Optimization of the Air Pollution Mitigation Model Using Intuitionistic Fuzzy Trapezoidal Preference Relations and Grey Relational Analysis

Abstract

Addressing the pressing issue of air pollution requires sophisticated decision-making frameworks that can effectively prioritize mitigation strategies. However, existing literature falls short in providing comprehensive models that integrate intuitionistic fuzzy trapezoidal preference relations (IFTrPRs) with robust ranking methods. This study bridges this gap by introducing a novel framework that synergizes IFTrPRs with Grey Relational Analysis (GRA) and projection methods to derive priority weight vectors and compare alternatives. A key contribution of this research is the development of an optimization model for determining priority weights from IFTrPRs, which ensures multiplicative consistency. By integrating this model with GRA and projection methods, our framework provides a robust decision-making tool that can accurately handle uncertain situations and provide reliable results. The proposed framework is demonstrated in a real-world air pollution mitigation scenario, showcasing its effectiveness and superiority over existing techniques. By integrating IFTrPRs with GRA, this study offers a pioneering approach to tackling environmental challenges, contributing to the development of more effective strategies for air pollution mitigation.