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
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Page(s) | S571 - S591 | |
DOI | https://doi.org/10.1051/ro/2019092 | |
Published online | 02 March 2021 |
- R.D. Banker, A. Charnes and W.W. Cooper, Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage. Sci. 30 (1984) 1078–1092. [Google Scholar]
- P. Brockett, W. Cooper, H. Deng, L. Golden and T. Ruefli, Using DEA to identify and manage congestion. J. Prod. Anal. 22 (2004) 207–226. [Google Scholar]
- W.W. Cooper, R.G. Thompson and R.M. Thrall, Introduction: extensions and new developments in DEA. Ann. Oper. Res. 66 (1996) 1–45. [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. Kluwer Academic Publishers, Boston (2000). [Google Scholar]
- W.W. Cooper, H. Deng, B. Gu, S. Li and R.M. Thrall, Using DEA to improve the management of congestion in Chinese industries (1981–1997). Soc.-Econ. Plan. Sci. 35 (2001) 227–242. [Google Scholar]
- W.W. Cooper, B. Gu and S. Li, Comparisons and evaluations of alternative approaches to the treatment of congestion in DEA. Eur. J. Oper. Res. 132 (2001) 62–74. [Google Scholar]
- W.W. Cooper, L.M. Seiford and J. Zhu, Handbook on Data Envelopment Analysis. Kluwer Academic Publishers, MA, USA (2004). [Google Scholar]
- M. Ebrahimzade Adimi, M. Rostamy-Malkhalifeh, F.H. Lotfi and R. Mehrjoo, A new linear method to find the congestion hyperplane in DEA. Math. Sci. 13 (2019) 43–52. [Google Scholar]
- A. Emrouznejad and G.L. Yang, A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Soc.-Econ. Plan. Sci. 61 (2018) 4–8. [Google Scholar]
- R. Färe and L. Svensson, Congestion of factors of production. Econometrica 48 (1980) 1745–1753. [Google Scholar]
- R. Fare, S. Grosskopf and C.A.K. Lovell, The measurement of Efficiency of Production. Kluwer-Nijhoff Publishing, Boston, USA (1985). [Google Scholar]
- A. Ghomashi and M. Abbasi, An approach to identify and evaluate congestion in data envelopment analysis. Int. J. Data Envelopment Anal. 5 (2017) 1327–1336. [Google Scholar]
- G.R. Jahanshahloo and M. Khodabakhshi, Suitable combination of inputs for improving outputs in DEA with determining input congestion: considering textile industry of China. Appl. Math. Comput. 151 (2004) 263–273. [Google Scholar]
- C. Kao, Congestion measurement and elimination under the framework of data envelopment analysis. Int. J. Prod. Econ. 123 (2010) 257–265. [Google Scholar]
- H. Kheirollahi, P. Hessari, V. Charles and R. Chawshini, An input relaxation model for evaluating congestion in fuzzy DEA. Croatian Oper. Res. Rev. 8 (2017) 391–408. [Google Scholar]
- M. Khoveyni and R. Eslami, Determining the strongly and weakly most congested firms in data envelopment analysis. International Association for Management of Technology (2017) 1–8. [Google Scholar]
- M. Khoveyni, R. Eslami, M. Khodabakhshi, G.R. Jahanshahloo and F. Hosseinzadeh Lotfi, Recognizing strong and weak congestion slack based in data envelopment analysis. Comput. Ind. Eng. 64 (2013) 731–738. [Google Scholar]
- M. Mehdiloozad, J. Zhu and B.K. Sahoo, Identification of congestion in data envelopment analysis under the occurrence of multiple projections: a reliable method capable of dealing with negative data. Eur. J. Oper. Res. 265 (2018) 644–654. [Google Scholar]
- A.A. Noura, F. Hosseinzadeh Lotfi, G.R. Jahanshahloo, S. Fanati Rashidi and B.R. Parker, A new method for measuring congestion in data envelopment analysis. Soc.-Econ. Plan. Sci. 44 (2010) 240–246. [Google Scholar]
- T. Sueyoshi and K. Sekitani, DEA congestion and returns to scale under an occurrence of multiple optimal projections. Eur. J. Oper. Res. 194 (2009) 592–607. [Google Scholar]
- K. Tone and B.K. Sahoo, Degree of scale economies and congestion: a unified DEA approach. Eur. J. Oper. Res. 158 (2004) 755–772. [Google Scholar]
- P. Wanke, C.P. Barros and A. Emrouznejad, A Comparison between stochastic DEA and Fuzzy DEA approaches: revisiting efficiency in Angolan banks. RAIRO: OR 25 (2018) 285–303. [Google Scholar]
- Q.L. Wei and H. Yan, Congestion and returns to scale in data envelopment analysis. Eur. J. Oper. Res. 153 (2004) 641–660. [Google Scholar]
- Q.L. Wei and H. Yan, Weak congestion in output additive data envelopment analysis. Soc.-Econ. Plan. Sci. 43 (2009) 40–54. [Google Scholar]
- J. Wu, Q. An, B. Xiong and Y. Chen, Congestion measurement for regional industries in China: a data envelopment analysis approach with undesirable outputs. Energy Policy 57 (2013) 7–13. [Google Scholar]
- F. Wu, P. Zhou and D.Q. Zhou, Measuring energy congestion in Chinese industrial sectors: a slacks-based DEA approach. Comput. Econ. 46 (2015) 479–494. [Google Scholar]
- F. Wu, P. Zhou and D.Q. Zhou, Does there exist energy congestion? Empirical evidence from Chinese industrial sectors. Energ. Effic. 9 (2015) 1–14. [Google Scholar]
- G.L. Yang, Directional Congestion in Data Envelopment Analysis. Preprint arXiv:1510.07225 (2015). [Google Scholar]
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