| Summary: | 博士 === 長庚大學 === 企業管理研究所博士班 === 101 === Risk analysis is a very important issue for an organization, especially in the decision-making process. However, there are many uncertainties in risk management. In uncertain and complex situations, making choices may be difficult for decision makers. For these reasons, in this dissertation, I analyze risks in linguistic terms and based on interval-valued fuzzy numbers (IVFNs), which may better address linguistic uncertainties in complex situations. I develop methods for solving decision-making problems in a fuzzy environment. To make organizational decisions based on multiple criteria, I develop a method that extends the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach by calculating degrees of similarity between the ideal and the negative ideal solutions simultaneously. Most studies related to TOPSIS based on fuzzy theory measure the distance between the alternatives and the positive/negative ideal solution. Rather than adopting this approach, in this dissertation, I develop a method to measure the similarity between the IVFNs of the alternatives and the positive ideal solution/negative ideal solution, which may lead to more intuitive results than measuring the distance. Based on applications in different types of organizations, I find that using the proposed method can make the decision-making process easier and more consistent for decision makers. Moreover, all of the calculations in the proposed method can be written into a decision support system that would simplify the decision-making process. The decision maker only has to input opinions in linguistic terms into the decision support system.
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