓專家採以直覺式模糊數進行評估。
熵值(entropy)在熱力學中是用來描述分子不規則運動的度量，在模糊集合理論中則是被用來表示模糊數的模糊程度，本研究透過熵值先求出評估值的明確程度，再以明確程度的相對比例求出客觀權重值，因為過去許多文獻權重常常是已知或直接給定的。證據推理法可視為證據理論的延伸，可以將不確定與不完整的資訊(證據)進行融合，而決策中就常常含有不確定的評估資訊，本研究欲將證據推理法延伸至群體決策模式，透過兩次證據推理法整合專家意見及評估屬性。
本研究決策模式包含模式一及模式二，模式一與模式二的主要差異乃在於整合時機點的不同。初始階段為決策的前置作業，包括決定參與決策的專家、確定可行方案及評估屬性，讓專家進行評估並計算專家共識程度。模式一在整合的順序上為先對專家整合，然後才對評估屬性進行整合；模式二則是會先讓各專家對評估屬性進行整合，最後才進行專家間的整合。整合完畢後，透過排序函數對各方案進行排序，選出最佳的方案。最後透過範例演算，分析並比較本研究兩模式在排序上的差異、本研究的模式與其他決策方法在排序上的差異，以及不同排序方法的差異。

Decision making(DM) is an important part of daily life. With the advance of science and technology, DM problems have become more complex. Although a single decision maker can make a decision efficiently, the outcome might be subjective. In the same way, because of the complexity of the problem, single criteria can not describe the problem sufficiently. For these reasons, multiple attribute group decision making(MAGDM) can be a useful tool to deal with problems of this kind.
In the past, when experts evaluated each alternative with respect to different attributes, most of them used crisp value. However, there exist uncertain or fuzzy areas in human thinking. In view of this, intuitionistic fuzzy number provide a better way to handle fuzziness and uncertainty.
In this thesis, we use entropy to find weights to develop two group decision making models under intuitionistic fuzzy environments by using evidential reasoning methods. The main difference between Model 1 and Model 2 is integration timing. Model 1 integrates expert opinions first and then integrates attributes. Model 2 integrates attributes first and then integrates expert opinions. After all integration is completed, the proposed ranking function is used to sort every alternative and choose the best one. At the end of this thesis, four examples are presented to illustrate the procedure of the proposed methods as well as to compare the sorting results between different models.

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