An innovative decisive framework for optimized agri-automobile evaluation and HRM pattern recognition via possibility fuzzy hypersoft setting

In multi-attribute decision-making system, decision-makers have to face two kinds of situations: (i) the opted parameters are likely to be classified into their respective parametric-valued sub-collections and (ii) the acceptance degree for approximate opinions of decision-makers is required to be assessed by possibility setting. The literature related to fuzzy soft sets is unable to provide any model which can tackle such situations collectively. Therefore, this study aims to address this scarcity through the development of a novel structure, that is, possibility fuzzy hypersoft set (pfhs-set). Firstly, the algebraic properties and set-theoretic operations of pfhs-set are characterized by mathematical illustration. Secondly, two algorithms based on AND and OR-operations of pfhs-set are proposed and authenticated through application in multi-attribute decision-making real-world problems for the evaluation of agri-automobile and then their suitability is judged through vivid comparison. Thirdly, similarity measures between pfhs-sets are formulated and validated with the help of application in recruitment-based pattern recognition and its significance is assessed through comparison with most relevant models. Lastly, the advantageous aspects of the proposed structure are analyzed by its comparison with possibility fuzzy soft set-like models by observing some important evaluating features.