Optimizing Electric Vehicle Selection Using Neutrosophic SuperHyperSoft Set Theory

Abstract

The rising demand for sustainable transportation has intensified the need for robust decision
making 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. 

DOI: 10.5281/zenodo.15514527