Applying inverse DEA and cone constraint to sensitivity analysis of DMUs with undesirable inputs and outputs
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DOI: 10.1057/s41274-016-0004-7
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References listed on IDEAS
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Cited by:
- 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).
- 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.
- 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.
- 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).
- 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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Keywords
DEA; inverse DEA; MOLP; undesirable data; cone constraints;All these keywords.
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