摘要
決策是日常生活中最常見的問題之一，近年來其複雜性隨著科技與資訊的蓬勃發展而日益提升。因此，通常會由多位決策參與者組成決策團隊來共同進行決策，並考量多個目標或多項決策屬性之多準則決策模式，以獲得更縝密且公正的決策結果。在決策資訊方面，本研究採用直覺式模糊集合以同時表達正向、負向以及不確定等三種資訊，進而幫助決策參與者們更清楚地表達其思緒與想法。
在決策流程中，整合所有決策資訊的流程為必要環節之一，而各筆決策資訊之權重對整合結果有著舉足輕重的影響力。因此，如何合理並有系統地計算各筆決策資訊之權重，是決策領域中相當值得深究的議題。然而在過往文獻中，較缺乏以辨別力為核心概念的權重演算法，因此本研究將集群分析與分群效度指標之概念應用於決策領域，進而發展出一套以決策資訊所蘊含的辨別力為權重給定依據之權重演算法。
本研究之權重演算法利用集群分析對決策資訊作前置處理以獲得分群結果，再藉由分群效度指標衡量分群結果所蘊含的辨別力並推算出其權重。而在集群分析階段，本研究發展出一套新的距離/相似測度公式，用以必要之計算。接著，本研究建構了一套直覺式模糊群體多屬性決策模式。
再來，本研究透過實際範例進行範例演算，進而比較並分析本研究與過往文獻的權重演算法，於各筆決策資訊之權重與最佳解決方案之排序結果上的差異。根據演算結果，過往文獻之演算法皆有所缺點，會導致不合理的數值結果且較缺乏縝密性與合理性。而本研究之權重演算法能有效地根據決策資訊所蘊含的辨別力加以給定權重，也更加具有縝密性與合理性。最後，本研究對權重演算法與測度公式進行了敏感度分析，進而檢視調整各項參數對權重與最佳解決方案之影響。根據演算結果，本研究於權重演算法與測度公式之各項參數的設定皆具有合理性與必要性。

關鍵字：直覺式模糊集合、群體決策、多屬性決策、集群分析、分群效度指標

SUMMARY

Decision making(DM) is an important part of daily life, and it has become increasingly more complex with advances in information science and technology. To deal with complex DM problems, multiple attribute group decision making(MAGDM) models are useful tools to obtain the convincing results. When decision-makers in a group are evaluating a problem, there will be some uncertain areas in human thought processess. In this situation, intuitionistic fuzzy sets(IFSs) provide a better way to handle fuzziness and uncertainty, and they can serve as good tools for decision-makers to provide their subjective views.
During DM procedures, it’s necessary and important to calculate the weight of each decision-maker and attribute in a reasonable way. However, there is no reasonable weight algorithm based on the concept of discrimination in the existing literature. In view of this, we use cluster analysis and cluster validity indexes to develop a new weight algorithm based on the concept of discrimination.
Furthermore, we develop a MAGDM model in intuitionistic fuzzy environments. During the DM procedures applied in our thesis, we also develop a new measurement formula to do the necessary calculations because there are some problems in the existing formulas.
Finally, we illustrate the procedures of our weight algorithm and MAGDM models with actual cases, and then compare results with those from the existing literature. It was found that our weight algorithm provides more reasonable and convincing results than those in the existing literature.

Keywords: Intuitionistic Fuzzy Sets, Group Decision, Multiple Attribute Decision, Cluster Analysis, Cluster Validity Index

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