A Novel Pythagorean Fuzzy Set–Based Risk-Ranking Method for Handling Human Cognitive Information in Risk-Assessment Problems

Abstract

With the rapid evolution of the information age and the development of artificial intelligence, processing human cognitive information has become increasingly important. The risk-priority-number (RPN) approach is a natural language-processing method and is the most widely used risk-evaluation tool. However, the typical RPN approach cannot effectively process the various forms of human cognitive information or hesitant information provided by experts in risk assessments. In addition, it cannot process the relative-weight consideration of risk-assessment factors. In order to fully grasp the various forms of human cognitive information provided by experts during risk assessment, this paper proposes a novel Pythagorean fuzzy set–based (PFS) risk-ranking method. This method integrates the PFS and the combined compromise-solution (CoCoSo) method to handle human cognitive information in risk-assessment problems. In the numerical case study, this paper used a healthcare waste-hazards risk-assessment case to verify the validity and rationality of the proposed method for handling risk-assessment issues. The calculation results of the healthcare waste-hazards risk-assessment case are compared with the typical RPN approach, intuitionistic fuzzy set (IFS) method, PFS method, and the CoCoSo method. The numerical simulation verification results prove that the proposed method can comprehensively grasp various forms of cognitive information from experts and consider the relative weight of risk-assessment factors, providing more accurate and reasonable risk-assessment results.

Keywords

• artificial intelligence
• Pythagorean fuzzy sets
• intuitionistic fuzzy sets
• CoCoSo method
• human cognitive information