| Summary: | 博士 === 國立成功大學 === 工業與資訊管理學系碩博士班 === 101 === 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.
|
|---|
