Open Access
RAIRO-Oper. Res.
Volume 55, Number 4, July-August 2021
Page(s) 2189 - 2202
Published online 14 July 2021
  • S. Agarwal, Efficiency measure by fuzzy data envelopment analysis model. Fuzzy Inf. Eng. 1 6 (2014) 59–70. [Google Scholar]
  • S. Agarwal, SBM data envelopment analysis in fuzzy environment. Math. Sci. Int. Res. J. 3 (2014) 478–484. [Google Scholar]
  • S. Agarwal, Fuzzy slack based measure of data envelopment analysis: a possibility approach. In: Proceedings of the Third International Conference on Soft Computing for Problem Solving (2014) 733–740. [Google Scholar]
  • S. Agarwal, S.P. Yadav and S.P. Singh, DEA based estimation of the technical efficiency of state transport undertakings in India. Opsearch 47 (2010) 216–230. [Google Scholar]
  • M. Arana-Jiménez, M.C. Sánchez-Gil and S. Lozano, A fuzzy DEA slacks-based approach. J. Comput. Appl. Math. (2020) 113180. [Google Scholar]
  • M. Arana-Jimenez, M.C. Sánchez-Gil and S. Lozano, Efficiency assessment and target setting using a fully fuzzy DEA approach. Int. J. Fuzzy Syst. 22 (2020) 1056–1072. [Google Scholar]
  • R.D. Baker, Maximum likelihood, consistency and DEA statistical foundations. Manage. Sci. 39 (1993) 1265–1273. [CrossRef] [Google Scholar]
  • A. Charnes and W.W. Cooper, Chance-constrained programming. Manage. Sci. 6 (1959) 73–79. [Google Scholar]
  • A. Charnes and L. Neralić, Sensitivity analysis of the additive model in data envelopment analysis. Eur. J. Oper. Res. 48 (1990) 332–341. [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]
  • A. Charnes, W.W. Cooper, B. Golany, L. Seiford and J. Stutz, Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions. J. Econometrics 30 (1985) 91–107. [Google Scholar]
  • W.W. Cooper, L.M. Seiford and K. Tone, Data envelopment analysis. In: Handbook on Data Envelopment Analysis (2000) 1–40. [Google Scholar]
  • A. Emrouznejad and A. Mustafa, Fuzzy data envelopment analysis: a discrete approach. Expert Syst. App. 39 (2012) 2263–2269. [Google Scholar]
  • A. Emrouznejad and M. Tavana, Performance Measurement with Fuzzy Data Envelopment Analysis. Springer (2014). [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. Planning Sci. 61 (2018) 4–8. [CrossRef] [Google Scholar]
  • P. Guo and H. Tanaka, Fuzzy DEA: a perceptual evaluation method. Fuzzy Sets Syst. 119 (2001) 149–160. [CrossRef] [Google Scholar]
  • P. Gupta, M.K. Mehlawat, A. Kumar, S. Yadav and A. Aggarwal, A credibilistic fuzzy DEA approach for portfolio efficiency evaluation and rebalancing toward benchmark portfolios using positive and negative returns. Int. J. Fuzzy Syst. 22 (2020) 824–843. [Google Scholar]
  • C. Heydari, H. Omrani and R. Taghizadeh, A fully fuzzy network DEA-Range Adjusted Measure model for evaluating airlines efficiency: a case of Iran. J. Air Transp. Manage. 89 (2020) 101923. [Google Scholar]
  • G.R. Jahanshahloo, M. Soleimani-Damaneh and E. Nasrabadi, Measure of efficiency in DEA with fuzzy input–output levels: a methodology for assessing, ranking and imposing of weights restrictions. Appl. Math. Comput. 156 (2004) 175–187. [Google Scholar]
  • G.R. Jahanshahloo, F. Hosseinzadeh, N. Shoja, M. Sanei and G. Tohidi, Sensitivity and stability analysis in DEA. Appl. Math. Comput. 169 (2005) 897–904. [Google Scholar]
  • D. Kahneman and A. Tversky, Prospect theory: an analysis of decision under risk. Econometrics 47 (1979) 263–291. [Google Scholar]
  • C. Kao and S.T. Liu, Fuzzy efficiency measures in data envelopment analysis. Fuzzy Sets Syst. 113 (2000) 427–437. [Google Scholar]
  • M.Z.A. Langroudi, A. Emrouznejad, A. Mustafa and J. Ignatius, Type-2 TOPSIS: a group decision problem when ideal values are not extreme endpoints. Group Decision Negotiation 22 (2013) 851–866. [Google Scholar]
  • S. Lertworasirikul, S.C. Fang, J.A. Joines and H.L. Nuttle, Fuzzy data envelopment analysis (DEA): a possibility approach. Fuzzy Sets Syst. 139 (2003) 379–394. [CrossRef] [Google Scholar]
  • S. Lertworasirikul, S.C. Fang, J. Joines and H. Nuttle, Fuzzy data envelopment analysis: a credibility approach. Fuzzy Sets Based Heuristics Optim. (2003) 141–158. [Google Scholar]
  • B. Liu, Uncertainty Theory. Springer, Berlin, Heidelberg (2007) 205–234. [Google Scholar]
  • B. Liu, Some research problems in uncertainty theory. J. Uncertain Syst. 3 (2009) 3–10. [Google Scholar]
  • B. Liu, Uncertainty Theory. Springer, Berlin, Heidelberg (2010) 1–79. [Google Scholar]
  • B. Liu and Y.K. Liu, Expected value of fuzzy variable and fuzzy expected value models. IEEE Trans. Fuzzy Syst. 10 (2002) 445–450. [Google Scholar]
  • F.H. Lotfi, M.A. Jondabeh and M. Faizrahnemoon, Senstivity analysis in fuzzy environment. Appl. Math. Sci. 4 (2010) 1635–1646. [Google Scholar]
  • C.K. Lovell, Measuring the macroeconomic performance of the Taiwanese economy. Int. J. Prod. Econ. 39 (1995) 165–178. [Google Scholar]
  • D. Mahla, S. Agarwal, A credibility approach on fuzzy Slacks-Based Measure (SBM) model (2020). DOI: 10.22111/ijfs.2020.31572.5443 (In Press). [Google Scholar]
  • R. Mahmoudi, A. Emrouznejad, S.N. Shetab-Boushehri and S.R. Hejazi, The origins, development and future directions of data envelopment analysis approach in transportation systems. Soc.-Econ. Plann. Sci. 69 (2020) 100672. [Google Scholar]
  • L. Neralić and R.E. Wendell, Sensitivity in data envelopment analysis using an approximate inverse matrix. J. Oper. Res. Soc. 55 (2004) 1187–1193. [Google Scholar]
  • L. Neralić and R.E. Wendell, Enlarging the radius of stability and stability regions in Data Envelopment Analysis. Eur. J. Oper. Res. 278 (2019) 430–441. [Google Scholar]
  • O.B. Olesen and N.C. Petersen, Chance constrained efficiency evaluation. Manage. Sci. 41 (1995) 442–457. [CrossRef] [Google Scholar]
  • S. Saati and A. Memariani, SBM model with fuzzy input-output levels in DEA. Aust. J. Basic Appl. Sci. 3 (2009) 352–357. [Google Scholar]
  • M. Sanei, N. Noori and H. Saleh, Sensitivity analysis with fuzzy data in DEA. Appl. Math. Sci. 3 (2009) 1235–1241. [Google Scholar]
  • L.M. Seiford and J. Zhu, Sensitivity and stability of the classifications of returns to scale in data envelopment analysis. J. Prod. Anal. 12 (1999) 55–75. [Google Scholar]
  • J.K. Sengupta, Efficiency measurement in stochastic input-output systems. Int. J. Syst. Sci. 13 (1982) 273–287. [Google Scholar]
  • P. Smith, Model misspecification in data envelopment analysis. Ann. Oper. Res. 73 (1997) 233–252. [Google Scholar]
  • K. Tone, A slacks-based measure of efficiency in data envelopment analysis. Eur. J. Oper. Res. 130 (2001) 498–509. [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 52 (2018) 285–303. [Google Scholar]
  • M. Wen, C. You and R. Kang, A new ranking method to fuzzy data envelopment analysis. Comput. Math. App. 59 (2010) 3398–3404. [Google Scholar]
  • M. Wen, Z. Qin and R. Kang, Sensitivity and stability analysis in fuzzy data envelopment analysis. Fuzzy Optim. Decis. Making 10 (2011) 1–10. [Google Scholar]
  • M. Wen, Z. Qin, R. Kang and Y. Yang, Sensitivity and stability analysis of the additive model in uncertain data envelopment analysis. Soft Comput. 19 (2015) 1987–1996. [Google Scholar]
  • L.A. Zadeh, Fuzzy sets. Inf. Control 8 (1965) 338–353. [Google Scholar]
  • M. Zahedi-Seresht, G.R. Jahanshahloo and J. Jablonsky, A robust data envelopment analysis model with different scenarios. Appl. Math. Modell. 52 (2017) 306–319. [Google Scholar]
  • Z. Zhou, E. Chen, H. Xiao, T. Ren and Q. Jin, Performance evaluation of portfolios with fuzzy returns. RAIRO:OR 53 (2019) 1581–1600. [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.