Determining weights of fuzzy attributes for multi-attribute decision-making problems based on consensus of expert opinions

Seyed Hossein Razavi Hajiagha, Hannan Amoozad Mahdiraji, Shide Sadat Hashemi, Zenonas Turskis

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)
19 Downloads (Pure)


An important objective of a group decision-making problem is to determine the weights of attributes that are given by experts participating in the decision-making process. Since different
decision-makers have unequal importance in decision-making, a series of studies focused on finding a set of appropriate weights for experts participating in a decision problem. In this paper, the
problem of weight determination among decision-makers is investigated by extending an algorithm taken from the technique for order preference by similarity-to-ideal solution. In this case, a pair of most compromising and least compromising solutions is derived from individual judgments of decision-makers and then, these solutions are applied as the bases for determining the magnitude of individual alignment with the group opinion by using a closeness coefficient approach. Determining the weights of decision-makers, the group decision-making problem is then solved. Application of the proposed method is illustrated by a numerical example for the selection of a maintenance
Original languageEnglish
Pages (from-to)738-755
Number of pages18
JournalTechnological and Economic Development of Economy
Issue number5
Publication statusPublished - 3 Sept 2015
Externally publishedYes

Bibliographical note

Copyright © 2019 The Author(s). Published by VGTU Press.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


  • weights
  • multi-attribute
  • fuzzy
  • group decision-making
  • linguistic variable
  • Triangular fuzzy numbers
  • most compromising solution
  • least compromising solution


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