A q-rung orthopair fuzzy multi-criteria group decision making method for supplier selection based on a novel distance measure

Pinar A., BORAN F. E.

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, vol.11, no.8, pp.1749-1780, 2020 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 11 Issue: 8
  • Publication Date: 2020
  • Doi Number: 10.1007/s13042-020-01070-1
  • Page Numbers: pp.1749-1780
  • Keywords: q-Rung orthopair fuzzy set, Distance measure, Supplier selection, q-ROF TOPSIS, q-ROF ELECTRE, SIMILARITY MEASURES, SETS, TOPSIS, CRITERIA, OPERATORS, VENDOR


Supplier selection and evaluation is a crucial decision-making issue to establish an effective supply chain. Higher-order fuzzy decision-making methods have become powerful tools to support decision-makers in solving their problems effectively by reflecting uncertainty in calculations better than crisp sets in the last 3 decades. The q-rung orthopair fuzzy (q-ROF) sets which are the general form of both intuitionistic and Pythagorean fuzzy sets, have been recently introduced to provide decision-makers more freedom of expression than other fuzzy sets. In this paper, we introduce q-ROF TOPSIS and q-ROF ELECTRE as two separate methods and new approaches for group decision making to select the best supplier. As the existing distance measures in q-rung orthopair fuzzy environment have some drawbacks and generate counter-intuitive results, we propose a new distance measure along with its proofs to use in both q-ROF TOPSIS and q-ROF ELECTRE methods. Moreover, a comparison study is conducted to illustrate the superiority of the proposed distance measure. Subsequently, a comprehensive case study is performed with q-ROF TOPSIS and q-ROF ELECTRE methods separately to choose the best supplier for a construction company by rating the importance of criteria and alternatives under q-ROF environment. Finally, a comparison and parameter analysis are performed among the proposed q-ROF TOPSIS and q-ROF ELECTRE methods and existing q-ROF decision-making methods to demonstrate the effectiveness of our proposed methods.