Analyzing customer preferences for hydrogen cars: a characteristic objects method approach

Shekhovtsov A., Rafiei Oskooei A., Wątróbski J., Sałabun W.

Artificial Intelligence Review, cilt.58, sa.2, 2025 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 58 Sayı: 2
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1007/s10462-024-11027-3
  • Dergi Adı: Artificial Intelligence Review
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, Educational research abstracts (ERA), Index Islamicus, INSPEC, Library and Information Science Abstracts, Library, Information Science & Technology Abstracts (LISTA), Metadex, Psycinfo, zbMATH, Civil Engineering Abstracts
  • Anahtar Kelimeler: COMET, Fuzzy logic, Hydrogen cars, Local weights, MCDM
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

As hydrogen vehicles gain popularity, car manufacturers are introducing numerous models, presenting customers with the challenge of choosing the most suitable option. To address this, Multi-Criteria Decision Analysis methods are often used to evaluate and select the best alternative. This study applies the Characteristic Objects Method (COMET) to address the practical problem of selecting the most appropriate hydrogen car for decision-makers. Using data provided by manufacturers, we evaluate ten hydrogen vehicles and create six decision models based on the preferences of three decision-makers, utilizing both the recently proposed Triad Support and Expected Solution Point-COMET algorithms. The models provide insights into how customer preferences can be extracted and represented in decision models. Moreover, we analyze local weights derived from the models to understand customer expectations for hydrogen cars better. The results of our study highlight the effectiveness of the COMET approach in capturing and comparing decision-maker preferences, offering a valuable methodology framework for future applications in similar multi-criteria decision-making problems.