A novel approach for multi-criteria decision making: Extending the WASPAS method using decomposed fuzzy sets


Arslan Ö., ÇEBİ S.

Computers and Industrial Engineering, cilt.196, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 196
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1016/j.cie.2024.110461
  • Dergi Adı: Computers and Industrial Engineering
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, DIALNET, Civil Engineering Abstracts
  • Anahtar Kelimeler: Decomposed Fuzzy Sets, DF AHP, DF WASPAS, IF AHP, IF WASPAS, WASPAS
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

Multi-criteria decision-making (MCDM) methods are crucial for addressing complex real-world problems with multiple conflicting criteria. One of the most common MCDM methods is the Weighted Aggregated Sum Product Assessment (WASPAS) method, which combines the weighted sum (WSM) and weighted product (WPM) models for evaluating and ranking alternatives based on criteria. The WASPAS method provides a reliable and balanced approach by integrating these two methods. In this paper, we propose an innovative extension of the WASPAS method by integrating it with Decomposed Fuzzy Sets (DFS). This integration, Decomposed Fuzzy WASPAS (DF WASPAS), allows for a more comprehensive and precise capture of preferences and opinions of decision-makers, considering both optimistic and pessimistic perspectives. The DF WASPAS method is applied to a case study, where criteria weights are determined using DF AHP, and the proposed method is compared with the IF WASPAS method utilizing IF AHP for criteria weighting. The comparative analysis reveals significant differences in alternative rankings obtained using both WASPAS extensions. The proposed DF WASPAS method enhances decision-making accuracy by incorporating decision-makers’ nuanced perspectives and addressing uncertainties in complex problems.