D-CFPR: D numbers extended consistent fuzzy preference relations

Xinyang Deng, Xi Lu, Tung Sun Chan, Rehan Sadiq, Sankaran Mahadevan, Yong Deng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

58 Citations (Scopus)


How to express an expert's or decision maker's preference for alternatives is an open issue. Consistent fuzzy preference relation (CFPR) is with big advantages to handle this problem due to it can be construed via a smaller number of pairwise comparisons and satisfies transitivity property. However, the CFPR is incapable of dealing with the cases involving uncertain and incomplete information. In this paper, a D numbers extended consistent fuzzy preference relation (D-CFPR) is proposed to overcome the weakness. The D-CFPR extends the classical CFPR by using a new model of expressing uncertain information called D numbers. The D-CFPR inherits the merits of classical CFPR and can totally reduce to the classical CFPR. This study can be integrated into our previous study about D-AHP (D numbers extended AHP) model to provide a systematic solution for multi-criteria decision making (MCDM).
Original languageEnglish
Pages (from-to)61-68
Number of pages8
JournalKnowledge-Based Systems
Publication statusPublished - 1 Jan 2015


  • Belief function
  • Consistent fuzzy preference relations
  • D numbers
  • D-CFPR
  • Dempster-Shafer evidence theory

ASJC Scopus subject areas

  • Management Information Systems
  • Software
  • Information Systems and Management
  • Artificial Intelligence


Dive into the research topics of 'D-CFPR: D numbers extended consistent fuzzy preference relations'. Together they form a unique fingerprint.

Cite this