Optimizing Electric Vehicle Selection Using Neutrosophic SuperHyperSoft Set Theory

The rising demand for sustainable transportation has intensified the need for robust decisionmaking models in selecting optimal Electric Vehicles (EVs) for organizational fleets. Traditional evaluation methods often struggle to handle the uncertainty, vagueness, and complex interdependencies involved in real-world multi-criteria assessments. To address these limitations, this study proposes a novel Multi-Criteria Decision-Making (MCDM) framework based on Neutrosophic SuperHyperSoft Sets (NSHSS). The proposed model introduces a powerful way to incorporate linguistic expert assessments, enabling flexible representation of indeterminacy and subjectivity through Neutrosophic triplets. By defining five core evaluation criteria range and Battery Efficiency (RBE), Total Cost of Ownership (TCO), Safety and Reliability (SR), Charging Infrastructure Compatibility (CIC), and Technology and Connectivity (TC), each subdivided into four linguistic sub-criteria, the framework constructs an extensive NSHSS universe using power sets and Cartesian products, resulting in 1,048,576 elements and 1024 propositions. A novel aggregation mechanism using the Generalized Neutrosophic SuperHyperSoft Weighted Heronian Mean (GNSHSWHM) operator and a customized score function is developed to rank EV alternatives effectively. A numerical illustration involving four EVs is presented to demonstrate the effectiveness, scalability, and practicality of the approach. Additionally, an automated R-based computational model is implemented to support real-time decision analysis. The study contributes a scalable, uncertainty-resilient, and context-adaptive tool for strategic EV adoption, and can be extended to broader domains involving complex MCDM problems under uncertainty.

Abstract licence: CC BY-NC

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