Modification of the fuzzy analytic hierarchy process via different ranking methods

KINAY A. Ö., TEZEL B. T.

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, cilt.37, sa.1, ss.336-364, 2022 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 37 Sayı: 1
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1002/int.22628
  • Dergi Adı: INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.336-364
  • Anahtar Kelimeler: fuzzy analytic hierarchy process, fuzzy multiple criteria decision-making, performance analysis, ranking fuzzy numbers, LOGARITHMIC LEAST-SQUARES, MULTICRITERIA DECISION-MAKING, EXTENT ANALYSIS METHOD, REASONABLE PROPERTIES, SUPPORT-SYSTEM, AHP, SELECTION, NUMBERS
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

The use of fuzzy set theory in the analytic hierarchy process (AHP) has gained popularity in recent years as part of the multiple criteria decision-making (MCDM) process to more realistically reflect human judgment. However, due to the nature of fuzzy calculations, this situation imposes more computational load. The aim of this study is to propose methods for obtaining accurate weights from fuzzy pairwise comparison matrices with the least amount of computational load possible. In this context, two different fuzzy AHP (FAHP) methods based on fuzzy numbers ranking methods have been proposed and these proposed methods are compared with commonly accepted FAHP methods. Magnitude-based fuzzy AHP (MFAHP), which is one of the proposed methods, has outperformed all other methods according to accurate weight and computational load. Although the other proposed method, called the total difference-based fuzzy AHP (TDFAHP), gave better results than the frequently used Chang's fuzzy extent analysis method, it could not produce more accurate weight results than many other methods in general. But performance analysis shows that it is as good as the MFAHP in terms of computational load.