A modified interval valued intuitionistic fuzzy CODAS method and its application to multi-criteria selection among renewable energy alternatives in Turkey

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Deveci K., Cin R., Kağızman A.

Applied Soft Computing Journal, cilt.96, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 96
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1016/j.asoc.2020.106660
  • Dergi Adı: Applied Soft Computing Journal
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC
  • Anahtar Kelimeler: CODAS, Intuitionistic fuzzy sets, Multi-criteria decision making, Renewable energy, GROUP DECISION-MAKING, AGGREGATION OPERATORS, PREFERENCE, MODEL
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

© 2020 Elsevier B.V.Combinative Distance based ASsesment (CODAS) method aims to perform multi-criteria selection process according to the largest Euclidean and Taxicab distance with respect to negative ideal solutions. Recently, several CODAS methods have been applied to multi-criteria decision making problems with interval valued intuitionistic fuzzy sets. This paper demonstrates the weaknesses of using Euclidean and Taxicab distance on interval valued intuitionistic fuzzy sets and provides alternative strategies to model the vagueness and uncertainty in decision maker evaluations more effectively. The contribution of this paper is twofold. First, a new selection metric is defined in order to eliminate the disadvantages of using Euclidean and Taxicab distance in interval valued intuitionistic fuzzy CODAS. Second, a new fuzzy aggregation operator is proposed for aggregating decision maker evaluations by using fuzzy weights rather than using crisp weights. To show the effectiveness of the modified CODAS method, an application is given for multi-criteria selection of renewable energy alternatives in Turkey and the results are compared with two other interval valued intuitionistic fuzzy CODAS methods in the literature.