Multi-Criteria Decision Making to Logarithmic Pythagorean Fuzzy Entropy Measure Under TOPSIS Approach

H. Arora, Anjali Naithani
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Abstract

One of the most essential ideas for tracing the best objects among a set of possible ones is decision-making theory. We make decisions to gain a wide range of advantages from them based on our previous experiences. The concept of Pythagorean fuzzy sets (PFS) was first established by Yager to provides a new technique to describe ambiguity with great precision when compared to intuitionistic fuzzy sets (IFS) and fuzzy sets (FS). The study of PFS is recently gaining importance due to its wide application in situations involving ambiguity. It can easily be merged with MADM techniques to solve real-life problems. However, many of these measures for PFS are ineffective in the sense that they have fundamental shortcomings that restrict them from providing reliable and consistent results. This paper provides a novel Pythagorean fuzzy entropy measure and its application to decision-making problem using technique for order preference by similarity of ideal solution (TOPSIS) on some real-life environment. Comparative study is also done for validation of the proposed measure.
TOPSIS方法下对数勾股定理模糊熵测度的多准则决策
在一组可能的目标中寻找最佳目标的最基本思想之一是决策理论。我们根据以前的经验做出决定,从他们那里获得广泛的优势。毕达哥拉斯模糊集(Pythagorean fuzzy sets, PFS)的概念最早是由Yager提出的,与直觉模糊集(IFS)和模糊集(FS)相比,毕达哥拉斯模糊集(Pythagorean fuzzy sets, PFS)提供了一种新的描述模糊性的技术,具有很高的精度。由于PFS在歧义情境中的广泛应用,其研究近年来越来越受到重视。它可以很容易地与MADM技术合并来解决现实生活中的问题。然而,许多针对PFS的这些措施是无效的,因为它们有根本的缺点,限制了它们提供可靠和一致的结果。本文提出了一种新的毕达哥拉斯模糊熵测度方法,并利用理想解相似性排序偏好技术(TOPSIS)将其应用于实际环境中的决策问题。为验证所提措施的有效性,进行了对比研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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