多屬性決策（Multiple Attribute Decision Making, MADM）是一種基於多個評選準則，經由系統化架構以評估方案的方法，已被廣泛地用來解決方案排序的決策問題。由於社會經濟環境日益複雜以及人類主觀判斷的模糊性，決策者在決策過程中提供的個人偏好可能以具有不同性質的模糊評估值表示，如語意、直覺模糊、或區間值直覺模糊。在現實生活中所遇到的決策問題大部分是複雜矛盾而難以決定，往往需要由多個決策者共同進行評估解決，因此模糊多屬性群體決策方法之研究顯得相當重要。為了協助決策者在模糊環境下進行決策，面對一個多屬性決策問題需要採用適合的方法以獲得滿意的結果，因此本研究建構不同模糊環境（模糊、直覺模糊、區間值直覺模糊）下，以理想解相似度順序偏好法（TOPSIS）為基礎的三個模糊多屬性決策方法。決策者能夠依照模糊決策的情境，採用一個合適的方法，以獲得滿意的排序結果。第一個方法是整合性（fuzzy AHP與fuzzy TOPSIS）模糊群體決策方法，以解決製藥公司評選供應商的問題。我們建立一個適用於製藥產業，作為評估供應商的一個完整層級架構。第二個方法是熵（entropy）權重的fuzzy TOPSIS直覺模糊群體決策方法，適用於直覺模糊環境下，利用客觀的熵值以求解未知的評估準則權重。第三個方法是考慮決策者態度特徵的最大熵OWA（maximum entropy OWA, MEOWA）直覺模糊/區間值直覺模糊決策方法，此法利用我們所提考慮決策者樂觀程度的計分函數建構一個評分矩陣。最後，經由四個應用案例之探討並與其它方法作比較，以說明本研究所提決策模式的運算過程與其實用性。

Multiple Attribute Decision Making (MADM), the most well known branch of decision making, provides an effective framework for alternatives’ comparison based on the evaluation of multiple decision criteria. Due to the increasing complexity of the socio-economic environment as well as the inherent vagueness and subjective nature of human thinking, a decision maker may provide his/her preferences in the decision making process represented as values belonging to domains with different natures, such as linguistic, intuitionistic fuzzy, or interval-valued intuitionistic fuzzy. In real life, however, the problems encountered are often complicated and individual opinion must be fused with group opinion to make a final decision. Thus, it is necessary to develop more effective methods for group decision-making in an uncertain environment. To help decision makers make better decisions in uncertain environments, suitable approaches are required for obtaining a satisfactory outcome for a given MADM problem. To address this issue, we construct three TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)-based MADM approaches to solve decision making problems in fuzzy environments. Decision makers can apply an appropriate method in considering the situation of decision making under certainty to obtain a satisfactory ranking outcome. The first method uses the integrated (fuzzy AHP and fuzzy TOPSIS) fuzzy group decision approach to solve a supplier selection problem in a pharmaceutical company. We identify the selection criteria and build a comprehensive hierarchical structure of the decision model that is capable of providing a valuable reference for pharmaceutical industries. The second method uses the MADM model with entropy weight in an intuitionistic fuzzy environment. This approach is suitable for dealing with intuitionistic fuzzy MADM problems in which the attribute weights are not predefined. The third method considers the decision maker’s attitudinal character to solve MADM problems in an intuitionistic fuzzy or interval-valued intuitionistic fuzzy environment. We utilize the proposed score functions considering a decision maker’s degree of optimism to construct a score matrix. Finally, four numerical examples are used to illustrate applicability, and comparisons with existing approaches are conducted to demonstrate the feasibility of our proposed approaches.

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