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Measuring the performances of decision-making units using geometric average efficiency

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Listed:
  • Y M Wang

    (School of Public Administration, Fuzhou University
    Manchester Business School, The University of Manchester)

  • K S Chin

    (City University of Hong Kong)

  • J B Yang

    (School of Public Administration, Fuzhou University)

Abstract

The performances of decision-making units (DMUs) can be measured from two different points of view: optimistic or pessimistic, which leads to two different efficiencies for each DMU: the best relative efficiency and the worst relative efficiency. The conventional data envelopment analysis (DEA) considers only the best relative efficiency. It is argued that the two different efficiencies should be considered together and any approach considers only one of them is biased. This paper proposes to integrate the two different efficiencies into a geometric average efficiency, which measures the overall performance of each DMU. It is found that the geometric average efficiency has better discriminating power than either of the two efficiencies. This is illustrated by two numerical examples.

Suggested Citation

  • Y M Wang & K S Chin & J B Yang, 2007. "Measuring the performances of decision-making units using geometric average efficiency," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(7), pages 929-937, July.
    • Handle: RePEc:pal:jorsoc:v:58:y:2007:i:7:d:10.1057_palgrave.jors.2602205
    DOI: 10.1057/palgrave.jors.2602205
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    References listed on IDEAS

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    1. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    2. Joseph Paradi & Mette Asmild & Paul Simak, 2004. "Using DEA and Worst Practice DEA in Credit Risk Evaluation," Journal of Productivity Analysis, Springer, vol. 21(2), pages 153-165, March.
    3. Doyle, J. R. & Green, R. H. & Cook, W. D., 1995. "Upper and Lower Bound Evaluation of Multiattribute Objects: Comparison Models Using Linear Programming," Organizational Behavior and Human Decision Processes, Elsevier, vol. 64(3), pages 261-273, December.
    4. Entani, Tomoe & Maeda, Yutaka & Tanaka, Hideo, 2002. "Dual models of interval DEA and its extension to interval data," European Journal of Operational Research, Elsevier, vol. 136(1), pages 32-45, January.
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    Cited by:

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    13. Balk, Bert M. & (René) De Koster, M.B.M. & Kaps, Christian & Zofío, José L., 2021. "An evaluation of cross-efficiency methods: With an application to warehouse performance," Applied Mathematics and Computation, Elsevier, vol. 406(C).
    14. Róbert Štefko & Jarmila Horváthová & Martina Mokrišová, 2020. "Bankruptcy Prediction with the Use of Data Envelopment Analysis: An Empirical Study of Slovak Businesses," JRFM, MDPI, vol. 13(9), pages 1-15, September.
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    16. Farzaneh Asadi & Sohrab Kordrostami & Alireza Amirteimoori & Morteza Bazrafshan, 2023. "Inverse data envelopment analysis without convexity: double frontiers," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 46(1), pages 335-354, June.
    17. Adel Hatami-Marbini & Madjid Tavana & Kobra Gholami & Zahra Ghelej Beigi, 2015. "A Bounded Data Envelopment Analysis Model in a Fuzzy Environment with an Application to Safety in the Semiconductor Industry," Journal of Optimization Theory and Applications, Springer, vol. 164(2), pages 679-701, February.
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    19. Vahed Ghiasi & Seyed Amir Reza Ghasemi & Mahyar Yousefi, 2021. "Landslide susceptibility mapping through continuous fuzzification and geometric average multi-criteria decision-making approaches," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(1), pages 795-808, May.
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