Optimized model involving forward and inverse problem formulation for the assessment of environmentally sustainable transportation alternatives based on intuitionistic linguistic weighted average

Intuitionistic fuzzy set theory provides a versatile framework for conveying information acquired during the decision-making process, particularly adept at handling indeterminate data. This study aims to devise a robust α -cut approach for intuitionistic linguistic weighted average (ILWA) applied specifically to intuitionistic fuzzy numbers (IFN). The utilization of an extended Jaccard Similarity Measure (EJSM) within the intuitionistic fuzzy context will yield valuable insights into the accuracy of representing linguistic data through the ILWA method. These advancements hold significant implications for selecting environmentally sustainable transportation alternatives and furnish analytical techniques for addressing uncertainty and imprecision across diverse decision-making domains. • Introduces the Intuitionistic Linguistic Weighted Average (ILWA) for decision-making. • Enhances ILWA using Intuitionistic Fuzzy Numbers (IFNs) to improve uncertainty handling. • ILWA applied to sustainable transportation offers a robust performance evaluation framework. • Combines Extended Karnik-Mendel (EKM) algorithm and alpha cuts for computing ILWA. • Uses Extended Jaccard Similarity Measure (EJSM) for accuracy in linguistic data representation.