Assessment of learning management systems based on Schweizer–Sklar picture fuzzy Maclaurin symmetric mean aggregation operators

In literature, several tools have been developed to cope with ambiguity in data. Picture fuzzy set (PFS) is a very significant framework for extracting the maximum information from real-life phenomena with minimum uncertainty. Consequently, solving the muti-attribute decision-making (MADM) with the help of the PFS would be certainly useful. In this study, a short note on basic terms is provided for a better understanding of the article. A new class of operators picture fuzzy Schweizer–Sklar Maclaurin symmetric mean, picture fuzzy Schweizer–Sklar weighted Maclaurin Symmetric mean, picture fuzzy Schweizer–Sklar dual Maclaurin symmetric mean and picture fuzzy Schweizer–Sklar dual weighted Maclaurin symmetric mean is introduced. Some of the introduced AOs are applied to a real-life problem with the help of an illustrative example. For significance, the introduced AOs are compared to some existing AOs. The disparity of the results with the change in the involved parameters is also studied. The results obtained are tabulated and graphed.