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Using mathematical programming to solve large ranking problems

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  • A C B Tse

    (Chinese University of Hong Kong)

Abstract

This paper introduces a mathematical programming model that overcomes the major methodological problem of a large ranking task: respondent fatigue and deteriorated decision quality caused by an excessive number of objects to be ranked. The model was applied to the problem of ranking Marketing and International Business journals. There are more than 200 such journals, making direct ranking or rating very difficult, if not impossible. The result shows that the mathematical programming model uses very little information and yet can produce rankings that are in agreement with results obtained from direct ranking studies.

Suggested Citation

  • A C B Tse, 2001. "Using mathematical programming to solve large ranking problems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(10), pages 1144-1150, October.
    • Handle: RePEc:pal:jorsoc:v:52:y:2001:i:10:d:10.1057_palgrave.jors.2601203
    DOI: 10.1057/palgrave.jors.2601203
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    Cited by:

    1. I Horowitz, 2003. "Preference-neutral attribute weights in the journal-ranking problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(5), pages 452-457, May.
    2. Tüselmann, Heinz & Sinkovics, Rudolf R. & Pishchulov, Grigory, 2015. "Towards a consolidation of worldwide journal rankings – A classification using random forests and aggregate rating via data envelopment analysis," Omega, Elsevier, vol. 51(C), pages 11-23.

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