Evaluation of University Professors Using the Spherical Fuzzy AHP and Grey MARCOS Multi-Criteria Decision-Making Model: A Case Study

This study introduces a hybrid multi-criteria decision-making (MCDM) model combining Spherical Fuzzy AHP and Grey MARCOS methods to evaluate university professors comprehensively. Addressing the limitations of traditional assessment methods, the model incorporates subjective and imprecise data while maintaining transparency and precision. Seven criteria, including teaching quality, accessibility to students, professional competence, preparation and organization, student feedback, contribution to the university, and ethical behavior, were used. Spherical Fuzzy AHP was employed to determine the weight coefficients of the criteria, leveraging fuzzy logic to capture uncertainties. Subsequently, Grey MARCOS ranked professors by evaluating their performance against ideal and anti-ideal solutions using interval grey numbers. The hybrid approach effectively mitigates bias and provides an accurate ranking of alternatives, even under conditions of incomplete or subjective data. The results validate the model's robustness and adaptability, highlighting its utility for both individual evaluations and broader institutional improvements. This methodology offers actionable insights for enhancing teaching quality, encouraging professional growth, and strengthening institutional competitiveness. The findings emphasize the importance of integrating advanced MCDM techniques to ensure comprehensive and equitable assessments in academia. This study provides a practical framework for universities aiming to elevate educational standards while addressing global challenges in higher education evaluation. By applying this hybrid model, institutions can foster a culture of continuous improvement and achieve more reliable and impactful outcomes in professor evaluations.