Open Access
Issue
RAIRO-Oper. Res.
Volume 58, Number 1, January-February 2024
Page(s) 557 - 577
DOI https://doi.org/10.1051/ro/2023197
Published online 19 February 2024
  • G.R. Amin and M. Ibn-Boamah, Modeling business partnerships: a data envelopment analysis approach. Eur. J. Oper. Res. 305 (2023) 329–337. [CrossRef] [Google Scholar]
  • G.R. Amin, A. Emrouznejad and S. Gattoufi, Minor and major consolidations in inverse DEA: Definition and determination. Comput. Ind. Eng. 103 (2017) 193–200. [Google Scholar]
  • G.R. Amin, A. Emrouznejad and S. Gattoufi, Modelling generalized firms–restructuring using inverse DEA. J. Prod. Anal. 48 (2017) 51–61. [CrossRef] [Google Scholar]
  • R.D. Banker, A. Charnes and W.W. Cooper, Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag. Sci. 30 (1984) 1078–1092. [Google Scholar]
  • A. Charnes, W.W. Cooper and E. Rhodes, Measuring the efficiency of decision making DMUs. Eur. J. Oper. Res. 2 (1978) 429–444. [CrossRef] [Google Scholar]
  • L. Chen and Y.M. Wang, Limitation and optimization of inputs and outputs in the inverse data envelopment analysis under variable returns to scale. Expert Syst. Appl. 183 (2021) 115344. [CrossRef] [Google Scholar]
  • Y.C. Chen, Y.H. Chiu, C.W. Huang and C.H. Tu, The analysis of bank business performance and market risk–Applying Fuzzy DEA. Econ. Model. 32 (2013) 225–232. [CrossRef] [Google Scholar]
  • A. Emrouznejad and G.R. Amin, Advances in inverse data envelopment analysis: empowering performance assessment. IMA J. Manag. Math. 34 (2023) 415–419. [MathSciNet] [Google Scholar]
  • A. Emrouznejad, G.L. Yang and G.R. Amin, A novel inverse DEA model with application to allocate the CO2 emissions quota to different regions in Chinese manufacturing industries. J. Oper. Res. Soc. 70 (2019) 1079–1090. [CrossRef] [Google Scholar]
  • A. Emrouznejad, G.R. Amin, M. Ghiyasi and M. Michali, A review of inverse data envelopment analysis: origins, development and future directions. IMA J. Manag. Math. 34 (2023) 421–440. [MathSciNet] [Google Scholar]
  • S. Gattoufi, G.R. Amin and A. Emrouznejad, A new inverse DEA method for merging banks. IMA J. Manag. Math. 25 (2014) 73–87. [Google Scholar]
  • J. Gerami, M.R. Mozaffari, P.F. Wanke and H.L. Correa, A generalized inverse DEA model for firm resturcturing based on value efficiency. IMA J. Manag. Math. 34 (2023) 541–580. [MathSciNet] [Google Scholar]
  • M. Ghiyasi, Inverse DEA based on cost and revenue efficiency. Comput. Ind. Eng. 114 (2017) 258–263. [Google Scholar]
  • M. Ghiyasi and N. Zhu, An inverse semi-oriented radial data envelopment analysis measure for dealing with negative data. IMA J. Manag. Math. 31 (2020) 505–516. [MathSciNet] [Google Scholar]
  • S. Ghobadi, A generalized DEA model for inputs (outputs) estimation under inter-temporal dependence. RAIRO:RO 53 (2019) 1791–1805. [CrossRef] [EDP Sciences] [Google Scholar]
  • S. Ghobadi, Merging decision-making units with internal data. RAIRO:RO 55 (2021) S1605–S1631. [CrossRef] [EDP Sciences] [Google Scholar]
  • A. Hadi-Vencheh and A.A. Foroughi, A generalized DEA model for inputs/outputs estimation. Math. Comput. Model. 43 (2006) 447–457. [Google Scholar]
  • A. Kazemi and D.U.A. Galagedera, An inverse DEA model for intermediate and output target setting in serially linked general two-stage processes. IMA J. Manag. Math. 34 (2023) 511–539. [MathSciNet] [Google Scholar]
  • P. Korhonen and M. Syrj¨anen, Resource allocation based on efficiency analysis. Manag. Sci. 50 (2004) 1134–114. [CrossRef] [Google Scholar]
  • S. Lertworasirikul, P. Charnsethikul and S.C. Fang, Inverse data envelopment analysis model to preserve relative efficiency values: The case of variable returns to scale. Comput. Ind. Eng. 61 (2011) 1017–1023. [CrossRef] [Google Scholar]
  • D.J. Lim, Inverse DEA with frontier changes for new product target setting. Eur. J. Oper. Res. 254 (2016) 510–516. [CrossRef] [Google Scholar]
  • J.C. Lu, M.J. Li and Z.J. Shen, A new inverse DEA model with frontier changes for analyzing the achievement path of CO2 emissions target of China in 2030. J. Clean. Prod. 375 (2022) 134014. [CrossRef] [Google Scholar]
  • M. Mahla, S. Agarwal, G.R Amin and T. Mathur, An inverse data envelopment analysis model to consider ratio data and preferences of decision-makers. IMA J. Manag. Math. 34 (2023) 441–464. [MathSciNet] [Google Scholar]
  • Z. Moghaddas, B.M. Tosarkani and S. Yousefi, Resource reallocation for improving sustainable supply chain performance: an inverse data envelopment analysis. Int. J. Prod. Econ. 252 (2022) 108560. [CrossRef] [Google Scholar]
  • V.V. Podinovski, Production trade-offs and weight restrictions in data envelopment analysis. J. Oper. Res. Soc. 55 (2004) 1311–1322. [CrossRef] [Google Scholar]
  • V.V. Podinovski, Optimal weights in DEA models with weight restrictions. Eur. J. Oper. Res. 254 (2016) 916–924. [CrossRef] [Google Scholar]
  • Y. Roll, W.D. Cook and B. Golany, Controlling factor weights in data envelopment analysis. IIE Trans. 23 (1991) 2–9. [CrossRef] [Google Scholar]
  • T. Sayar, M. Ghiyasi and J. Fathali, New inverse DEA models for budgeting and planning. RAIRO:RO 55 (2021) 1933–1948. [CrossRef] [EDP Sciences] [Google Scholar]
  • K.H. Soleimani-Chamkhorami, F. Lotfi, G.R. Jahanshahloo and M. Rostamy-Malkhalifeh, A ranking system based on inverse data envelopment analysis. IMA J. Manag. Math. 31 (2020) 367–385. [MathSciNet] [Google Scholar]
  • M. Soltanifar, M. Ghiyasi and H. Sharafi, Inverse DEA-R models for merger analysis with negative data. IMA J. Manag. Math. 34 (2023) 491–510. [MathSciNet] [Google Scholar]
  • M. Wegener and G.R. Amin, Minimizing greenhouse gas emissions using inverse DEA with an application in oil and gas. Expert Syst. Appl. 122 (2019) 369–375. [Google Scholar]
  • Q.L. Wei, J.Z. Zhang and X.S. Zhang, An inverse DEA model for inputs/outputs estimate. Eur. J. Oper. Res. 121 (2000) 151–163. [Google Scholar]
  • H. Yan, Q.L. Wei and G. Hao, DEA models for resource reallocation and production input/output estimation. Eur. J. Oper. Res. 136 (2002) 19–31. [Google Scholar]
  • E. Zeinodin and S. Ghobadi, Merging decision-making units under inter-temporal dependence. IMA J. Manag. Math. 31 (2020) 139–166. [Google Scholar]
  • X.S. Zhang and J.C. Cui, A project evaluation system in the state economic information system of China: An operations research practice in public sectors. Int. Trans. Oper. Res. 6 (1999) 441–452. [CrossRef] [Google Scholar]
  • M. Zhang and J.C. Cui, The extension and integration of the inverse DEA method. J. Oper. Res. Soc. 67 (2016) 1212–1220. [CrossRef] [Google Scholar]
  • W.W. Zhu, Y.J. Huang and Y. Yu, DEA model for partial centralization resource allocation among independent subset of DMUs. Comput. Ind. Eng. 176 (2023) 109013. [CrossRef] [Google Scholar]

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