Tri-Phase Implementation of an Innovative Fuzzy Logic Approach for Decision-Making

• Published in: Symmetry (Basel) 2024-08, Vol.16 (8), p.994
• Main Authors: Tarray, Tanveer Ahmad, Khaki, Zahid Gulzar, Ganie, Zahoor Ahmad, Sultan, Adil, Danish, Faizan, Albalawi, Olayan
• Format: Article
• Language: English
• Subjects: Balancing; Cost analysis; Cost effectiveness; Costs; Data collection; Data mining; Data processing; Decision making; Design; Fuzzy logic; optimal allocation; Optimization techniques; Polls & surveys; Privacy; Probability; sensitive attributes; Statistical inference; Strategy; stratified sampling; Symmetry; Systems design; tri-phase implementation
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym16080994

This paper proposes a novel approach to decision-making based on a three-phase application of a new fuzzy logic model that embraces the principles of symmetry by balancing competing objectives in data collection and analysis. Our study, which employs a three-stage stratified random sample strategy with a randomized response technique, addresses the critical challenges of cost management and volatility reduction. Using the alpha-cut method, our model creates an effective allocation strategy that finds a balance between cost constraints and variance reduction objectives. We use numerical examples from real-world scenarios to demonstrate our approach’s durability and practicality. Our revolutionary technique maintains data quality and cost-effectiveness while offering a game-changing answer to sensitive information acquisition concerns. By combining randomized response techniques and fuzzy logic, this study establishes a new standard for decision-making models that prioritizes both data-gathering precision and privacy preservation, encapsulating the essential principle of symmetry in balancing competing aims.