Open Access
Issue
RAIRO-Oper. Res.
Volume 55, Number 4, July-August 2021
Page(s) 2189 - 2202
DOI https://doi.org/10.1051/ro/2021097
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. [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. [Google Scholar]
  • P. Guo and H. Tanaka, Fuzzy DEA: a perceptual evaluation method. Fuzzy Sets Syst. 119 (2001) 149–160. [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. [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. [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]

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