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All issues Volume 59 / No 4 (July-August 2025) RAIRO-Oper. Res., 59 4 (2025) 2325-2357 References
Open Access
Issue
RAIRO-Oper. Res.
Volume 59, Number 4, July-August 2025
Page(s) 2325 - 2357
DOI https://doi.org/10.1051/ro/2025102
Published online 05 September 2025
  • J. Puri and S.P. Yadav, Performance evaluation of public and private sector banks in India using DEA approach. Int. J. Oper. Res. 18 (2013) 91–121. [CrossRef] [Google Scholar]
  • M.J. Farrell, The measurement of productive efficiency. J. R. Stat. Soc. Ser. A (General) 120 (1957) 253–281. [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.W. Cooper, L.M. Seiford and K. Tone, Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software. Vol. 2. Springer, New York (2007). [Google Scholar]
  • A. Emrouznejad and G.-L. Yang, A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Econ. Plan. Sci. 61 (2018) 4–8. [CrossRef] [Google Scholar]
  • W. Cooper, L. Seiford and J. Zhu, Handbook on Data Envelopment Analysis. Springer, New York (2011). [Google Scholar]
  • C. Kao, Efficiency decomposition for general multi-stage systems in data envelopment analysis. Eur. J. Oper. Res. 232 (2014) 117–124. [Google Scholar]
  • C. Kao, Network data envelopment analysis: a review. Eur. J. Oper. Res. 239 (2014) 1–16. [Google Scholar]
  • R. F¨are, S. Grosskopf and G. Whittaker, Network DEA II, in Data Envelopment Analysis. Springer, New York (2014) 307–327. [Google Scholar]
  • A. Amirteimoori and F. Yang, A DEA model for two-stage parallel-series production processes. RAIRO-Oper. Res. 48 (2014) 123–134. [Google Scholar]
  • L.M. Seiford and J. Zhu, Modeling undesirable factors in efficiency evaluation. Eur. J. Oper. Res. 142 (2002) 16–20. [Google Scholar]
  • C.A. Knox Lovell and J.T. Pastor, Units invariant and translation invariant DEA models. Oper. Res. Lett. 18 (1995) 147–151. [CrossRef] [MathSciNet] [Google Scholar]
  • M.C.A. Silva Portela, E. Thanassoulis and G. Simpson, Negative data in DEA: a directional distance approach applied to bank branches. J. Oper. Res. Soc. 55 (2004) 1111–1121. [CrossRef] [Google Scholar]
  • K. Tone, T.-S. Chang and C.-H. Wu, Handling negative data in slacks-based measure data envelopment analysis models. Eur. J. Oper. Res. 282 (2020) 926–935. [CrossRef] [Google Scholar]
  • R.G. Chambers, Y. Chung and R. F¨are, Benefit and distance functions. J. Econ. Theory 70 (1996) 407–419. [CrossRef] [Google Scholar]
  • R. Lin and Y. Liu, Super-efficiency based on the directional distance function in the presence of negative data. Omega 85 (2019) 26–34. [CrossRef] [Google Scholar]
  • S. Sarkar, Performance measurement using a novel directional distance function based super efficiency model and neighbourhood theory. RAIRO-Oper. Res. 55 (2021) 3617–3638. [Google Scholar]
  • G. Halkos and K.N. Petrou, Treating undesirable outputs in DEA: a critical review. Econ. Anal. Policy 62 (2019) 97–104. [CrossRef] [Google Scholar]
  • S. Yu, J. Liu and L. Li, Evaluating provincial eco-efficiency in China: an improved network data envelopment analysis model with undesirable output. Environ. Sci. Poll. Res. 27 (2020) 6886–6903. [Google Scholar]
  • X. Shi, A. Emrouznejad and W. Yu, Overall efficiency of operational process with undesirable outputs containing both series and parallel processes: a SBM network DEA model. Expert Syst. Appl. 178 (2021) 115062. [CrossRef] [Google Scholar]
  • K. Asanimoghadam, M. Salahi, A. Jamalian and R. Shakouri, A two-stage structure with undesirable outputs: slacks-based and additive slacks-based measures DEA models. RAIRO-Oper. Res. 56 (2022) 2513–2534. [Google Scholar]
  • K. Khalili-Damghani and Z. Shahmir, Uncertain network data envelopment analysis with undesirable outputs to evaluate the efficiency of electricity power production and distribution processes. Comput. Ind. Eng. 88 (2015) 131–150. [Google Scholar]
  • T. Hassani and M. Rostamy-Malkhalifeh, Efficiency of decision making units in network DEA using interval data. Int. J. Data Envel. Anal. 4 (2016) 1087–1094. [Google Scholar]
  • F.S.S. Esmaeili, M. Rostamy-Malkhalifeh and F.H. Lotfi, Two-stage network DEA model under interval data. Math. Anal. Convex Optim. 1 (2020) 103–108. [Google Scholar]
  • N. Zhang, A. Kalhor, R. Azizi and R. Kazemi-Matin, Improved efficiency assessment in network DEA through interval data analysis: an empirical study in agriculture. RAIRO-Oper. Res. 57 (2023) 3007–3031. [Google Scholar]
  • H. Drucker, C.J.C. Burges, L. Kaufman, A. Smola and V. Vapnik, Support vector regression machines. Adv. Neural Inf. Process. Syst. 9 (1997) 155–161. [Google Scholar]
  • J.A.K. Suykens and J. Vandewalle, Least squares support vector machine classifiers. Neural Process. Lett. 9 (1999) 293–300. [Google Scholar]
  • R. Lin and Q. Liu, Multiplier dynamic data envelopment analysis based on directional distance function: an application to mutual funds. Eur. J. Oper. Res. 293 (2021) 1043–1057. [Google Scholar]
  • A. Charnes, W.W. Cooper, B. Golany, L. Seiford and J. Stutz, Foundations of data envelopment analysis for paretokoopmans efficient empirical production functions. J. Econom. 30 (1985) 91–107. [Google Scholar]
  • K. Kerstens and I. Van de Woestyne, Negative data in DEA: a simple proportional distance function approach. J. Oper. Res. Soc. 62 (2011) 1413–1419. [CrossRef] [Google Scholar]
  • G. Cheng, P. Zervopoulos and Z. Qian, A variant of radial measure capable of dealing with negative inputs and outputs in data envelopment analysis. Eur. J. Oper. Res. 225 (2013) 100–105. [CrossRef] [Google Scholar]
  • J.A.K. Suykens, J. Vandewalle and B. De Moor, Optimal control by least squares support vector machines. Neural Netw. 14 (2001) 23–35. [Google Scholar]
  • K. De Brabanter, J. De Brabanter, J.A.K. Suykens and B. De Moor, Approximate confidence and prediction intervals for least squares support vector regression. IEEE Trans. Neural Netw. 22 (2010) 110–120. [Google Scholar]
  • X. Yang, L. Tan and L. He, A robust least squares support vector machine for regression and classification with noise. Neurocomputing 140 (2014) 41–52. [Google Scholar]
  • S. Chandra, Jayadeva and A. Mehra, Numerical Optimization with Applications. Alpha Science International (2009). [Google Scholar]
  • R. Khemchandani, K. Goyal and S. Chandra, TWSVR: regression via twin support vector machine. Neural Netw. 74 (2016) 14–21. [Google Scholar]
  • J. Mercer, XVI. Functions of positive and negative type, and their connection the theory of integral equations, in Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character. Vol. 209 (1909) 415–446. [Google Scholar]
  • S. Lozano, E. Gutiérrez and P. Moreno, Network DEA approach to airports performance assessment considering undesirable outputs. Appl. Math. Model. 37 (2013) 1665–1676. [Google Scholar]
  • P.F. Wanke, A. Hadi-Vencheh and A. Forghani, A DDF based model for efficiency evaluation in two-stage DEA. Optim. Lett. 12 (2018) 1029–1044. [Google Scholar]
  • K. Petridis, M. Ünsal, P.K. Dey and H.H. Örkcü, A novel network data envelopment analysis model for performance measurement of turkish electric distribution companies. Energy 174 (2019) 985–998. [Google Scholar]
  • L. Shao, X. Yu and C. Feng, Evaluating the eco-efficiency of China’s industrial sectors: a two-stage network data envelopment analysis. J. Environ. Manag. 247 (2019) 551–560. [Google Scholar]
  • M.-A. Thi Nguyen and M.-M. Yu, Decomposing the operational efficiency of major cruise lines: a network data envelopment analysis approach in the presence of shared input and quasi-fixed input. Manag. Dec. Econ. 41 (2020) 1501–1516. [Google Scholar]
  • R. Lin and Q. Liu, Directional distance based efficiency decomposition for series system in network data envelopment analysis. J. Oper. Res. Soc. 73 (2022) 1873–1888. [CrossRef] [Google Scholar]
  • M. Azadi, Z. Moghaddas, R.F. Saen, A. Gunasekaran, S.K. Mangla and A. Ishizaka, Using network data envelopment analysis to assess the sustainability and resilience of healthcare supply chains in response to the COVID-19 pandemic. Ann. Oper. Res. 328 (2023) 107–150. [Google Scholar]
  • R. Kaur and J. Puri, A novel dynamic data envelopment analysis approach with parabolic fuzzy data: case study in the Indian banking sector. RAIRO-Oper. Res. 56 (2022) 2853–2880. [Google Scholar]
  • H.K. Hong, S.H. Ha, C.K. Shin, S.C. Park and S.H. Kim, Evaluating the efficiency of system integration projects using data envelopment analysis (DEA) and machine learning. Exp. Syst. Appl. 16 (1999) 283–296. [Google Scholar]
  • H.-K. Hong and J.-K. Kim, Evaluating efficiency of life insurance companies utilizing DEA and machine learning. J. Intell. Inf. Syst. 7 (2001) 63–79. [Google Scholar]
  • B. Jiang, W. Chen, H. Zhang and W. Pan, Supplier’s efficiency and performance evaluation using DEA-SVM approach. J. Softw. 8 (2013) 25–30. [Google Scholar]
  • H.-Y. Kao, T.-K. Chang and Y.-C. Chang, Classification of hospital web security efficiency using data envelopment analysis and support vector machine. Math. Prob. Eng. 2013 (2013) 1–8. [Google Scholar]
  • X. Yang and S. Dimitrov, Data envelopment analysis may obfuscate corporate financial data: using support vector machine and data envelopment analysis to predict corporate failure for nonmanufacturing firms. INFOR: Inf. Syst. Oper. Res. 55 (2017) 295–311. [Google Scholar]
  • M. Farahmand, M.I. Desa and M. Nilashi, A combined data envelopment analysis and support vector regression for efficiency evaluation of large decision making units. Int. J. Eng. Technol. 6 (2014) 2310–2321. [Google Scholar]
  • M. Farahmand, M.I. Desa, M. Nilashi and A. Wibowo, An improved method for predicting and ranking suppliers efficiency using data envelopment analysis. Jurnal Teknologi 73 (2015) 91–97. [Google Scholar]
  • Q. Zhang and C. Wang, DEA efficiency prediction based on IG–SVM. Neural Comput. Appl. 31 (2019) 8369–8378. [Google Scholar]
  • Y. An and X. Zhai, SVR-DEA model of carbon tax pricing for China’s thermal power industry. Sci. Tot. Environ. 734 (2020) 139438. [Google Scholar]
  • Y. Cheng, J. Peng, X. Gu, X. Zhang, W. Liu, Z. Zhou, Y. Yang and Z. Huang, An intelligent supplier evaluation model based on data-driven support vector regression in global supply chain. Comput. Ind. Eng. 139 (2020) 105834. [Google Scholar]
  • N. Nishtha, J. Puri and G. Setia, Performance prediction of DMUs using integrated DEA-SVR approach with imprecise data: application on Indian banks. Soft Comput. 27 (2023) 5325–5355. [Google Scholar]
  • G. Zhang, W. Guo, X. Xiong and Z. Guan, A hybrid approach combining data envelopment analysis and recurrent neural network for predicting the efficiency of research institutions. Exp. Syst. Appl. 238 (2023) 122150. [Google Scholar]
  • M. Mehdiloo and V.V. Podinovski, Selective strong and weak disposability in efficiency analysis. Eur. J. Oper. Res. 276 (2019) 1154–1169. [Google Scholar]
  • P. Bansal and A. Mehra, Malmquist-Luenberger productivity indexes for dynamic network DEA with undesirable outputs and negative data. RAIRO-Oper. Res. 56 (2022) 649–687. [Google Scholar]
  • D.K. Despotis and Y.G. Smirlis, Data envelopment analysis with imprecise data. Eur. J. Oper. Res. 140 (2002) 24–36. [Google Scholar]
  • N. Aghayi and B. Maleki, Efficiency measurement of DMUs with undesirable outputs under uncertainty based on the directional distance function: application on bank industry. Energy 112 (2016) 376–387. [Google Scholar]
  • S. Goyal, A.N. Sah, R.K. Sharma and J. Puri, Estimating technical efficiencies of Indian IT companies for setting improvement targets for inefficient companies: an empirical analysis with workers effort as key input. Work 66 (2020) 885–900. [Google Scholar]
  • S. Goyal, A.N. Sah and J. Puri, Effectiveness of top-tier information technology software service companies: a multi-component perspective. J. Glob. Inf. Technol. Manag. 24 (2021) 98–119. [Google Scholar]
  • M.D. Shapiro, Cyclical productivity and the workweek of capital. Am. Econ. Rev. 83 (1993) 229–233. [Google Scholar]
  • R. P˜oldaru and J. Roots, A PCA–DEA approach to measure the quality of life in Estonian counties. Soc.-Econ. Plan. Sci. 48 (2014) 65–73. [Google Scholar]
  • A. Amirteimoori, D.K. Despotis and S. Kordrostami, Variables reduction in data envelopment analysis. Optimization 63 (2014) 735–745. [Google Scholar]
  • F. Song, Z. Guo and D. Mei, Feature selection using principal component analysis. in 2010 International Conference on System Science, Engineering Design and Manufacturing Informatization. Vol. 1. IEEE (2010) 27–30. [Google Scholar]
  • Y.-H. Shao, C.-H. Zhang, Z.-M. Yang, L. Jing and N.-Y. Deng, An ε-twin support vector machine for regression. Neural Comput. Appl. 23 (2013) 175–185. [CrossRef] [Google Scholar]
  • V. Cerqueira, L. Torgo and I. Mozetič, Evaluating time series forecasting models: an empirical study on performance estimation methods. Mach. Learn. 109 (2020) 1997–2028. [Google Scholar]
  • Y. Tang, Z. Song, Y. Zhu, H. Yuan, M. Hou, J. Ji, C. Tang and J. Li, A survey on machine learning models for financial time series forecasting. Neurocomputing 512 (2022) 363–380. [Google Scholar]

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