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A New Method for Fuzzy Group Decision Making Based on α-Level Cut and Similarity

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Fuzzy Systems and Knowledge Discovery (FSKD 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3614))

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Abstract

Let opinions of experts among group decision making be represented as L-R fuzzy numbers. The difference of two experts’ opinions is reflected by two distances, which are called the left-hand side distance and the right-hand side one. A method to calculate two types of distances based on the same α-level is presented. Then the distances are employed to construct a new similarity function to measure the similarity degrees of both sides which represent the pessimistic and optimistic similarity degrees between the experts, respectively. The degree of importance of each expert among group decision making is obtained by employing Saaty’s analytic hierarchy process (AHP). The method of aggregating individual fuzzy opinions into a group consensus opinion by combining similarity degrees and the degree of importance of each expert is proposed. Finally some properties of the proposed similarity measure are proved and some numeric examples are shown to illustrate our method.

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Author information

Authors and Affiliations

  1. School of Economics and Management, Southwest Jiaotong University, 610031, Chengdu, Sichuan, P.R. China

    Jibin Lan

  2. School of Mathematics and Information Science, Guangxi University, 530004, Nanning, Guangxi, P.R. China

    Jibin Lan, Liping He & Zhongxing Wang

Authors
  1. Jibin Lan
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  2. Liping He
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  3. Zhongxing Wang
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. Honda Research Institute Europe GmbH, Offenbach/Main, Germany

    Yaochu Jin

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© 2005 Springer-Verlag Berlin Heidelberg

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Lan, J., He, L., Wang, Z. (2005). A New Method for Fuzzy Group Decision Making Based on α-Level Cut and Similarity. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_61

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  • DOI: https://doi.org/10.1007/11540007_61

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28331-7

  • Online ISBN: 978-3-540-31828-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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