Performance Measure Using a Multi-Attribute Decision-Making Approach Based on Complex T-Spherical Fuzzy Power Aggregation Operators

Muhammad Rizwan  Khan; Kifayat  Ullah; Dragan Pamucar; Mehwish  Bari

doi:10.47852/bonviewJCCE696205514

Authors

Muhammad Rizwan Khan Department of Mathematics, Riphah International University, Pakistan
Kifayat Ullah Department of Mathematics, Riphah International University, Pakistan https://orcid.org/0000-0002-1438-6413
Dragan Pamucar Department of Logistics Military Academy, University of Defense in Belgrade, Serbia https://orcid.org/0000-0001-8522-1942
Mehwish Bari Riphah International College, Pakistan

DOI:

https://doi.org/10.47852/bonviewJCCE696205514

Keywords:

multi-attribute decision making, complex T-spherical fuzzy set, power aggregation operators, performance measure

Abstract

Performance measurement has a vital role in almost every field of life especially when uncertainty is involved in processing
information. The purpose of this research is to use the concept of power aggregation operators (PAOs) of complex T-spherical fuzzy sets (CTSFSs) to analyze the performance measures of software packages. In comparison to other studies, the main advantage of the PAOs of CTSFSs is its ability to feature four possible aspects of the information under uncertainty. In CTSFSs, each component has further two aspects denoted by their amplitude and phase terms and hence provides us a better ground to deal with practical problems. Another advantage of using PAOs for performance evaluation is the involvement of the relationship of the input arguments that play an essential role in aggregation. Other traditional aggregation operators (AOs) do not have such capabilities. We aim to develop complex T-spherical fuzzy (TSF) power weighted averaging and complex TSF power weighted geometric (CTSFPWG) operators and evaluated their validity using some tests. Ultimately, using the recommended operators, multi-attribute decision-making (MADM) algorithm is established for the performance evaluation of the software packages. With the help of a numerical example, we demonstrated the proposed MADM algorithm using complex uncertain information. The results show a positive impact by analyzing them after a comparative analysis where the results obtained using complex TSF PAOs seem to be more reliable than the results obtained using previously developed AOs. To see the effectiveness of the algorithm, the results are numerically compared using some existing approaches, and conclusions are drawn.

Received: 25 December 2021 | Revised: 4 February 2022 | Accepted: 7 February 2022

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.