Consideration of reciprocal judgments through Decomposed Fuzzy Analytical Hierarchy Process: A case study in the pharmaceutical industry

ÇEBİ S., Gündoğdu F. K., Kahraman C.

Applied Soft Computing, vol.134, 2023 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 134
  • Publication Date: 2023
  • Doi Number: 10.1016/j.asoc.2023.110000
  • Journal Name: Applied Soft Computing
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC
  • Keywords: Analytic hierarchy process, Decomposed Fuzzy Sets, Pharmaceutical industry, Pythagorean fuzzy AHP, Reciprocal questions, Third party logistic
  • Yıldız Technical University Affiliated: Yes


© 2023 Elsevier B.V.The number of academic research considering improvements/applications in the multiple criteria decision-making (MCDM) field has been increasing in the literature day by day. Among these works, the most used MCDM method is undoubtedly the analytic hierarchy process (AHP). Decomposed Fuzzy Sets (DFS) have been recently proposed to the literature in order to measure the inconsistency in expert judgments by providing optimistic and pessimistic point of view. The main objective of this paper is to extend AHP method by using DFS. DFS makes pairwise comparisons in AHP more reliable by considering the individual answers given by the decision makers to the reciprocal questions under vagueness and impreciseness. The proposed method, Decomposed Fuzzy Analytic Hierarchy Process (DF-AHP), is applied to determine the importance degrees of evaluation criteria in the pharmaceutical industry in order to illustrate the applicability of the approach. Based on the results, the “quality” is determined to be the most significant criterion in this industry. The results obtained from DF-AHP are compared with the results obtained from both traditional AHP and Pythagorean fuzzy AHP. The comparative analysis has also shown the most significant criterion is the same with the proposed method but slightly differences in the rank of the sub-criteria. The research can help businesses better understand the critical risks in the pharmaceutical industry.