Abstract
This study proposes a revolutionary approach to decision-making based on the tri-phase execution of an innovative fuzzy logic model. Our work uses a three-stage stratified random sample approach supplemented by a randomized response technique to tackle the critical challenge of minimizing variance while accounting for costs. Using the alpha-cut method, our proposed model generates an efficient allocation strategy that efficiently balances cost constraints and variance reduction goals. We present numerical examples to demonstrate our model's usefulness in real-world scenarios. Our contribution is to provide decision-makers with a comprehensive framework for enhancing data collection methods, especially when respondent privacy is crucial. Our technique, which blends fuzzy logic and randomized response approaches, presents a pioneering solution to the inherent challenges of acquiring sensitive information, while maintaining data integrity and cost-effectiveness.