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Maximum Lower Bound Estimation of Fuzzy Priority Weights from a Crisp Comparison Matrix

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Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9376))

Abstract

In Interval AHP, our uncertain judgments are denoted as interval weights by assuming a comparison as a ratio of the real values in the corresponding interval weights. Based on the same concept as Interval AHP, this study denotes uncertain judgments as fuzzy weights which are the extensions of the interval weights. In order to obtain the interval weight for estimating a fuzzy weight, Interval AHP is modified by focusing on the lower bounds of the interval weights similarly to the viewpoint of belief function in evidence theory. It is reasonable to maximize the lower bound since it represents the weight surely assigned to one of the alternatives. The sum of the lower bounds of all alternatives is considered as a membership value and then the fuzzy weight is estimated. The more consistent comparisons are given as a result of the higher-level sets of fuzzy weights in a decision maker’s mind.

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Authors and Affiliations

  1. Graduate School of Applied Informatics, University of Hyogo, Kobe, Hyogo, 650-0047, Japan

    Tomoe Entani

  2. Graduate School of Engineering Science, Osaka University, Toyonaka, Osaka, 560-8531, Japan

    Masahiro Inuiguchi

Authors
  1. Tomoe Entani
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  2. Masahiro Inuiguchi
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Correspondence to Tomoe Entani .

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Editors and Affiliations

  1. Science and Technology, Japan Advanced Institute of, Nomi, Japan

    Van-Nam Huynh

  2. Science, Dept of Systems Innovation, Osaka University,Graduate School of, Osaka, Japan

    Masahiro Inuiguchi

  3. Université de Technologie de Compiègne, Compiègne, France

    Thierry Demoeux

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Entani, T., Inuiguchi, M. (2015). Maximum Lower Bound Estimation of Fuzzy Priority Weights from a Crisp Comparison Matrix. In: Huynh, VN., Inuiguchi, M., Demoeux, T. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2015. Lecture Notes in Computer Science(), vol 9376. Springer, Cham. https://doi.org/10.1007/978-3-319-25135-6_8

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  • DOI: https://doi.org/10.1007/978-3-319-25135-6_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25134-9

  • Online ISBN: 978-3-319-25135-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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