An extended EDAS method with circular intuitionistic fuzzy value features and its application to multi-criteria decision-making process

Garg H., ÜNVER M., OLGUN M., Türkarslan E.

Artificial Intelligence Review, cilt.56, ss.3173-3204, 2023 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 56
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1007/s10462-023-10601-5
  • 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
  • Sayfa Sayıları: ss.3173-3204
  • Anahtar Kelimeler: Circular intuitionistic fuzzy value, EDAS method, Multi-criteria decision-making
  • Ankara Üniversitesi Adresli: Evet

Özet

The paper aims is to present a multi-criteria decision-making algorithm for solving decision-making problems with the utilization of the C-IFSs (circular intuitionistic fuzzy sets) features. In it, the uncertainties present in the data are handled with the help of C-IFSs in which we considers the circular rating of each object within a certain radius. Later on, we propose a novel algebraic framework for C-IFSs based on Archimedean t-norm operations, including addition, multiplication, subtraction, and division. These operations enable the aggregation of preferences from multiple experts into a single ranking. Also, we propose an extended EDAS (Evaluation Based on Distance from Average Solution) method, which utilizes weighted aggregation operators and defuzzification techniques to rank alternatives. To validate our approach, we provide a numerical example and compare the results with existing methods. Additionally, we discuss the time complexity of the algorithm. The proposed methodology offers decision-makers the flexibility to analyze the influence of different ratings on the final decision and select suitable parameters.