• DocumentCode
    510134
  • Title

    Group Decision Making with Linguistic Preference Relations Based on Fuzzy Measures

  • Author

    Tan, Chunqiao

  • Author_Institution
    Sch. of Bus., Central South Univ., Changsha, China
  • Volume
    1
  • fYear
    2009
  • fDate
    7-8 Nov. 2009
  • Firstpage
    500
  • Lastpage
    504
  • Abstract
    Linguistic preference relation is a useful tool for expressing preferences of decision makers in group decision making according to linguistic scales. But in the real decision problems, there usually exist interactive phenomena among the preference of decision makers so that it is not suitable for us to aggregate preference information by conventional additive aggregation operators. Thus, to approximate the human subjective preference evaluation process, it would be more suitable to apply non-additive measures tool, where it is not necessary to assume additivity and independence among preference of decision makers. In this paper, based on the fuzzy measure, we developed a new linguistic ordered geometric averaging operator to aggregate the multiplicative linguistic preference relations, where interactions or dependence among subjective preference of decision makers are considered. Further, the procedure and algorithm of group decision making based on the new linguistic aggregation operators is given. Finally, a practical example is provided to illustrate the developed approaches.
  • Keywords
    computational linguistics; decision making; fuzzy set theory; fuzzy measures; group decision making; human subjective preference evaluation process; linguistic ordered geometric averaging operator; multiplicative linguistic preference relations; Additives; Aggregates; Artificial intelligence; Computational intelligence; Concrete; Decision making; Fuzzy sets; Humans; Phase measurement; Power measurement; Fuzzy measure; Group decision making; Linguistic preference relations; aggregation operator;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-3835-8
  • Electronic_ISBN
    978-0-7695-3816-7
  • Type

    conf

  • DOI
    10.1109/AICI.2009.140
  • Filename
    5376283