A Fermatean fuzzy GLDS approach for ranking potential risk in the Fine-Kinney framework

Abstract

The Fine-Kinney (F-K) model has been broadly employed for evaluating and ranking risk in various fields. The risk scoring information expression and priority ranking are two significant operations for its application. Numerous approaches have been extended to the two operations to improve the performance of conventional Fine-Kinney for risk analysis. Nevertheless, current literature on the F-K framework seldom considers the collective and individual risk attitudes in ranking potential hazards, especially with Fermatean fuzzy-based -risk scoring information. This paper generates a new ranking approach for risk prioritization in F-K to fulfill this gap by integrating the Fermatean fuzzy sets with the GLDS (gained and lost dominance score) method. First, the Fermatean fuzzy sets-based risk scale is introduced to acquire risk scores. Then, a new collective risk scoring matrix establishment approach based on Fermatean fuzzy Bonferroni mean (BM) operator is built for considering the interactive effects between experts. Next, an extended Fermatean fuzzy GLDS method with CRITIC (Criteria Importance Through Inter-criteria Correlation)is proposed to rank the potential hazards, in which the Fermatean fuzzy CRITIC method is adopted to determine the weights. Especially, this developed weighting method can depict the inter-correlation among risk parameters. Finally, this paper presents a risk evaluation case of professional hazards for construction operations to display the application and advantages of this improved hybrid risk ranking model in the F-K framework. The result reveals that the enhanced framework can effectively rank potential hazards with complex risk information.