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The Advantage of Interval Weight Estimation over the Conventional Weight Estimation in AHP in Ranking Alternatives

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Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12482))

Abstract

In this paper, we investigate the significance of interval weight estimation in the setting of Analytic Hierarchy Process (AHP). We consider several estimation methods for a normalized interval weight vector from a crisp pairwise comparison matrix. They have a desirable property. To avoid the non-uniqueness of the solution, we add an additional constraint, i.e., the sum of centers of interval weights is one. A few ranking methods under interval weights are considered. Numerical experiments are executed to compare the estimation accuracy of ranking alternatives under the assumption that the decision maker has a true interval weight vector. The advantage of interval weight estimation over crisp weight estimation is demonstrated.

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Acknowledgement

This work was supported by JSPS KAKENHI Grant Number JP17K18952.

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Authors and Affiliations

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

    Masahiro Inuiguchi & Issei Torisu

Authors
  1. Masahiro Inuiguchi
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  2. Issei Torisu
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Corresponding author

Correspondence to Masahiro Inuiguchi .

Editor information

Editors and Affiliations

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

    Van-Nam Huynh

  2. University of Hyogo, Kobe, Japan

    Tomoe Entani

  3. Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand

    Chawalit Jeenanunta

  4. Graduate School of Engineering Science, Osaka University, Toyonaka, Osaka, Japan

    Masahiro Inuiguchi

  5. Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand

    Pisal Yenradee

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Inuiguchi, M., Torisu, I. (2020). The Advantage of Interval Weight Estimation over the Conventional Weight Estimation in AHP in Ranking Alternatives. In: Huynh, VN., Entani, T., Jeenanunta, C., Inuiguchi, M., Yenradee, P. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2020. Lecture Notes in Computer Science(), vol 12482. Springer, Cham. https://doi.org/10.1007/978-3-030-62509-2_4

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  • DOI: https://doi.org/10.1007/978-3-030-62509-2_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62508-5

  • Online ISBN: 978-3-030-62509-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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