New model for occupational health and safety risk assessment based on Fermatean fuzzy linguistic sets and CoCoSo approach

Nowadays, occupational health and safety management has gained more and more importance by organizations around the world because it positively impacts on the productivity, competitiveness and reputation. The occupational health and safety risk assessment (OHSRA) is a critical activity to identify and evaluate the most serious work-related hazards for corrective actions. In this study, a new OHSRA model using Fermatean fuzzy linguistic sets (FFLSs) and combined compromise solution (CoCoSo) approach is proposed for the risk evaluation and prioritization of occupational hazards. Specifically, the scientific contributions of this study are as follows: The FFLSs are used to cope with the complex and vague risk evaluations obtained from experts; the CoCoSo approach is utilized to determine the risk priority of the identified occupational hazards; an extended block-wise rating the attribute weighting method is introduced for specifying risk criteria weights. Finally, the applicability and effectiveness of the proposed OHSRA model are demonstrated by an occupational risk assessment example along with simulation experiments and a comprehensive comparative analysis. The major advantages of the proposed model are higher flexibility in handling experts' intricate and fuzzy assessment information and effective in providing a reliable risk ranking of occupational hazards with high discriminability.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

Occupational health and safety management is increasingly important for organizations worldwide as it has a positive impact on productivity, competitiveness, and reputation. This study proposes a new model for occupational hazard risk assessment using Fermatean fuzzy linguistic sets and the combined compromise solution approach. The model demonstrates flexibility in handling complex and vague assessment information and provides a reliable risk ranking with high discriminability.