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Ranking The Efficiency Performance Within A Set Of Decision Making Units By Data Envelopment Analysis

Author

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  • HONGYU LI

    (Department of Management Science, School of Management, Fudan University, 220 Handan Road, Shanghai, 200433, China)

  • JISHAN ZHU

    (School of Business, Long Island University, 1 University Plaza, Brooklyn, New York 11201, USA)

Abstract

In contrast to parametric approaches which try to optimize a single regression function through the observed data set, DEA optimizes on each individual observation with a objective of calculating a discrete piece-wise linear frontier. In the so-called preliminary DEA efficiency evaluation, whether or not a DMU is efficient is solely determined by the solution of the linear programming problem constructed for this particular DMU. In our proposed ranking method, we assess the efficiency performance of a particular DMU based not only on its efficiency score when the DMU is standing out in the objective function of the linear problem but also its efficiency ratios under the optimal solutions of othern-1linear programming problems. This constitutes the overall efficiency performance of a DMU. A ranking procedure is developed to rank thenDMUs in terms of the overall efficiency performance.

Suggested Citation

  • Hongyu Li & Jishan Zhu, 2005. "Ranking The Efficiency Performance Within A Set Of Decision Making Units By Data Envelopment Analysis," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 4(03), pages 345-357.
    • Handle: RePEc:wsi:ijitdm:v:04:y:2005:i:03:n:s0219622005001611
    DOI: 10.1142/S0219622005001611
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    Cited by:

    1. Diogo Cunha Ferreira & Rui Cunha Marques, 2016. "Malmquist and Hicks–Moorsteen Productivity Indexes for Clusters Performance Evaluation," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(05), pages 1015-1053, September.

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