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An aggressive game cross-efficiency evaluation in data envelopment analysis

Author

Listed:
  • Wenli Liu

    (Fuzhou University
    Fuzhou University)

  • Ying-Ming Wang

    (Fuzhou University)

  • Shulong Lv

    (Fuzhou University
    Fuzhou University)

Abstract

Cross-efficiency evaluation is an effective method for ranking decision making units (DMUs) in data envelopment analysis, which is performed with peer-evaluation and self-evaluation. From different points of view, various cross-efficiency evaluations have been proposed with different secondary goals. Yet they usually lead to different average cross-efficiencies and different rankings. In this paper, we develop a concept of the aggressive game cross-efficiency, and propose an aggressive secondary model to minimize the cross-efficiencies of other DMUs under the constraints that the aggressive game cross-efficiency of the evaluated DMU is guaranteed. To achieve the aggressive game cross-efficiency, we develop an iterative algorithm. Mathematically, it is proved that all conventional average cross-efficiencies are sure to converge to the same aggressive game cross-efficiency by the iterative algorithm. Finally, numerical examples are presented to show the effectiveness of our approach in evaluating and ranking DMUs.

Suggested Citation

  • Wenli Liu & Ying-Ming Wang & Shulong Lv, 2017. "An aggressive game cross-efficiency evaluation in data envelopment analysis," Annals of Operations Research, Springer, vol. 259(1), pages 241-258, December.
    • Handle: RePEc:spr:annopr:v:259:y:2017:i:1:d:10.1007_s10479-017-2524-1
    DOI: 10.1007/s10479-017-2524-1
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