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The extension and integration of the inverse DEA method

Author

Listed:
  • Meng Zhang

    (Academy of Mathematics and Systems Science, Chinese Academy of Sciences)

  • Jin-chuan Cui

    (Academy of Mathematics and Systems Science, Chinese Academy of Sciences)

Abstract

The inverse DEA (Data Envelopment Analysis) method is primarily used to analyse the changing relationship between the inputs and outputs of a DMU (Decision-Making Unit) when its efficiency is kept constant or set to a target value. However, the existing inverse DEA method cannot be applied directly to estimate all the changing relationships. For example, the existing DEA models fail to estimate the input variations when the supervisor wants to maintain the DMU’s output-oriented efficiency during the downscaling of production. This paper analyses all the possible changing relationships that need to be solved by the inverse DEA method and develops different models for both the output and input orientations, accomplishing the extension and integration of the inverse DEA model. For illustration of our results, a numerical example is given.

Suggested Citation

  • Meng Zhang & Jin-chuan Cui, 2016. "The extension and integration of the inverse DEA method," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(9), pages 1212-1220, September.
    • Handle: RePEc:pal:jorsoc:v:67:y:2016:i:9:d:10.1057_jors.2016.2
    DOI: 10.1057/jors.2016.2
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    References listed on IDEAS

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    Cited by:

    1. Moghaddas, Zohreh & Tosarkani, Babak Mohamadpour & Yousefi, Samuel, 2022. "Resource reallocation for improving sustainable supply chain performance: An inverse data envelopment analysis," International Journal of Production Economics, Elsevier, vol. 252(C).
    2. Gholam R. Amin & Ali Emrouznejad & Said Gattoufi, 2017. "Modelling generalized firms’ restructuring using inverse DEA," Journal of Productivity Analysis, Springer, vol. 48(1), pages 51-61, August.
    3. Ehrgott, Matthias & Holder, Allen & Nohadani, Omid, 2018. "Uncertain Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 268(1), pages 231-242.
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    5. Xiaoyin Hu & Jianshu Li & Xiaoya Li & Jinchuan Cui, 2020. "A Revised Inverse Data Envelopment Analysis Model Based on Radial Models," Mathematics, MDPI, vol. 8(5), pages 1-17, May.
    6. 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.
    7. Qingxian An & Xuyang Liu & Yongli Li & Beibei Xiong, 2019. "Resource planning of Chinese commercial banking systems using two-stage inverse data envelopment analysis with undesirable outputs," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-20, June.
    8. Wen-Chi Yang & Wen-Min Lu, 2023. "Achieving Net Zero—An Illustration of Carbon Emissions Reduction with A New Meta-Inverse DEA Approach," IJERPH, MDPI, vol. 20(5), pages 1-20, February.

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