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Preference, Production and Performance in Data Envelopment Analysis

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  • Wenbin Liu
  • John Sharp
  • Zhongmin Wu

Abstract

This paper attempts to provide a systematic approach to the DEA model building. To this end, we try to identify some essential aspects of DEA modelling. Three key building blocks in a DEA model are identified: they are preference order, production possibility set and performance measure. It is shown that the preferences and performance measurements used in the standard DEA models are only particular examples in this framework. It is also illustrated in this work that this methodology is useful in building new DEA models to handle nonstandard applications such as those involve non-Pareto preferences or undesirable inputs-outputs. Copyright Springer Science+Business Media, LLC 2006

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  • Wenbin Liu & John Sharp & Zhongmin Wu, 2006. "Preference, Production and Performance in Data Envelopment Analysis," Annals of Operations Research, Springer, vol. 145(1), pages 105-127, July.
    • Handle: RePEc:spr:annopr:v:145:y:2006:i:1:p:105-127:10.1007/s10479-006-0042-7
    DOI: 10.1007/s10479-006-0042-7
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    References listed on IDEAS

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    Cited by:

    1. Helmi Hammami & Thanh Ngo & David Tripe & Dinh-Tri Vo, 2022. "Ranking with a Euclidean common set of weights in data envelopment analysis: with application to the Eurozone banking sector," Annals of Operations Research, Springer, vol. 311(2), pages 675-694, April.
    2. Liu, W.B. & Zhang, D.Q. & Meng, W. & Li, X.X. & Xu, F., 2011. "A study of DEA models without explicit inputs," Omega, Elsevier, vol. 39(5), pages 472-480, October.
    3. Chen, Chailin & Cook, Wade D. & Imanirad, Raha & Zhu, Joe, 2020. "Balancing Fairness and Efficiency: Performance Evaluation with Disadvantaged Units in Non-homogeneous Environments," European Journal of Operational Research, Elsevier, vol. 287(3), pages 1003-1013.
    4. J A Sharp & W Meng & W Liu, 2007. "A modified slacks-based measure model for data envelopment analysis with ‘natural’ negative outputs and inputs," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(12), pages 1672-1677, December.
    5. H Chang & C Galantine & A Thevaranjan, 2009. "Returns to scale pattern and efficient firm size in the public accounting industry: an empirical investigation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(11), pages 1495-1501, November.
    6. Dyckhoff, Harald & Souren, Rainer, 2022. "Integrating multiple criteria decision analysis and production theory for performance evaluation: Framework and review," European Journal of Operational Research, Elsevier, vol. 297(3), pages 795-816.
    7. Victoria Wojcik & Harald Dyckhoff & Sebastian Gutgesell, 2017. "The desirable input of undesirable factors in data envelopment analysis," Annals of Operations Research, Springer, vol. 259(1), pages 461-484, December.
    8. Lim, Sungmook & Zhu, Joe, 2013. "Incorporating performance measures with target levels in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 230(3), pages 634-642.
    9. Wanfang Shen & Guoliang Yang & Zhongbao Zhou & Wenbin Liu, 2019. "DEA models with Russell measures," Annals of Operations Research, Springer, vol. 278(1), pages 337-359, July.
    10. W. Liu & W. Meng & X. Li & D. Zhang, 2010. "DEA models with undesirable inputs and outputs," Annals of Operations Research, Springer, vol. 173(1), pages 177-194, January.

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