Decision-Making for Faculty Recruitment using Intuitionistic Cubic Fuzzy Graphs

Abstract

Multi-Criteria Decision Making (MCDM) models most often lack the ability to simultaneously account for the relational structure among criteria, hesitation and uncertainty in the human judgement, as shown by this paper. Therefore, this paper proposes a new approach Interpreting Intuitionistic Cubic Fuzzy Graphs (ICFG) Model using the Additive-Ratio Assessment (ARAS) Method, which uses an additive-ratio method. The resulting ICF-ARAS model provides the most logical method for creating a structured relational model of a decision-making element and includes interval degree membership, non-membership and hesitation. To demonstrate applicability of this new MCDM Methodology, this paper provides a case study on Faculty Recruitment, comparing 4 candidates on 4 criteria (qualifications, interview results, teaching experience and communication), the resulting model produced an identical ranking () as established alternative methodologies (ICF-TOPSIS and ICF-WASPAS) while offering enhanced interpretability, computational simplicity, and relational transparency. The new approach provides an effective, transparent and flexible decision-support mechanism for selecting multi-faceted and uncertain candidates in higher education and elsewhere.