Free Access
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) S571 - S591
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]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.