An intuitionistic fuzzy hypersoft expert set-based robust decision-support framework for human resource management integrated with modified TOPSIS and correlation coefficient.

Human resource management is the process of making a company's human resources decisions. In general, these decisions include hiring, firing, training, and developing people according to their positions and the needs of the organization. It includes a variety of policies and strategies designed to recognize the contribution that people make to an organization. The core goal of this article is to depict a novel fuzzy multi-criteria decision-making methodology for selecting employees. The purpose behind selecting employees is to identify and hire individuals who possess the required skills, qualifications, and attributes that align with the organization's goals and job requirements. To reflect an inadequate assessment, ambiguity, and anxiety in making choices, the intuitionistic fuzzy hypersoft expert set is an extension of the intuitionistic fuzzy soft expert and hypersoft sets. It is a novel approach to decisions and intelligent computing in the face of uncertainty. The intuitionistic fuzzy hypersoft expert set has a better ability to handle ambiguous and unclear data. In the research that follows, the ideas and characteristics of the correlation coefficient and the weighted correlation coefficient of the intuitionistic fuzzy hypersoft expert sets are proposed. Under the aegis of intuitionistic fuzzy hypersoft expert sets, a TOPSIS based on correlation coefficients and weighted correlation coefficients is introduced. Aside from that, we also covered aggregation operators, including intuitionistic fuzzy hypersoft weighted geometric operators. The decision-making process is suggested in an intuitionistic fuzzy hypersoft expert environment to resolve uncertain and ambiguous information, relying on the well-established TOPSIS approach and aggregation operators. An illustration of decision-making challenges shows how the suggested algorithm can be used. The efficacy of this strategy is lastly demonstrated by comparing its benefits, effectiveness, flexibility, and numerous current studies. [ABSTRACT FROM AUTHOR]