On qualitative multi-attribute group decision making and its consensus measure: A probability based perspective

機関リポジトリ

説明

This paper focuses on qualitative multi-attribute group decision making (MAGDM) with linguistic information in terms of single linguistic terms and/or flexible linguistic expressions. To do so, we propose a new linguistic decision rule based on the concepts of random preference and stochastic dominance, by a probability based interpretation of weight information. The importance weights and the concept of fuzzy majority are incorporated into both the multi-attribute and collective decision rule by the so-called weighted ordered weighted averaging operator with the input parameters expressed as probability distributions over a linguistic term set. Moreover, a probability based method is proposed to measure the consensus degree between individual and collective overall random preferences based on the concept of stochastic dominance, which also takes both the importance weights and the fuzzy majority into account. As such, our proposed approaches are based on the ordinal semantics of linguistic terms and voting statistics. By this, on one hand, the strict constraint of the uniform linguistic term set in linguistic decision making can be released; on the other hand, the difference and variation of individual opinions can be captured. The proposed approaches can deal with qualitative MAGDM with single linguistic terms and flexible linguistic expressions. Two application examples taken from the literature are used to illuminate the proposed techniques by comparisons with existing studies. The results show that our proposed approaches are comparable with existing studies.

identifier:https://dspace.jaist.ac.jp/dspace/handle/10119/15434

収録刊行物

  • Omega

    Omega 70 94-117, 2016-09-12

    Elsevier

キーワード

詳細情報 詳細情報について

  • CRID
    1050564287706909824
  • NII論文ID
    120006522918
  • ISSN
    03050483
  • Web Site
    http://hdl.handle.net/10119/15434
  • 本文言語コード
    en
  • 資料種別
    journal article
  • データソース種別
    • IRDB
    • CiNii Articles

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