Circular intuitionistic fuzzy TOPSIS method: pandemic hospital location selection

Alkan N., Kahraman C.

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, cilt.42, sa.1, ss.295-316, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 42 Sayı: 1
  • Basım Tarihi: 2022
  • Doi Numarası: 10.3233/jifs-219193
  • Dergi Adı: JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.295-316
  • Anahtar Kelimeler: Circular intuitionistic fuzzy sets, intuitionistic fuzzy sets, MCDM, TOPSIS, pandemic, hospital location selection, GROUP DECISION-MAKING, SUPPLIER SELECTION, EXTENSION, HYBRID
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

A circular intuitionistic fuzzy set (CIFS) recently introduced by Atanassov as a new extension of intuitionistic fuzzy sets is represented by a circle whose radius is r and whose center is composed of membership and non-membership degrees. The idea is similar to type-2 fuzzy sets, which are based on the fuzziness of membership functions with a third dimension. CIFSs help us define membership functions more flexibly, taking into account the vagueness in membership and non-membership degrees. In this study, TOPSIS, which is a multi-criteria decision-making (MCDM) method, is developed under circular intuitionistic fuzzy environment. The proposed CIF-TOPSIS method is applied to determine the most appropriate pandemic hospital location selection problem. Then, a sensitivity analysis based on criteria weights and the weight of the decision maker's optimistic and pessimistic attitudes are conducted to check the robustness of the decisions given by the proposed approach. A comparative analysis with the single-valued intuitionistic fuzzy TOPSIS, Pythagorean fuzzy TOPSIS, picture fuzzy TOPSIS methods is also performed to verify the developed approach and to demonstrate its effectiveness.