A Fermatean fuzzy Fine–Kinney for occupational risk evaluation using extensible MARCOS with prospect theory. (January 2023)

• Record Type: Journal Article
• Authors: Wang, Weizhong; Han, Xiao; Ding, Weiping; Wu, Qun; Chen, Xiaoqing; Deveci, Muhammet
• Is Part Of: Engineering applications of artificial intelligence. Volume 117:Part A(2023)
• Publication Date: 2023-01
• Subjects: Fine–kinney model -- Occupational risk -- Fermatean fuzzy -- MARCOS -- Prioritized weighted average operator; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105518
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: The extant Fine–Kinney frameworks are insufficient to tackle the risk evaluation problem with Fermatean fuzzy information, in which the prioritization degrees and psychological characteristics of decision-makers are considered. Hence, this study develops a hybrid Fine–Kinney-based occupational risk evaluation framework with an extended Fermatean fuzzy MARCOS method (measurement of alternatives and ranking to Compromise solution). Such a MARCOS method improves conventional MARCOS by integrating Fermatean fuzzy prioritized weighted average operator and prospect theory. This improved method has the capability to handle the occupational risk analysis problem with Fermatean fuzzy data in the risk ranking procedure considering the prioritization degrees and bounded rational behavior of decision-makers. In addition, the Fermatean fuzzy numbers-based risk rating scales are established to transform the linguistic risk scores from decision-makers, it allows for handling uncertain risk rating information from decision-makers more effectively. Further, the improved MARCOS method is incorporated into the occupational risk ranking procedure, as it considers the decision-maker's prioritization relationships among decision-makers and their reference point effect in occupational risk priority calculation. After that, an occupational risk analysis case for construction operations is selected to test the applicability and validity of the proposed framework. The result indicates that the occupational risk OR6 (Back injury) is the most serious risk with the lowest utility function value (-0.324), and OR7 (Tendinitis) is the least severe risk with the highest utility function value (0.682). Finally, sensitivity exploration and comparative study are implemented to further test the advantages of the developed framework.