Technique for Order Preferences by Similarity to Ideal Solutions (TOPSIS) in Uncertainty Environment

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Springer Science and Business Media Deutschland GmbH

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This chapter investigate the world of Fuzzy TOPSIS, exploring its philosophical underpinnings, applications, and practical examples. The chapter begins with an exploration of the existential philosophy underlying the TOPSIS method, setting the stage for the subsequent discussions. The TOPSIS method is then introduced as a powerful tool for ranking alternatives, and its application in the chapter focuses on utilizing the TOPSIS method to determine the order of preference among alternatives. The method provides a systematic approach for decision-making, facilitating the selection of the most favorable alternative. The main highlight of the chapter lies in the comprehensive examination of the challenges and considerations associated with implementing Fuzzy TOPSIS. The chapter addresses the various attitudes and perspectives that decision-makers face when dealing with the uncertainties and imprecisions inherent in real-world decision scenarios. Moreover, the chapter presents an in-depth analysis of the Group Fuzzy TOPSIS method, enabling multiple decision-makers to collaboratively evaluate alternatives. This approach offers a valuable framework for addressing complex decision-making scenarios where diverse opinions and preferences are involved. In addition to Group Fuzzy TOPSIS, the chapter explores the Intuitionistic Fuzzy TOPSIS method, providing an alternative approach to tackle decision-making problems under ambiguity and intuition-based judgments. To illustrate the practical application of Fuzzy TOPSIS, the chapter concludes with two compelling case studies. These examples showcase the versatility and effectiveness of Fuzzy TOPSIS in different contexts, demonstrating its applicability in diverse domains. Furthermore, the application of TOPSIS in fuzzy data envelopment analysis has been discussed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.


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Studies in Computational Intelligence

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