An uncertainty-aware ergonomic risk assessment framework using Interval-Valued Fermatean Fuzzy Sets and hybrid MCDM

Musculoskeletal disorders (MSDs) pose a significant occupational health concern in manufacturing, where traditional ergonomic risk assessment methods (RULA, REBA, OWAS, OCRA, QEC, and SI) are limited by subjectivity and the inadequate handling of interdependent criteria. Existing research reveals a notable gap in integrated frameworks that systematically combine multiple assessment methods under uncertainty. This study introduces a novel approach that integrates Interval-Valued Fermatean Fuzzy Sets (IVFFS) with multi-criteria decision-making (MCDM) techniques for systematic ergonomic evaluation. The framework incorporates IVFFS–PIPRECIA for uncertainty-aware factor weighting, IVFFS–MAIRCA for comparative method ranking, and an ENTROPY formulation that treats tasks as criteria to enhance objectivity. The results show that the force requirement and automation compatibility are the most influential ergonomic factors, whereas REBA, OCRA, and OWAS demonstrate superior overall performance. Comparative findings further reveal substantial variation across single-method evaluations: posture-centric tools disproportionately emphasize certain risks, whereas the integrated model uncovers overlooked high-risk activities by aggregating the complementary strengths of different methods. The proposed framework advances ergonomic risk management by integrating advanced fuzzy logic and MCDM techniques, providing a transparent, systematic decision-support tool for practitioners and industrial stakeholders. It supports evidence-based workplace design and contributes to safer, more efficient manufacturing systems.