Decision Making Framework for Phishing Incident Response Using Intuitionistic Fuzzy Trapezoidal Preference Relations

Abstract

 Decision-making often involves uncertainty, requiring precise methodologies to ensure accuracy and reliability. This paper presents an intuitionistic fuzzy trapezoidal preference relation (IFTrPR)-based decision-making framework that integrates multiplicative consistency for improved priority weight assessment. The proposed approach determines intuitionistic fuzzy trapezoidal priority weight vectors and ranks alternatives using the technique for order preference by similarity to the ideal solution (TOPSIS). To enhance the consistency of priority weights, a Linear Decision Model (LDM) is employed, effectively capturing decision-makers’ perceptions. Additionally, Model 1 is introduced to compute priority weights based on intuitionistic fuzzy trapezoidal numbers (IFTNs) across various alternatives. The integration of fuzzy logic and optimization techniques strengthens the framework’s ability to handle complex decision-making problems. A comparative analysis with hierarchical fuzzy systems (HFS) demonstrates that the proposed method enhances accuracy and reliability in priority weight assessment. Furthermore, the study provides a systematic approach to handling linguistic variables in decision-making, particularly in the representation of membership (MS) and non-membership.