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Applying inverse DEA and cone constraint to sensitivity analysis of DMUs with undesirable inputs and outputs

Author

Listed:
  • M. Eyni

    (Payame Noor University)

  • G. Tohidi

    (Islamic Azad University, Central Tehran Branch)

  • S. Mehrabeian

    (Kharazmi University)

Abstract

In this paper, the inverse data envelopment analysis (DEA) with the preference of cone constraints will be discussed in a way that in the decision-making units, the undesirable inputs and outputs exist simultaneously. Supposing that the efficiency level does not change, if the unit under assessment increases the level of the desirable outputs and decreases the level of the undesirable outputs, how will it affect the amount of the desirable input level and the undesirable input level? To answer this question, the application of the inverse DEA with preference of cone constraints is suggested. The suggested approach, while maintaining the efficiency level, increases the level of its undesirable input and decreases the level of its desirable input by selection of strongly efficient solutions or some weakly efficient solutions of the multiple objective linear programming (MOLP) model. While maintaining the efficiency level, the suggested approach by selection of strongly efficient solution or some of the weakly efficient solutions of the MOLP model can increase the undesirable input level and decrease the desirable input level. Similarly, the suggested approach can be applied if the decision-making unit increases its undesirable input level and decreases the desirable input level so that the undesirable output level decreases and the desirable output level increases while maintaining the efficiency level. As an illustration, two numerical examples are rendered.

Suggested Citation

  • M. Eyni & G. Tohidi & S. Mehrabeian, 2017. "Applying inverse DEA and cone constraint to sensitivity analysis of DMUs with undesirable inputs and outputs," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(1), pages 34-40, January.
  • Handle: RePEc:pal:jorsoc:v:68:y:2017:i:1:d:10.1057_s41274-016-0004-7
    DOI: 10.1057/s41274-016-0004-7
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    References listed on IDEAS

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    1. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
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    3. Wei, Quanling & Zhang, Jianzhong & Zhang, Xiangsun, 2000. "An inverse DEA model for inputs/outputs estimate," European Journal of Operational Research, Elsevier, vol. 121(1), pages 151-163, February.
    4. Yan, Hong & Wei, Quanling & Hao, Gang, 2002. "DEA models for resource reallocation and production input/output estimation," European Journal of Operational Research, Elsevier, vol. 136(1), pages 19-31, January.
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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. 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.
    3. Gholam R. Amin & Mustapha Ibn Boamah, 2020. "A new inverse DEA cost efficiency model for estimating potential merger gains: a case of Canadian banks," Annals of Operations Research, Springer, vol. 295(1), pages 21-36, December.
    4. Ghiyasi, Mojtaba & Soltanifar, Mehdi & Sharafi, Hamid, 2022. "A novel inverse DEA-R model with application in hospital efficiency," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    5. 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.

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