Decision Algorithm With Interval-Valued Intuitionistic Fuzzy Hamy Mean Aggregation Operators for Assessment of Agricultural Education Practice

The assessment of performance in agricultural education practice is an important issue because it concerns the quality and effectiveness of agricultural education. This evaluation requires effective assessment methods. Interval-valued intuitionistic fuzzy set (IVIFS) is a powerful extension of fuzzy sets and intuitionistic fuzzy sets, conceived to handle uncertainty and vagueness more effectively in agricultural education practice assessment performance. A comprehensive exploration of interval-valued intuitionistic fuzzy (IVIF) information offers some flexible methods for assessment of the performance of agricultural education practice. This article identifies the relationship among input arguments with the help of Hamy mean aggregation models. In order to avail smooth approximated results during the aggregation process, some prominent operations of Aczel Alsina aggregation operators have also been adopted in light of an IVIF theory. The robustness of this article is to develop a family of new mathematical strategies based on IVIF information, namely IVIF Hamy mean (IVIFHM), IVIF weighted Hamy mean (IVIFWHM), IVIF Dual Hamy mean (IVIFDHM) and IVIF weighted Dual Hamy mean (IVIFWDHM) operators. Some dominant properties of diagnosed aggregation operators are also discussed to show their validity and effectiveness. Moreover, the decision algorithm of the multi-attribute decision-making (MAMD) problem is also adopted to reveal the versatility and adaptability of IVIF contexts. A case study of agricultural education practice assessment performance is also illustrated to showcase the practicability of derived approaches. The advantages and supremacy of invented research work are verified with a comparative study of prevailing research work that exists in the literature.