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The transportation sector plays a critical role in reducing global carbon emissions; in this context, electric vehicles stand out as environmentally friendly, quiet, and efficient alternatives. The hatchback segment, in particular, holds a significant place in the electric transition process because it offers economical and practical solutions for urban transportation. In this study, a framework representing uncertainty using interval-valued neutrosophic (IVN) numbers was proposed for the evaluation of electric hatchback vehicles, and the CODAS method was used for ranking. Twenty-four alternatives were evaluated against seven criteria, and the decision process was tested through a comprehensive sensitivity analysis conducted on 105 different weight sets. Furthermore, the scalar multiplication IVN-CODAS operator was compared with the conventional version, and similar rankings were obtained under different threshold parameters, demonstrating the model’s stability. The findings reveal that the IVN-CODAS approach is an effective and flexible method not only for electric vehicle selection but also for MCDM problems involving uncertainty. The study offers an original contribution to the MCDM literature with both its methodological contribution and applied sensitivity analyses.

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A Hybrid IVN-CODAS Model for Ranking Electric Hatchback Cars: Application with Extensive Weight Sensitivity Analysis

  • 会议论文

  • 发表日期：

    2026年

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  • Mahmut Baydaş,
  • Abdullah Özçil,
  • Željko Stević,
  • Zhiyuan Wang

摘要

The transportation sector plays a critical role in reducing global carbon emissions; in this context, electric vehicles stand out as environmentally friendly, quiet, and efficient alternatives. The hatchback segment, in particular, holds a significant place in the electric transition process because it offers economical and practical solutions for urban transportation. In this study, a framework representing uncertainty using interval-valued neutrosophic (IVN) numbers was proposed for the evaluation of electric hatchback vehicles, and the CODAS method was used for ranking. Twenty-four alternatives were evaluated against seven criteria, and the decision process was tested through a comprehensive sensitivity analysis conducted on 105 different weight sets. Furthermore, the scalar multiplication IVN-CODAS operator was compared with the conventional version, and similar rankings were obtained under different threshold parameters, demonstrating the model’s stability. The findings reveal that the IVN-CODAS approach is an effective and flexible method not only for electric vehicle selection but also for MCDM problems involving uncertainty. The study offers an original contribution to the MCDM literature with both its methodological contribution and applied sensitivity analyses.