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A multiple attribute relative quality measure based on the harmonic and arithmetic mean

Author

Listed:
  • Arie Taal

    (University of Amsterdam)

  • Marc X. Makkes

    (Vrije Universiteit)

  • Marijke Kaat

    (Surfnet)

  • Paola Grosso

    (University of Amsterdam)

Abstract

In this paper a relative quality measure is presented that is applicable to rank alternatives characterized by multiple attributes or performance measures. The quality measure proposed is based on the harmonic and arithmetic mean, and allows for a simple and quick analysis of the alternatives with respect to their attributes. An alternative ranked by this method and having the maximum relative quality of one can be considered as an extreme efficient unit according to the method of data envelopment analysis. The proposed method of the relative quality measure is compared with different multiple attribute decision making approaches that apply simple additive weighting, the MADM methods based on OWA operator, maximizing deviations, and information entropy, and the PROMETHEE II method.

Suggested Citation

  • Arie Taal & Marc X. Makkes & Marijke Kaat & Paola Grosso, 2019. "A multiple attribute relative quality measure based on the harmonic and arithmetic mean," Operational Research, Springer, vol. 19(1), pages 117-134, March.
    • Handle: RePEc:spr:operea:v:19:y:2019:i:1:d:10.1007_s12351-016-0282-5
    DOI: 10.1007/s12351-016-0282-5
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    References listed on IDEAS

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    1. William W. Cooper & Lawrence M. Seiford & Joe Zhu (ed.), 2011. "Handbook on Data Envelopment Analysis," International Series in Operations Research and Management Science, Springer, number 978-1-4419-6151-8, December.
    2. Bernard Roy, 2016. "Paradigms and Challenges," International Series in Operations Research & Management Science, in: Salvatore Greco & Matthias Ehrgott & José Rui Figueira (ed.), Multiple Criteria Decision Analysis, edition 2, chapter 0, pages 19-39, Springer.
    3. Zeshui Xu, 2015. "Uncertain Multi-Attribute Decision Making," Springer Books, Springer, edition 127, number 978-3-662-45640-8, February.
    Full references (including those not matched with items on IDEAS)

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