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Multiple-criteria performance ranking based on profile distributions: An application to university research evaluations

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  • Kunsch, Pierre L.
  • Ishizaka, Alessio

Abstract

This article addresses a category of multi-criteria ranking problems in which performance evaluations of the ‘objects’ or ‘alternatives’ to be ranked are not given by unique numbers but by performance profile distributions running over an ordered set of performance score levels. The numerical values to be given to score levels are not specified a priori using cardinal scales. A weighted sum approach is developed based on order statistics to combine the individual profile distributions. In this way, a global ranking indicator is obtained, considering not only mean distribution values but also standard deviations. As a test of feasibility, the resulting Profile Ranking with Order Statistics Evaluations (PROSE) approach has been applied to the performance profile distributions provided by the UK Research Excellence Framework 2014, evaluating the quality of UK research.

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  • Kunsch, Pierre L. & Ishizaka, Alessio, 2018. "Multiple-criteria performance ranking based on profile distributions: An application to university research evaluations," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 154(C), pages 48-64.
    • Handle: RePEc:eee:matcom:v:154:y:2018:i:c:p:48-64
    DOI: 10.1016/j.matcom.2018.05.021
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    Cited by:

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    2. Kunsch, Pierre L. & Ishizaka, Alessio, 2019. "A note on using centroid weights in additive multi-criteria decision analysis," European Journal of Operational Research, Elsevier, vol. 277(1), pages 391-393.
    3. Pierre L. Kunsch & Jean-Pierre Brans, 2019. "Visualising multi-criteria weight elicitation by multiple stakeholders in complex decision systems," Operational Research, Springer, vol. 19(4), pages 955-971, December.
    4. Alessio Ishizaka & Enrique Mu, 2023. "What is so special about the analytic hierarchy and network process?," Annals of Operations Research, Springer, vol. 326(2), pages 625-634, July.
    5. Muñuzuri, Jesús & Muñoz-Díaz, María-Luisa, 2019. "Use of DEA to identify urban geographical zones with special difficulty for freight deliveries," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.
    6. Csató, László & Tóth, Csaba, 2020. "University rankings from the revealed preferences of the applicants," European Journal of Operational Research, Elsevier, vol. 286(1), pages 309-320.

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