Inverse data envelopment analysis with stochastic data

Open Access

Issue		RAIRO-Oper. Res. Volume 55, Number 5, September-October 2021


Page(s)		2739 - 2762
DOI		https://doi.org/10.1051/ro/2021135
Published online		20 September 2021

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]
M.H. Behzadi and M. Mirbolouki, Symmetric error structure in stochastic DEA. Int. J. Ind. Math. 4 (2012) 335–343. [Google Scholar]
M.H. Behzadi, N. Nematollahi and M. Mirbolouki, Ranking efficient DMUS with stochastic data by considering inefficient frontier. Int. J. Ind. Math. 1 (2009) 219–226. [Google Scholar]
A. Charnes, W.W. Cooper and E. Rhodes, Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2 (1978) 429–444. [Google Scholar]
W.W. Cooper, Z. Huang and S. Li, Satisficing DEA models under chance constraints. Ann. Oper. Res. 66 (1996) 259–279. [Google Scholar]
W.W. Cooper, Z. Huang, V. Lelas, S. Li and O.B. Olesen, Chance constrained programming formulations for stochastic characterizations of efficiency and dominance in DEA. J. Prod. Anal. 9 (1998) 53–80. [Google Scholar]
W.W. Cooper, L.M. Seiford and K. Tone, Data Envelopment Analysis: A Comprehensive Text With Models, Applications. Kluwer Academic Publisher, References and DEA-Solver Software (1999). [Google Scholar]
W.W. Cooper, H. Deng, Z.M. Huang and S.X. Li, Change constrained programming approaches to technical efficiencies and inefficiencies in stochastic data envelopment analysis. J. Oper. Res. Soc. 53 (2002) 1347–1356. [Google Scholar]
D. Despotis and Y. Smirlis, Data envelopment analysis with imprecise data. Eur. J. Oper. Res. 140 (2002) 24–36. [Google Scholar]
L. Dong Joon, Inverse DEA with frontier changes for new target setting. Eur. J. Oper. Res. 254 (2016) 510–516. [Google Scholar]
M. Ehrgott, Multicriteria Optimization. Springer, Berlin (2005). [Google Scholar]
A. Emrouznejad and M. Tavana, Performance Measurement with Fuzzy Data Envelopment Analysis. Springer (2014). [Google Scholar]
A. Emrouznejad and G. Yang, A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. J. Soc.-Econ. Plan. Sci. 61 (2018) 4–8. [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. [Google Scholar]
T. Entani, Y. Maeda and H. Tanaka, Dual models of interval DEA and its extension to interval data. Eur. J. Oper. Res. 136 (2002) 32–45. [Google Scholar]
M.J. Farrell, The measurement of productive efficiency. J. R. Stat. Soc.: Ser. A (General) 120 (1957) 253–281. [CrossRef] [Google Scholar]
S. Gattoufi, G.R. Amin and A. Emrouznejad, A new inverse DEA method for merging banks. IMA J. Manage. Math. 25 (2014) 73–87. [CrossRef] [Google Scholar]
S. Ghobadi, Inverse DEA using enhanced russell measure in the presence of fuzzy data. Int. J. Ind. Math. 10 (2018) 1–16. [Google Scholar]
S. Ghobadi, A generalized DEA model for inputs (outputs) estimation under inter-temporal dependence. RAIRO:OR 53 (2019) 1791–1805. [Google Scholar]
S. Ghobadi, A dynamic DEA model for resource allocation. Int. J. Math. Oper. Res. 17 (2020) 50–77. [Google Scholar]
S. Ghobadi, Merging decision-making units with interval data. RAIRO:OR 55 (2021) 1605–1630. [Google Scholar]
S. Ghobadi and S. Jahangiri, Inverse DEA: review, extension and application. Int. J. Technol. Decis. Making 14 (2015) 805–824. [CrossRef] [Google Scholar]
A. Hadi-Vencheh, A.A. Foroughi and M. Soleimani-Damaneh, A DEA model for resource allocation. Econ. Model. 25 (2008) 983–993. [Google Scholar]
F. Hosseinzadeh Lotfi, N. Nematollahi, M.H. Behzadi, M. Mirbolouki and Zohreh Moghaddas, Centralized resource allocation with stochastic data. J. Comput. Appl. Math. 236 (2012) 1783–1788. [Google Scholar]
Z. Huang and S. Li, Stochastic DEA models with different types of input-output disturbances. J. Prod. Anal. 15 (2001) 95–113. [Google Scholar]
G.R. Jahanshahloo, M.H. Behzadi and M. Mirbolouki, Ranking stochastic efficient dmus based on reliability. Int. J. Ind. Math. 2 (2010) 263–270. [Google Scholar]
G.R. Jahanshahloo, F. Hoseinzadeh Lotfi, M. Rostami-malkhalifeh and S. Ghobadi, Using enhanced russell model to solve inverse data envelopment analysis problems. Sci. World J. 2014 (2014) 1–10. [CrossRef] [Google Scholar]
G.R. Jahanshahloo, M. Soleimani-damaneh and S. Ghobadi, Inverse DEA under inter-temporal dependence using multiple-objective programming. Eur. J. Oper. Res. 240 (2015) 447–456. [Google Scholar]
M. Khodabakhshi, Estimating most productive scale size with stochastic data in data envelopment analysis. Econ. Model. 26 (2009) 968–973. [Google Scholar]
T. Kuosmanen and M. Kortelainen, Stochastic non-smooth envelopment of data: semi-parametric frontier estimation subject to shape constraints. J. Prod. Anal. 38 (2012) 11–28. [Google Scholar]
S. Li, Stochastic models and variable returns to scales in data envelopment analysis. Eur. J. Oper. Res. 104 (1998) 532–548. [CrossRef] [Google Scholar]
H.T. Lin, An efficiency-driven approach for setting revenue target. Decision Support Syst. 49 (2010) 311–317. [Google Scholar]
J.S. Liu, L.Y. Lu, W.M. Lu and B.J. Lin, A survey of DEA applications. Omega 41 (2013) 893–902. [CrossRef] [Google Scholar]
F.H. Lotfi, N. Nematollahi, M. Behzadi and M. Mirbolouki, Ranking decision making units with stochastic data by using coefficient of variation. Math. Comput. App. 15 (2010) 148–155. [Google Scholar]
H. Naseri, S.E. Najafi and A. Saghaei, DEA model considering outputs with stochastic noise and a heavy-tailed (stable) distribution. INFOR: Inf. Syst. Oper. Res. 58 (2020) 87–108. [Google Scholar]
O.B. Olesen and N.C. Petersen, Stochastic data envelopment analysis – a review. Eur. J. Oper. Res. 251 (2016) 2–21. [CrossRef] [Google Scholar]
P. Peykani, E. Mohammadi, M.S. Pishvaee, M. Rostamy-Malkhalifeh and A. Jabbarzadeh, A novel fuzzy data envelopment analysis based on robust possibilistic programming: Possibility, necessity and credibility-based approaches. RAIRO:OR 52 (2018) 1445–1463. [Google Scholar]
P. Peykani, E. Mohammadi, R.F. Saen, S.J. Sadjadi and M. Rostamy-Malkhalifeh, Data envelopment analysis and robust optimization: a review. Expert Syst. 37 (2020) e12534. [Google Scholar]
J.K. Sengupta, Efficiency analysis by stochastic data envelopment analysis. Appl. Econ. Lett. 7 (2002) 379–383. [Google Scholar]
L. Simar, How to improve the performance of DEA/FDH estimators in the presence of noise. J. Prod. Anal. 28 (2007) 1833–201. [Google Scholar]
L. Simar and P.W. Wilson, A general methodology for bootstrapping in non-parametric frontier models. J. Appl. Stat. 27 (2000) 779–802. [Google Scholar]
K. Soleimani-Chamkhorami, F.H. Lotfi, G.R. Jahanshahloo and M. Rostamy-Malkhalifeh, Preserving cost and revenue efficiency through inverse data envelopment analysis models. INFOR: Inf. Syst. Oper. Res. 58 (2020) 561–578. [Google Scholar]
S. Thore, Chance-constrained activity analysis. Eur. J. Oper. Res. 30 (1987) 267–269. [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]
E. Zenodin and S. Ghobadi, Merging decision-making units under inter-temporal dependence. IMA J. Manage. Math. 31 (2020) 139–16. [Google Scholar]
X. Zhang and J. Cui, A project evaluation system in the state economic information system of China: an operation research practice in public sectore. Int. Trans. Oper. 6 (1999) 441–452. [CrossRef] [Google Scholar]
J. Zhu, Imprecise DEA via standard linear DEA models with a revisit to a Korean mobile telecommunication company. Oper. Res. 52 (2004) 323–329. [Google Scholar]

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