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DEA models for minimizing weight disparity in cross-efficiency evaluation

Author

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  • Y-M Wang

    (Fuzhou University, Fuzhou, PR China)

  • K-S Chin

    (City University of Hong Kong, Kowloon Tong, Hong Kong)

  • S Wang

    (The University of Manchester, Manchester, UK)

Abstract

Cross-efficiency evaluation is a commonly used approach for ranking decision-making units (DMUs) in data envelopment analysis (DEA). The weights used in the cross-efficiency evaluation may sometimes differ significantly among the inputs and outputs. This paper proposes some alternative DEA models to minimize the virtual disparity in the cross-efficiency evaluation. The proposed DEA models determine the input and output weights of each DMU in a neutral way without being aggressive or benevolent to the other DMUs. Numerical examples are tested to show the validity and effectiveness of the proposed DEA models and illustrate their significant role in reducing the number of zero weights.

Suggested Citation

  • Y-M Wang & K-S Chin & S Wang, 2012. "DEA models for minimizing weight disparity in cross-efficiency evaluation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(8), pages 1079-1088, August.
    • Handle: RePEc:pal:jorsoc:v:63:y:2012:i:8:p:1079-1088
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    Citations

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    Cited by:

    1. Oukil, Amar, 2020. "Exploiting value system multiplicity and preference voting for robust ranking," Omega, Elsevier, vol. 94(C).
    2. Jie Wu & Junfei Chu & Qingyuan Zhu & Pengzhen Yin & Liang Liang, 2016. "DEA cross-efficiency evaluation based on satisfaction degree: an application to technology selection," International Journal of Production Research, Taylor & Francis Journals, vol. 54(20), pages 5990-6007, October.
    3. Shiang-Tai Liu & Yueh-Chiang Lee, 2021. "Fuzzy measures for fuzzy cross efficiency in data envelopment analysis," Annals of Operations Research, Springer, vol. 300(2), pages 369-398, May.
    4. Alcaraz, Javier & Ramón, Nuria & Ruiz, José L. & Sirvent, Inmaculada, 2013. "Ranking ranges in cross-efficiency evaluations," European Journal of Operational Research, Elsevier, vol. 226(3), pages 516-521.
    5. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min, 2016. "Research fronts in data envelopment analysis," Omega, Elsevier, vol. 58(C), pages 33-45.
    6. Ruiz, José L., 2013. "Cross-efficiency evaluation with directional distance functions," European Journal of Operational Research, Elsevier, vol. 228(1), pages 181-189.
    7. Davtalab-Olyaie, Mostafa & Asgharian, Masoud, 2021. "On Pareto-optimality in the cross-efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 288(1), pages 247-257.
    8. Qingyuan Zhu & Jie Wu & Malin Song & Liang Liang, 2017. "A unique equilibrium efficient frontier with fixed-sum outputs in data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(12), pages 1483-1490, December.
    9. Hamid Kiaei & Reza Farzipoor Saen & Reza Kazemi Matin, 2023. "Cross-efficiency evaluation and improvement in two-stage network data envelopment analysis," Annals of Operations Research, Springer, vol. 321(1), pages 281-309, February.
    10. Ebrahimi, Bohlool & Dhamotharan, Lalitha & Ghasemi, Mohammad Reza & Charles, Vincent, 2022. "A cross-inefficiency approach based on the deviation variables framework," Omega, Elsevier, vol. 111(C).
    11. Kao, Chiang & Liu, Shiang-Tai, 2020. "A slacks-based measure model for calculating cross efficiency in data envelopment analysis," Omega, Elsevier, vol. 95(C).

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