Multi-criteria Comparison of Electric Sport Utility Vehicles Using Proportional Spherical Fuzzy VIKOR Method

The burgeoning interest in Electric Vehicles (EVs) as sustainable alternatives to traditional combustion engine vehicles underscores a pivotal shift in automotive preferences worldwide. Amidst this transition, electric Sport Utility Vehicles (SUVs) have emerged as a compelling option, offering spacious interiors and eco-friendly mobility. However, the rapid proliferation of electric SUV models presents consumers with a complex decision-making landscape, necessitating robust Multicriteria Decision Making (MCDM) methodologies. In response, this study proposes a novel approach by extending the VIKOR method using Proportional Spherical Fuzzy Sets (PSFSs), accommodating the vagueness and imprecision inherent in decision-making processes. The literature review highlights a dearth of research specifically targeting electric SUV selection, emphasizing the novelty and significance of this study. Drawing upon recent advancements in fuzzy set theory, particularly PSFSs, the proposed methodology aims to provide a more intuitive and accurate means of evaluating electric SUV alternatives. By leveraging the flexibility and sensitivity of PSFSs, this method offers decision-makers a systematic framework to navigate the multifaceted criteria influencing electric SUV selection, encompassing factors such as driving range, charging infrastructure, performance, and price. Through a comprehensive evaluation process, this study aims to empower consumers with informed decision-making tools, facilitating the adoption of electric SUVs and contributing to a more sustainable automotive future. A comparative analysis with the Proportional Intuitionistic Fuzzy (PIF) CODAS method demonstrated the superiority of the proposed approach in terms of simplicity, computational efficiency, and flexibility in assigning membership degrees. Moreover, a sensitivity analysis highlighted the robustness of the proposed method against variations in expert weights, ensuring reliable decision-making outcomes.