BENCHMARKING FUZZY-BASED MCDM APPROACHES IN RENEWABLE ENERGY SOURCES SELECTION: A NEW INTERVAL-VALUED NEUTROSOPHIC FUZZY DEMATEL-ANP-TOPSIS FRAMEWORK

Abstract

Assessing renewable energy resources requires robust multi-criteria decision-making tools capable of handling uncertainty, vagueness, and the complex interactions among sustainability-related criteria. This study provides a comprehensive comparison of several widely used fuzzy-based multi-criteria decision-making methods applied to renewable energy source evaluation, including Fuzzy DEMATEL, Fuzzy AHP, Fuzzy ANP, Fuzzy TOPSIS, Fuzzy VIKOR, Fuzzy COPRAS, Fuzzy ELECTRE, etc., and also spherical, intuitionistic or neutrosophic fuzzy variants reported in the literature. By applying each method to the same dataset, the analysis highlights the similarities, divergences, and sensitivity patterns that emerge across different fuzzy modelling perspectives. Building on these comparative insights, the study introduces a novel interval-valued neutrosophic fuzzy hybrid decision-making framework integrating DEMATEL, ANP, and TOPSIS. In the proposed model, interval-valued neutrosophic fuzzy DEMATEL is employed to capture causal relationships among criteria and determine influence weights, while interval-valued neutrosophic fuzzy ANP models interdependencies within the decision network. Finally, interval-valued neutrosophic fuzzy TOPSIS is used to generate a robust and discriminative ranking of renewable energy source alternatives. The results demonstrate that the hybrid interval-valued neutrosophic framework offers enhanced consistency, stronger representation of expert hesitation, and improved prioritization stability compared with conventional fuzzy MCDM methods. Overall, this study advances the methodological landscape of renewable energy source decision-making by both benchmarking existing fuzzy techniques and proposing an innovative interval-valued neutrosophic hybrid approach that can support more reliable and sustainable energy planning.