Advancing physical fitness evaluation in colleges using a spherical fuzzy FUCA integrated decision support framework

When analyzing the physical fitness of college students, several criteria that are interconnected and the unpredictability of expert judgment must be taken into consideration. To eliminate this difficulty, this research presents a multi-criteria decision-making (MCDM) paradigm, and it is entitled the Spherical Fuzzy Faire Un Choix Adequat (SF-FUCA) framework. The proposed approach can be illustrated with the help of the hypothetical case study that covers fifteen student options, seven criteria, and four decision-makers. The SF-FUCA model combines spherical fuzzy sets with the FUCA aggregation method that considers the expert hesitation and ambiguity, and at the same time addresses the benefit and cost criteria. The numerical validation results indicate that the alternative "Improving Physical Condition" achieves the highest SF-FUCA score (10.38), followed by "Reduced Fitness Level" (9.90) and "Critical Fitness Condition" (9.70), whereas "Low Fitness Stage" attains the lowest score (4.49), demonstrating the strong discriminative capability of the proposed framework. The research on the impact of decision-maker weight variation and criteria weight variation on the ranking results was termed a sensitivity analysis that confirmed the strength and consistency of the approach. Compared to the present SF-MCDM procedures and classical MCDM framework, relative appraisal verifies that the SF-FUCA framework offers a more consistent derivation of priorities, better differentiation of other closely performing alternatives and better management of spherical fuzzy uncertainty than the classical SF-MCDM methods. The findings offer practical recommendations that teachers and institutional administrators can apply in order to institute specific interventions, allocate resources optimally, and promote the well-being of students.