Managers nowadays face challenging decisions on daily basis and must weigh a growing number of factors while making such decisions. Previously, such judgments were frequently assessed solely based on a single criterion, such as profit or cost. Cost or profit, on the other hand, rarely captures the desirability of a decision option. One of the most common and popular research domains in decision science theory is the multiple attribute decision making (MADM) problem. To deal with such issues, a variety of approaches have been presented including TOPSIS. The primary goal is to uncover the importance aspect of divergence measures based on exponential function under Pythagorean fuzzy sets (PFSs), proposed by Yager (2013) and its application to multi attribute decision making. Numerical computations have been carried out to validate our proposed measures. Moreover, comparison of the result for the proposed measures has been carried out to demonstrate the efficacy.

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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.

PurposeThe purpose of this paper is to create a numerical technique to tackle the challenge of selecting software reliability growth models (SRGMs).Design/methodology/approachA real-time case study with five SRGMs tested against a set of four selection indexes were utilised to show the functionality of TOPSIS approach. As a result of the current research, rating of the different SRGMs is generated based on their comparative closeness.FindingsAn innovative approach has been developed to generate the current SRGMs selection under TOPSIS environment by blending the entropy technique and the distance-based approach.Originality/valueIn any multi-criteria decision-making process, ambiguity is a crucial issue. To deal with the uncertain environment of decision-making, various devices and methodologies have been explained. Pythagorean fuzzy sets (PFSs) are perhaps the most contemporary device for dealing with ambiguity. This article addresses novel tangent distance-entropy measures under PFSs. Additionally, numerical illustration is utilized to ascertain the strength and authenticity of the suggested measures.

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