Merging decision-making units with interval data

Free Access

Issue		RAIRO-Oper. Res. Volume 55, 2021 Regular articles published in advance of the transition of the journal to Subscribe to Open (S2O). Free supplement sponsored by the Fonds National pour la Science Ouverte


Page(s)		S1605 - S1631
DOI		https://doi.org/10.1051/ro/2020029
Published online		02 March 2021

G.R. Amin and S. Al-Muharrami, A new inverse data envelopment analysis model for mergers with negative data. IMA J. Manage. Math. 29 (2018) 137–149. [Google Scholar]
G.R. Amin and A. Oukil, Flexible target setting in mergers using inverse data envelopment analysis. Int. J. Oper. Res. 35 (2019) 301–317. [Google Scholar]
G.R. Amin, A. Emrouznejad and S. Gattoufi, Modelling generalized firms’ restructuring using inverse DEA. J. Prod. Anal. 48 (2017) 51–61. [Google Scholar]
G.R. Amin, S. Al-Muharrami and M. Toloo, A combined goal programming and inverse DEA method for target setting in mergers. Expert Syst. App. 115 (2019) 412–417. [Google Scholar]
X. Bai, J. Zeng and Y. Chiu, Pre-evaluating efficiency gains from potential mergers and acquisitions based on the resampling DEA approach: evidence from China’s railway sector. Trans. Policy 76 (2019) 46–56. [Google Scholar]
C. Bernad, L. Fuentelsaz and J. Gmez, The effect of mergers and acquisitions on productivity: an empirical application to Spanish banking. Omega 38 (2010) 283–293. [Google Scholar]
P. Bogetoft and D.X. Wang, Estimating the potential gains from mergers. J. Prod. Anal. 23 (2005) 145–171. [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.D. Cook and L.M. Seiford, Data envelopment analysis (DEA)-Thirty years on. Eur. J. Oper. Res. 192 (2009) 1–17. [Google Scholar]
W.W. Cooper, K.S. Park and G. Yu, IDEA and AR-IDEA: models for dealing with imprecise data in DEA. Manage. Sci. 45 (1999) 597–607. [Google Scholar]
W.W. Cooper, L.M. Seiford and K. Tone, Data Envelopment Analysis: A Comprehensive Text With Models, Applications, References and DEA-Solver Software, Second edition. Springer US, New York, NY (2007). [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 product target setting. Eur. J. Oper. Res. 254 (2016) 510–516. [Google Scholar]
M. Ehrgott, Multicriteria Optimization. Springer, Berlin-Heidelberg (2005). [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, B. Parker and G. Tavares, Evaluation of research in efficiency and productivity: a survey and analysis of the first 30 years of scholarly literature in DEA. J. Socio Econ. Plan. Sci. 42 (2008) 151–157. [Google Scholar]
A. Emrouznejad, M. Rostamy-Malkhalifeh, A. Hatami-Marbini, M. Tavana and N. Aghayi, An overall profit Malmquist productivity index with fuzzy and interval data. Math. Comput. Model. 54 (2011) 2827–2838. [Google Scholar]
A. Emrouznejad, M. Rostamy-Malkhalifeh, A. Hatami-Marbini and M. Tavana, General and multiplicative non-parametric corporate performance models with interval ratio data. Math. Comput. Model. 36 (2012) 5506–5514. [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 (2018) 1–12. [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]
S. Gattoufi, G.R. Amin and A. Emrouznejad, A new inverse DEA method for merging banks. IMA J. Manage. Math. 25 (2014) 73–87. [Google Scholar]
S. Ghobadi, Inputs and outputs estimation in inverse DEA. Iran. J. Optim. 9 (2017) 119–129. [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. [CrossRef] [Google Scholar]
S. Ghobadi and S. Jahangiri, Optimal allocation of resources using the ideal-solutions. J. New Res. Math. 5 (2019) 121–134. [Google Scholar]
S. Ghobadi, G.R. Jahanshahloo, F. Hoseinzadeh Lotfi and M. Rostami-Malkhalifeh, Dynamic inverse DEA in the presence of fuzzy data. Adv. Environ. Biol. 8 (2014) 139–151. [Google Scholar]
S. Ghobadi, G.R. Jahanshahloo, F. Hoseinzadeh Lotfi and M. Rostami-Malkhalifeh, Efficiency measure under inter-temporal dependence. Int. J. Technol. Decis. Making 17 (2018) 657–675. [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]
G.R. Jahanshahloo, F.H. Lotfi and M. Moradi, Sensitivity and stability analysis in DEA with interval data. Appl. Math. Comput. 156 (2004) 463–477. [Google Scholar]
G.R. Jahanshahloo, F.H. Lotfi, N. Shoja, G. Tohidi and S. Razavyan, Sensitivity of efficiency classifications in the inverse DEA models. Appl. Math. Comput. 169 (2005) 905–916. [Google Scholar]
G.R. Jahanshahloo, F. Hoseinzadeh Lotfi, M. Rostami-Malkhalifeh and M. Ahadzadeh Namin, A generalized model for data envelopment analysis with interval data. Appl. Math. Model. 33 (2009) 3237–3244. [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]
C. Kao, Interval efficiency measures in data envelopment analysis with imprecise data. Eur. J. Oper. Res. 174 (2006) 1087–1099. [Google Scholar]
H. Leleu, J. Moises and V. Valdmanis, Optimal productive size of hospitals intensive care units. Int. J. Prod. Econ. 136 (2012) 297–305. [Google Scholar]
F. Li, L. Liang, L. Yongjum and A. Emrouznejad, An alternative approach to decompose the potential gains from mergers. J. Oper. Res. Soc. 69 (2018) 1793–1802. [Google Scholar]
H.T. Lin, An efficiency-driven approach for setting revenue target. Decis. Support Syst. 49 (2010) 311–317. [Google Scholar]
H.H. Liu, T.Y. Chen and L.Y. Pai, The influence of merger and acquisition activities on corporate performance in the Taiwanese telecommunications industry. Serv. Ind. J. 27 (2007) 1041–1051. [Google Scholar]
P. Molyneux, K. Schaeck and T. MiZhou, Too systemically important to fail in banking – evidence from bank mergers and acquisitions. J. Int. Money Finance 49 (2014) 258–282. [Google Scholar]
V. Moonesian, S. Jahangiri and S. Ghobadi, Efficiency and super-efficiency under inter-temporal dependence. RAIRO:OR 54 (2020) 1385–1400. [Google Scholar]
J. Motis, Mergers and acquisitions motives, Working Papers 0730. University of Crete, Department of Economics (2007). [Google Scholar]
J.K. Sengupta, A fuzzy systems approach in data envelopment analysis. Comput. Math. App. 24 (1992) 259–266. [Google Scholar]
X. Shi, Y. Li, A. Emrouznejad, J. Xie and L. Liang, Estimation of potential gains from bank mergers: a novel two-stage cost efficiency DEA model. J. Oper. Res. Soc. 9 (2017) 1045–1055. [Google Scholar]
A.H. Shokouhi, A. Hatami-Marbini, M. Tavana and S. Saati, A robust optimization approach for imprecise data envelopment analysis. Comput. Ind. Eng. 59 (2010) 387–397. [Google Scholar]
Y. Wang, R. Greatbanks and J. Yang, Interval efficiency assessment using data envelopment analysis. Fuzzy Sets Syst. 153 (2005) 347–370. [Google Scholar]
P. Wanke, A. Maredza and R. Gupta, Merger and acquisitions in South African banking: a network DEA model. Res. Int. Bus. Finance 41 (2017) 362–376. [Google Scholar]
J.A. Weber and U.M. Dholakia, Including marketing synergy in acquisition analysis: a step-wise approach. Ind. Market. Manage. 29 (2000) 157–177. [Google Scholar]
M. Wegener and G.R. Amin, Minimizing greenhouse gas emissions using inverse DEA with an application in oil and gas. Expert Syst. App. 122 (2019) 369–375. [Google Scholar]
E. Zeinodin and S. Ghobadi, Merging DMUs based on of the idea inverse DEA. Iran. J. Optim. 11 (2019) 77–84. [Google Scholar]
E. Zeinodin and S. Ghobadi, Merging decision-making units under inter-temporal dependence. IMA J. Manage. Math. 31 (2020) 139–166. [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. [Google Scholar]

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