Social network based fuzzy decision-analytic model for hydrogen vehicle selection toward sustainable transportation systems
Year: 2026
Type: article
Abstract: The selection of an effective hydrogen vehicle technology is a multi-criteria decision making (MCDM) problem with conflicting evaluation criteria and uncertainty in experts’ judgments. To tackle this challenge, this study presents an integrated social network analysis based fuzzy MCDM approach to hydrogen vehicle selection. First, in the present work, Social Network Analysis (SNA) is used to calculate the relative influence of experts using degree centrality. Second, the uncertainty and imprecision in experts’ linguistic assessments are captured in terms of Interval-Valued Fuzzy Sets (IVFS). Third, the Entropy method is used as an objective weighting method to give relative importance of the evaluation criteria. Finally, the Evaluation based on Distance from Average Solution (EDAS) technique is used to evaluate and rank the alternatives. The suggested methodology is used in four hydrogen vehicle alternatives, with the evaluation criteria of eight. The results indicate that $$O_1$$ (FCEV) is the most suitable option, with an EDAS appraisal score of 0.9992 followed by $$O_3$$ (H2Hybrid), $$O_4$$ (H2REEV) and $$O_2$$ (H2ICE). The proposed approach is further tested the effectiveness by comparing four existing hybrid MCDM approaches, and O1 is consistently determined as the best alternative. In addition, Monte Carlo sensitivity analysis with 1,000 random perturbations of the criterion weights demonstrates that rank-1 probability for $$O_1$$ is 100% in all simulations. No rank reversal is observed in the investigated ranges for the sensitivity analyses when using criterion weights and expert weights as sensitivity factors. The quantitative results show the robustness and stability of the proposed approach for hydrogen vehicle selection under uncertain decision-making conditions.
Source: Scientific Reports
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7223475362
FWCI:
Cites:
Cited by:
Related to: