Fifty Years of Data Envelopment Analysis

Mergoni, A. A. Emrouznejad, and K. De Witte (2025) Fifty years of Data Envelopment Analysis, European Journal of Operational Research, 1-25. https://doi.org/10.1016/j.ejor.2024.12.049. 

 

 

List of papers cited in this article:

  • Adler, , & Raveh, A. (2008). Presenting DEA graphically. Omega, 36(5), 715–729. https://doi.org/10.1016/j.omega.2006.02.006
  • Adler, , & Yazhemsky, E. (2010). Improving discrimination in data envelopment analysis: PCA–DEA or variable reduction. European Journal of Operational Research, 202(1), 273–284. https://doi.org/10.1016/j.ejor.2009.03.050
  • Afsharian, M., Ahn, H., & Harms, S. G. (2018). A non-convex meta-frontier malmquist index for measuring productivity over time. IMA Journal of Management, Mathematics, 29(4), 377– https://doi.org/10.1093/imaman/dpx007 (2018).
  • Afsharian, M., & Podinovski, V. V. (2018). A linear programming approach to efficiency evaluation in nonconvex metatechnologies. European Journal of Operational Research, 268(1), 268– https://doi.org/10.1016/j.ejor.2018.01.013
  • Aigner, D., Lovell, C. A. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21– https://doi.org/10.1016/0304-4076(77)90052-5
  • Allen, R., Athanassopoulos, A., Dyson, R. G., & Thanassoulis, E. (1997). Weights restrictions and value judgements in Data Envelopment Analysis: Evolution, development and future directions. Annals of Operations Research, 73, 13– https:// doi.org/10.1023/A:1018968909638
  • Amirteimoori, A., & Emrouznejad, A. (2012). Optimal input/output reduction in production processes. Decision Support Systems, 52(3), 742– https://doi.org/ 10.1016/j.dss.2011.11.020
  • Amin, G. R., Emrouznejad, A., & Gattoufi, S. (2017a). Modelling generalized firms’ restructuring using inverse DEA. Journal of Productivity Analysis, 48(1), 51– https://doi.org/10.1007/s11123-017-0501-y
  • Amin, G. R., Emrouznejad, A., & Gattoufi, S. (2017b). Minor and major consolidations in inverse DEA: definition and determination. Computers and Industrial Engineering, 103, 193– https://doi.org/10.1016/j.cie.2016.11.029
  • Amin, G. R., & Ibn Boamah, M. (2021). A two-stage inverse data envelopment analysis approach for estimating potential merger gains in the US banking sector. Managerial and decision economics, 42(6), 1454– https://doi.org/10.1002/mde.3319
  • Amin, G. R., & Ibn-Boamah, M. (2023). Modeling business partnerships: a data envelopment analysis approach. European Journal of Operational Research, 305(1), 329– https://doi.org/10.1016/j.ejor.2022.05.036
  • Amin, G. R., & Oukil, A. (2019). Flexible target setting in mergers using inverse data envelopment analysis. International Journal of Operational Research, 35(3), 301– https://doi.org/10.1504/IJOR.2019.101196
  • An, Q., Wang, P., Emrouznejad, A., & Hu, J. (2020). Fixed cost allocation based on the principle of efficiency invariance in two-stage systems. European Journal of Operational Research, 283(2), 662– https://doi.org/10.1016/j.ejor.2019.11.031
  • Anderson, R. I., Fok, R., Springer, T., & Webb, J. (2002). Technical efficiency and economies of scale: A non-parametric analysis of REIT operating efficiency. European Journal of Operational Research, 139(3), 598– https://doi.org/10.1016/S0377-2217(01)00183-7
  • Andersen, P., & Petersen, C. (1993). A Procedure for Ranking Efficient Units in Data Envelopment Analysis. Management Science, 39(10), 1261–1264. https://doi.org/ 10.1287/mnsc.39.10.1261
  • Ang, S., & Chen, C. M. (2016). Pitfalls of decomposition weights in the additive multi-stage DEA model. Omega, 58, 139– https://doi.org/10.1016/j. omega.2015.05.008
  • Angulo-Meza, L., & Lins, M. P. E. (2002). Review of Methods for Increasing Discrimination in Data Envelopment Analysis. Annals of Operations Research, 116(1/ 4), 225– https://doi.org/10.1023/A:1021340616758
  • Aparicio, J., Cordero, J. M., Gonzalez, M., & Lopez-Espin, J. J. (2018). Using non-radial DEA to assess school efficiency in a cross-country perspective: An empirical analysis of OECD countries. Omega, 79, 9– https://doi.org/10.1016/j.omega.2017.07.004
  • Aparicio, J., Crespo-Cebada, E., Pedraja-Chaparro, F., & Santín, D. (2017). Comparing school ownership performance using a pseudo-panel database: A Malmquist-type index approach. European Journal of Operational Research, 256(2), 533– https:// doi.org/10.1016/j.ejor.2016.06.030
  • Aparicio, J., Esteve, M., Rodriguez-Sala, J. J., & Zofio, J. L. (2021). The estimation of productive efficiency through machine learning techniques: efficiency analysis trees. Data-enabled analytics: DEA for big data (pp. 51–92). Cham: Springer International Publishin https://doi.org/10.1007/978-3-030-75162-3_3
  • Aparicio, J., & Esteve, M. (2023). How to peel a data envelopment analysis frontier: A cross-validation-based approach. Journal of the Operational Research Society, 74(12), 2558– https://doi.org/10.1080/01605682.2022.2157765
  • Aparicio, J., Kapelko, M., & Ortiz, L. (2023). Enhancing the measurement of firm inefficiency accounting for corporate social responsibility: A dynamic data envelopment analysis fuzzy approach. European Journal of Operational Research, 306 (2), 986– https://doi.org/10.1016/j.ejor.2022.09.003
  • Aparicio, J., Pastor, J. T., & Zofio, J. L. (2013). On the inconsistency of the Malmquist–Luenberger index. European Journal of Operational Research, 229(3), 738– https://doi.org/10.1016/j.ejor.2013.03.031
  • Aparicio, J., & Santín, D. (2024). Global and local technical changes: A new decomposition of the Malmquist productivity index using virtual units. Economic Modelling, 134, Article https://doi.org/10.1016/j.econmod.2024.106674
  • Arabi, , Munisamy, S., & Emrouznejad, A. (2015). A new slacks-based measure of Malmquist–Luenberger index in the presence of undesirable outputs. Omega, 51, 29–37. https://doi.org/10.1016/j.omega.2014.08.006
  • Arocena, P., Saal, D. S., Urakami, T., & Zschille, M. (2020). Measuring and decomposing productivity change in the presence of mergers. European Journal of Operational Research, 282(1), 319– https://doi.org/10.1016/j.ejor.2019.08.048
  • Assani, S., Mansoor, M. S., Asghar, F., Li, Y., & Yang, F. (2022). Efficiency, RTS, and marginal returns from salary on the performance of the NBA players: A parallel DEAnetwork with shared inputs. Journal of Industrial and Management Optimization, 18 (3), https://doi.org/10.3934/jimo.2021053
  • Atici, K. , & Podinovski, V. V. (2015). Using data envelopment analysis for the assessment of technical efficiency of units with different specialisations: An application to agriculture. Omega, 54, 72–83. https://doi.org/10.1016/j. omega.2015.01.015
  • Atwood, J., & Shaik, S. (2020). Theory and statistical properties of Quantile Data Envelopment Analysis. European Journal of Operational Research, 286(2), 649– https://doi.org/10.1016/j.ejor.2020.03.077
  • Avkiran, K. (2015). An illustration of dynamic network DEA in commercial banking including robustness tests. Omega, 55, 141–150. https://doi.org/10.1016/j. omega.2014.07.002
  • Azadeh, A., Moghaddam, M., Asadzadeh, S. M., & Negahban, A. (2011). An integrated fuzzy simulation-fuzzy data envelopment analysis algorithm for job-shop layout optimization: The case of injection process with ambiguous data. European Journal of Operational Research, 214(3), 768– https://doi.org/10.1016/j.ejor.2011.05.015
  • Azadi, M., & Saen, R. F. (2014). Developing a New Theory of Integer-Valued Data Envelopment Analysis for Supplier Selection in the Presence of Stochastic Data. International Journal of Information Systems and Supply Chain Management, 7(3), 80– https://doi.org/10.4018/ijisscm.2014070104
  • Badunenko, O., D’Inverno, G., & De Witte, K. (2023). On distinguishing the direct causal effect of an intervention from its efficiency-enhancing effects. European Journal of Operational Research, 310(1), 432– https://doi.org/10.1016/j.ejor.2023.03.030
  • Bahari, A. R., & Emrouznejad, A. (2014). Influential DMUs and outlier detection in data envelopment analysis with an application to health care. Annals of Operations Research, 223(1), 95– https://doi.org/10.1007/s10479-014-1604-8
  • Charnes, A., Cooper, W. W., & Rhodes, E. (1981). Evaluating Program and Managerial Efficiency: An Application of Data Envelopment Analysis to Program Follow Through. Management Science, 27(6), 668– https://doi.org/10.1287/ mnsc.27.6.668
  • Chao, C. M., Yu, M. M., Hsiung, S., & Chen, L. S. (2018). Profitability efficiency, marketability efficiency and technology gaps in Taiwan’s banking industry: Meta-frontier network data envelopment analysis. Applied Economics, 50(3), 233–250. https://doi.org/10.1080/00036846.2017.1316827 (2018).
  • Chen, C. C. (2017). Measuring departmental and overall regional performance: applying the multi-activity DEA model to Taiwan׳s cities/counties. Omega, 67, 60– https:// doi.org/10.1016/j.omega.2016.04.002
  • Chen, C. M., Du, J., Huo, J., & Zhu, J. (2012). Undesirable factors in integer-valued DEA: Evaluating the operational efficiencies of city bus systems considering safety records. Decision Support Systems, 54(1), 330– https://doi.org/10.1016/j. dss.2012.05.040
  • Chen, L., Huang, Y., Li, M. J., & Wang, Y. M. (2020). Meta-frontier analysis using cross-efficiency method for performance evaluation. European Journal of Operational Research, 280(1), 219– https://doi.org/10.1016/j.ejor.2019.06.053
  • Chen, C. T., Lin, C. T., & Huang, S. F. (2006). A fuzzy approach for supplier evaluation and selection in supply chain management. International Journal of Production Economics, 102(2), 289– https://doi.org/10.1016/j.ijpe.2005.03.009
  • Chen, K., Ren, X., & Yang, G. (2021). A novel approach for assessing academic journals: Application of integer DEA model for management science and operations research field. Journal of Informetrics, 15(3), Article https://doi.org/10.1016/j. joi.2021.101176
  • Chen, W., Wang, Y., & Yang, S. (2009). Efficient influence maximization in social networks. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 199–208). https://doi.org/1145/ 1557019.1557047
  • Chen, Y., Cook, W. D., Du, J., Hu, H., & Zhu, J. (2017). Bounded and discrete data and Likert scales in data envelopment analysis: application to regional energy efficiency in China. Annals of Operations Research, 255(1–2), 347– https://doi.org/ 10.1007/s10479-015-1827-3
  • Chen, Y., Cook, W. D., Kao, C., & Zhu, J. (2014). Network DEA Pitfalls: Divisional Efficiency and Frontier Projection (pp. 31–54). https://doi.org/1007/978-1-4899-8068-7_2
  • Chen, Y., Li, Y., Liang, L., & Wu, H. (2019). An extension on super slacks-based measure DEA approach. Annals of Operations Research, 278(1–2), 101– https://doi.org/ 10.1007/s10479-017-2495-2
  • Chen, Y., Tsionas, M. G., & Zelenyuk, V. (2021). LASSO+DEA for small and big wide data. Omega, 102, Article https://doi.org/10.1016/j.omega.2021.102419
  • Cherchye, L., Moesen, W., Rogge, , & Puyenbroeck, T. Van (2007). An Introduction to ‘Benefit of the Doubt’ Composite Indicators. Social Indicators Research, 82(1), 111– https://doi.org/10.1007/s11205-006-9029-7
  • Chowdhury, H., & Zelenyuk, V. (2016). Performance of hospital services in Ontario: DEA with truncated regression approach. Omega, 63, 111– https://doi.org/10.1016/ j.omega.2015.10.007
  • Chung, Y. H., Fare, R., & Grosskopf, S. (1997). Productivity and Undesirable Outputs: A Directional Distance Function Approach. Journal of Environmental Management, 51 (3), 229– https://doi.org/10.1006/jema.1997.0146
  • Coelli, T. J., Rao, P. D. S., O’Donnell, C. J., & Battese, G. E. (2005). An Introduction to Efficiency and Productivity Analysis. Springer-Verla https://doi.org/10.1007/ b136381
  • Coelli, T., & Perelman, S. (1999). A comparison of parametric and non-parametric distance functions: With application to European railways. European Journal of Operational Research, 117(2), 326– https://doi.org/10.1016/S0377-2217(98) 00271-9
  • Contreras, I., & Lozano, S. (2022). Size efficiency, splits and merger gains, and centralized resource reallocation of Spanish public universities. Socio-Economic Planning Sciences, 81, Article https://doi.org/10.1016/j.seps.2021.101190
  • Cook, W. D., Kress, M., & Seiford, L. M. (1993). On the Use of Ordinal Data in Data Envelopment Analysis. Journal of the Operational Research Society, 44(2), 133– https://doi.org/10.1057/jors.1993.25
  • Cook, W. D., Kress, M., & Seiford, L. M. (1996). Data Envelopment Analysis in the Presence of Both Quantitative and Qualitative Factors. Journal of the Operational Research Society, 47(7), 945– https://doi.org/10.1057/jors.1996.120
  • Cook, W. D., Liang, L., & Zhu, J. (2010). Measuring performance of two-stage network structures by DEA: A review and future perspective. Omega, 38(6), 423– https:// doi.org/10.1016/j.omega.2009.12.001
  • Cook, W. D., Tone, K., & Zhu, J. (2014). Data envelopment analysis: Prior to choosing a model. Omega, 44, 1– https://doi.org/10.1016/j.omega.2013.09.004
  • Cook, W. D., & Zhu, J. (2006). Rank order data in DEA: A general framework. European Journal of Operational Research, 174(2), 1021– https://doi.org/10.1016/j. ejor.2005.01.063
  • Cook, W. D., & Zhu, J. (2008). CAR-DEA: Context-Dependent Assurance Regions in DEA. Operations Research, 56(1), 69– https://doi.org/10.1287/opre.1070.0500
  • Cook, W., & Zhu, J. (2015). Data Envelopment Analysis: A Handbook of Models and Methods (J. Zhu, Ed.; Vol. 221). Springer US. https://doi.org/1007/978-1-48 99-7553-9.
  • Cooper, W. W., Deng, H., Huang, Z., & Li, S. X. (2004). Chance constrained programming approaches to congestion in stochastic data envelopment analysis. European Journal of Operational Research, 155, 487– https://doi.org/10.1016/S0377-2217(02) 00901-3
  • Cooper, W. W., Huang, Z., Lelas, V., Li, S. X., & Olesen, O. (1998). Chance constrained programming formulations for stochastic characterizations of efficiency and dominance in DEA. Journal of Productivity Analysis, 9, 53–79. https://doi.org/ 10.1023/a:1018320430249
  • Cooper, W. W., Huang, Z., & Li, S. X. (1996). Satisficing DEA models under chance constraints. Annals of Operations research, 66, 279– https://doi.org/10.1007/ BF02187302
  • Cooper, W. W., Rark, K. S., & Yu, G. (2001). IDEA (Imprecise Data Envelopment Analysis) with CMDs (Column Maximum Decision Making Units). Journal of the Operational Research Society, 52(2), 176– https://doi.org/10.1057/palgrave.jors.2601070
  • Cooper, W. W., Pastor, J. T., Borras, F., Aparicio, J., & Pastor, D. (2011). BAM: a bounded adjusted measure of efficiency for use with bounded additive models. Journal of Productivity Analysis, 35(2), 85– https://doi.org/10.1007/s11123-010-0190-2
  • Cooper, W. W., Seiford, L. M., & Zhu, J. (2004). Handbook on Data Envelopment Analysis (W. W. Cooper, L. M. Seiford, & J. Zhu, Eds.; Vol. 71). Springer US. htt ps://doi.org/1007/b105307.
  • Cordero, J. M., Santín, D., & Sicilia, G. (2015). Testing the accuracy of DEA estimates under endogeneity through a Monte Carlo simulation. European Journal of Operational Research, 244(2), 511– https://doi.org/10.1016/j.ejor.2015.01.015
  • Da Silva e Souza, G., & Gomes, E. G. (2015). Management of agricultural research centers in Brazil: A DEA application using a dynamic GMM approach. European Journal of Operational Research, 240(3), 819– https://doi.org/10.1016/j.ejor.2014.07.027
  • Dai, Q., Li, Y., Lei, X., & Wu, D. (2021). A DEA-based incentive approach for allocating common revenues or fixed costs. European Journal of Operational Research, 292(2), 675– https://doi.org/10.1016/j.ejor.2020.11.006
  • Daraio, C., & Simar, L. (2005). Introducing Environmental Variables in Nonparametric Frontier Models: a Probabilistic Approach. Journal of Productivity Analysis, 24(1), 93– https://doi.org/10.1007/s11123-005-3042-8
  • Daraio, C., & Simar, L. (2007a). Advanced Robust and Nonparametric Methods in Efficiency Analysis, 4. US: Springer. https://doi.org/1007/978-0-387-35231-2
  • Daraio, C., & Simar, L. (2007b). Conditional nonparametric frontier models for convex and nonconvex technologies: a unifying approach. Journal of Productivity Analysis, 28 (1–2), 13– https://doi.org/10.1007/s11123-007-0049-3
  • Daraio, C., Simar, L., & Wilson, P. W. (2021). Quality as a latent heterogeneity factor in the efficiency of universities. Economic Modelling, 99, Article https://doi. org/10.1016/j.econmod.2021.03.004
  • De Gooyert, V., Rouwette, E., Van Kranenburg, H., & Freeman, E. (2017). Reviewing the role of stakeholders in operational research: A stakeholder theory perspective. European Journal of Operational Research, 262(2), 402– https://doi.org/ 10.1016/j.ejor.2017.03.079
  • De Witte, K., & Chenier, M.A. (2023). Learning Analytics in Education for the Twenty-First Century. . In E. Bertoni, M. Fontana, L. Gabrielli, S. Signorelli, & M. Vespe (Eds.), Handbook of Computational Social Science for Policy. (pp. 305–326).
  • De Witte, K., & Kortelainen, M. (2013). What explains the performance of students in a heterogeneous environment? Conditional efficiency estimation with continuous and discrete environmental variables. Applied Economics, 45(17), 2401– https:// doi.org/10.1080/00036846.2012.665602
  • De Witte, K., & Lopez-Torres, L. (2017). Efficiency in education: a review of literature and a way forward. Journal of the Operational Research Society, 68(4), 339– https://doi.org/10.1057/jors.2015.92
  • De Witte, K., & Marques, R. C. (2010). Influential observations in frontier models, a robust non-oriented approach to the water sector. Annals of Operations Research, 181 (1), 377– https://doi.org/10.1007/s10479-010-0754-6
  • De Witte, K., & Schiltz, F. (2018). Measuring and explaining organizational effectiveness of school districts: Evidence from a robust and conditional Benefit-of-the-Doubt approach. European Journal of Operational Research, 267(3), 1172– https://doi. org/10.1016/j.ejor.2017.12.034
  • Dia, M., Takouda, P. M., & Golmohammadi, A. (2022). Assessing the performance of Canadian credit unions using a three-stage network bootstrap DEA. Annals of Operations Research, 311(2), 641– https://doi.org/10.1007/s10479-020-03612-w
  • Dipierro, A. R., & De Witte, K. (2024). The underlying signals of efficiency in European universities: a combined efficiency and machine learning approach. Studies in Higher Education, 1– https://doi.org/10.1080/03075079.2024.2370948
  • D’Inverno, G., & De Witte, K. (2020). Service level provision in municipalities: A flexible directional distance composite indicator. European Journal of Operational Research, 286(3), 1129– https://doi.org/10.1016/j.ejor.2020.04.012
  • D’Inverno, G., Smet, M., & De Witte, K. (2021). Impact evaluation in a multi-input multi-output setting: Evidence on the effect of additional resources for schools. European Journal of Operational Research, 290(3), 1111– https://doi.org/10.1016/j. ejor.2020.08.042
  • Dogan, H. (2023). Measuring the Efficiency of Turkish Research Universities via Two-Stage Network DEA with Shared Inputs Model. Journal on Efficiency and Responsibility in Education and Science, 16(4), 329– https://doi.org/10.7160/ eriesj.2023.160406
  • Doumpos, M., & Cohen, S. (2014). Applying data envelopment analysis on accounting data to assess and optimize the efficiency of Greek local governments. Omega, 46, 74– https://doi.org/10.1016/j.omega.2014.02.004
  • Doyle, J., & Green, R. (1994). Efficiency and cross-efficiency in DEA: Derivations, meanings and uses. Journal of the Operational Research Society, 45(5), 567– https://doi.org/10.1057/jors.1994.84
  • Du, J., Chen, Y., & Huang, Y. (2018). A Modified Malmquist-Luenberger Productivity Index: Assessing Environmental Productivity Performance in China. European Journal of Operational Research, 269(1), 171– https://doi.org/10.1016/j. ejor.2017.01.006
  • Du, J., Liang, L., Chen, Y., Cook, W. D., & Zhu, J. (2011). A bargaining game model for measuring performance of two-stage network structures. European Journal of Operational Research, 210(2), 390– https://doi.org/10.1016/j.ejor.2010.08.025
  • Dyson, R. G., Allen, R., Camanho, A. S., Podinovski, V. V., Sarrico, C. S., & Shale, E. A. (2001). Pitfalls and protocols in DEA. European Journal of Operational Research, 132 (2), 245– https://doi.org/10.1016/S0377-2217(00)00149-1
  • Dyson, R. G., & Thanassoulis, E. (1988). Reducing weight flexibility in data envelopment analysis. Journal of the operational research society, 39(6), 563– https://doi.org/ 10.1057/jors.1988.96
  • Ebrahimi, , Dhamotharan, L., Ghasemi, M. R., & Charles, V. (2022). A cross-inefficiency approach based on the deviation variables framework. Omega, 111, Article 102668. https://doi.org/10.1016/j.omega.2022.102668
  • Edirisinghe, C. P., & Zhang, X. (2010). Input/output selection in DEA under expert information, with application to financial markets. European Journal of Operational Research, 207(3), 1669–1678. https://doi.org/10.1016/j.ejor.2010.06.027
  • El-Mahgary, S., & Lahdelma, R. (1995). Data envelopment analysis: Visualizing the results. European Journal of Operational Research, 83(3), 700– https://doi.org/ 10.1016/0377-2217(94)00303-T
  • Emrouznejad, A. (2005). Measurement efficiency and productivity in SAS/OR. Computers & Operations Research, 32(7), 1665– https://doi.org/10.1016/j. cor.2003.11.025
  • Emrouznejad, A., & Amin, G. R. (2009). DEA models for ratio data: Convexity consideration. Applied Mathematical Modelling, 33(1), 486– https://doi.org/ 10.1016/j.apm.2007.11.018
  • Emrouznejad, A., Amin, G. R., Thanassoulis, E., & Anouze, A. L. (2010). On the boundedness of the SORM DEA models with negative data. European Journal of Operational Research, 206(1), 265– https://doi.org/10.1016/j.ejor.2010.01.032
  • Emrouznejad, A., Anouze, A. L., & Thanassoulis, E. (2010). A semi-oriented radial measure for measuring the efficiency of decision making units with negative data, using DEA. European Journal of Operational Research, 200(1), 297– https://doi. org/10.1016/j.ejor.2009.01.001
  • Emrouznejad, A., & De Witte, K. (2010). COOPER-framework: A unified process for non-parametric projects. European Journal of Operational Research, 207(3), 1573– https://doi.org/10.1016/j.ejor.2010.07.025
  • Emrouznejad, A., Marra, M., Yang, G. L., & Michali, M. (2023). Eco-efficiency considering NetZero and Data Envelopment Analysis: A critical literature review. IMA Journal of Management Mathematics, 34, 599–
  • Emrouznejad, A., Petridis, K., & Charles, V. (2023). Data Envelopment Analysis with GAMS, 338. Springer International Publishin https://doi.org/10.1007/978-3-031-30701-0
  • Emrouznejad, A., Rostamy-Malkhalifeh, M., Hatami-Marbini, A., & Tavana, M. (2012). General and multiplicative non-parametric corporate performance models with interval ratio data. Applied Mathematical Modelling, 36(11), 5506– https://doi. org/10.1016/j.apm.2011.12.040
  • Emrouznejad, A., & Shale, E. (2009). A combined neural network and DEA for measuring efficiency of large scale datasets. Computers & Industrial Engineering, 56(1), 249– https://doi.org/10.1093/imaman/dpad002
  • Emrouznejad, A., Tavana, M., & Hatami-Marbini, A. (2014). The State of the Art in Fuzzy Data Envelopment Analysis, in Performance Measurement with Fuzzy Data Envelopment Analysis published in Studies in Fuzziness and Soft Computing, 309 p. 48). Springer-Verla
  • Emrouznejad, A., & Thanassoulis, E. (2005). A mathematical model for dynamic efficiency using data envelopment analysis. Applied Mathematics and Computation, 160(2), 363– https://doi.org/10.1016/j.amc.2003.09.026
  • Emrouznejad, A., & Thanassoulis, E. (2010). Measurement of productivity index with dynamic DEA. International Journal of Operational Research, 8(2), 247– https:// doi.org/10.1504/IJOR.2010.03314
  • Emrouznejad, A., & Yang, G. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978– Socio-Economic Planning Sciences, 61, 4–8. https://doi.org/10.1016/j.seps.2017.01.008
  • Emrouznejad, A., Yang, G. L., & Amin, G. R. (2019). A novel inverse DEA model with application to allocate the CO2 emissions quota to different regions in Chinese manufacturing industries. Journal of the Operational Research Society, 70(7),
  • 1079– https://doi.org/10.1080/01605682.2018.1489344
  • Eskelinen, J. (2017). Comparison of variable selection techniques for data envelopment analysis in a retail bank. European Journal of Operational Research, 259(2), 778– https://doi.org/10.1016/j.ejor.2016.11.009
  • Essid, H., Ouellette, P., & Vigeant, S. (2014). Productivity, efficiency, and technical change of Tunisian schools: a bootstrapped Malmquist approach with quasi-fixed inputs. Omega, 42(1), 88– https://doi.org/10.1016/j.omega.2013.04.001
  • Estellita Lins, M. P., Moreira da Silva, A. C., & Lovell, C. A. K. (2007). Avoiding infeasibility in DEA models with weight restrictions. European Journal of Operational Research, 181(2), 956– https://doi.org/10.1016/j.ejor.2006.05.041
  • Esteve, M., Aparicio, J., Rabasa, A., & Rodriguez-Sala, J. J. (2020). Efficiency analysis trees: A new methodology for estimating production frontiers through decision trees. Expert Systems with Applications, 162, Article https://doi.org/10.1016/j. eswa.2020.113783
  • Esteve, M., Aparicio, J., Rodriguez-Sala, J. J., & Zhu, J. (2023). Random Forests and the measurement of super-efficiency in the context of Free Disposal Hull. European Journal of Operational Research, 304(2), 729– https://doi.org/10.1016/j. ejor.2022.04.024
  • Fare, R., Grosskopf, S., & Norris, M. (1997). Productivity growth, technical progress, and efficiency change in industrialized countries: reply. The American Economic Review, 87(5), 1040– https://www.jstor.org/stable/2117971.
  • Fare, R., Grosskopf, S., Lindgren, , & Roos, P. (1992). Productivity changes in Swedish pharamacies 1980?1989: A non-parametric Malmquist approach. Journal of Productivity Analysis, 3(1–2), 85–101. https://doi.org/10.1007/BF00158770
  • Fare, R., Grosskopf, S., Lovell, C. A. K., & Pasurka, C. (1989). Multilateral Productivity Comparisons When Some Outputs are Undesirable: A Nonparametric Approach. The Review of Economics and Statistics, 71(1), https://doi.org/10.2307/1928055
  • Fare, R., Karagiannis, G., Hasannasab, M., & Margaritis, D. (2019). A benefit-of-the-doubt model with reverse indicators. European Journal of Operational Research, 278(2), 394– https://doi.org/10.1016/j.ejor.2019.02.009
  • Fare, R., Mizobuchi, H., & Zelenyuk, V. (2021). Hicks neutrality and homotheticity in technologies with multiple inputs and multiple outputs. Omega, 101, Article https://doi.org/10.1016/j.omega.2020.102240
  • Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Societ Series A (General), 120(3), 253. https://doi.org/10.2307/2343100
  • Fethi, M. D., & Pasiouras, F. (2010). Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey. European journal of operational research, 204(2), 189– https://doi.org/10.1016/j. ejor.2009.08.003
  • Fried, H. O., Lovel, K. C. A., & Schmidt, S. S. (2008). The Measurement of Productive Efficiency and Productivity Change (H. O. Fried, C. A. K. Lovell, & S. S. Schmidt, Eds.). Oxford University Press. https://doi.org/1093/acprof:oso/978019518 3528.001.0001.Fukuyama, H., & Weber, W. L. (2010). A slacks-based inefficiency measure for a two-stage system with bad outputs. Omega, 38(5), 398–409. https://doi.org/10.1016/j. omega.2009.10.006
  • Fusco, E. (2015). Enhancing non-compensatory composite indicators: A directional proposal. European Journal of Operational Research, 242(2), 620– https://doi. org/10.1016/j.ejor.2014.10.017
  • Fusco, E., Vidoli, F., & Rogge, (2020). Spatial directional robust Benefit of the Doubt approach in presence of undesirable output: An application to Italian waste sector. Omega, 94, Article 102053. https://doi.org/10.1016/j.omega.2019.03.011
  • Gattoufi, S., Amin, G. R., & Emrouznejad, A. (2014). A new inverse DEA method for merging banks. IMA Journal of Management Mathematics, 25(1), 73– https://doi. org/10.1093/imaman/dps027
  • Gearhart, R. S., & Michieka, M. (2018). A comparison of the robust conditional order-m estimation and two stage DEA in measuring healthcare efficiency among California counties. Economic Modelling, 73, 395–406. https://doi.org/10.1016/j. econmod.2018.04.015
  • Gerami, J., Mozaffari, M. R., Wanke, P. F., & Correa, H. (2022). A novel slacks-based model for efficiency and super-efficiency in DEA-R. Operational Research, 22(4), 3373– https://doi.org/10.1007/s12351-021-00679-6
  • Ghasemi, M. R., Ignatius, J., & Emrouznejad, A. (2014). A bi-objective weighted model for improving the discrimination power in MCDEA. European Journal of Operational Research, 233(3), 640– https://doi.org/10.1016/j.ejor.2013.08.041
  • Gobbo, S. C.de O., Mariano, E. , & Gobbo, J. A., Jr. (2021). Combining social network and data envelopment analysis: A proposal for a Selection Employment Contracts Effectiveness index in healthcare network applications. Omega, 103, Article 102377. https://doi.org/10.1016/j.omega.2020.102377
  • Golany, , & Roll, Y. (1989). An application procedure for DEA. Omega, 17(3), 237–250. https://doi.org/10.1016/0305-0483(89)90029-7
  • Greene, W. H. (2008). The Econometric Approach to Efficiency Analysis. The Measurement of Productive Efficiency and Productivity Change (pp. 92–250). Oxford University Press. https://doi.org/1093/acprof:oso/9780195183528.003.0002
  • Grifell-Tatje, E., & Lovell, C. A. K. (1999). Profits and Productivity. Management Science, 45(9), 1177– https://doi.org/10.1287/mnsc.45.9.1177
  • Guerrero, M., Moragues, R., Aparicio, J., & Valero-Carreras, D. (2024). Support Vector Frontiers with kernel splines. Omega. , Article 103130. https://doi.org/10.1016/j. omega.2024.103130
  • Guillen, M. D., Aparicio, J., & Esteve, M. (2023). Gradient tree boosting and the estimation of production frontiers. Expert Systems with Applications, 214, Article https://doi.org/10.1016/j.eswa.2022.119134
  • Guo, I. L., Lee, H. S., & Lee, D. (2017). An integrated model for slack-based measure of super-efficiency in additive DEA. Omega, 67, 160– https://doi.org/10.1016/j. omega.2016.05.002
  • Guo, C., & Zhu, J. (2017). Non-cooperative two-stage network DEA model: Linear vs. parametric linear. European Journal of Operational Research, 258(1), 398– https://doi.org/10.1016/j.ejor.2016.11.039
  • Halkos, G., & Petrou, K. (2019). Treating undesirable outputs in DEA: A critical review. Economic Analysis and Policy, 62, 97–104. https://doi.org/10.1016/j. eap.2019.01.005
  • Hatami-Marbini, A., Emrouznejad, A., & Agrell, P. J. (2014). Interval data without sign restrictions in DEA. Applied Mathematical Modelling, 38, 2028– https://doi.org/ 10.1016/j.apm.2013.10.027
  • Hatami-Marbini, A., & Arabmaldar, A. (2021). Robustness of Farrell cost efficiency measurement under data perturbations: Evidence from a US manufacturing application. European Journal of Operational Research, 295(2), 604– https://doi. org/10.1016/j.ejor.2021.03.019
  • Hatami-Marbini, A., Emrouznejad, A., & Tavana, M. (2011). A taxonomy and review of the fuzzy data envelopment analysis literature: Two decades in the makin European Journal of Operational Research, 214(3), 457–472. https://doi.org/10.1016/j. ejor.2011.02.001
  • Hayami, Y, & Ruttan, V. (1970). Agricultural productivity differences among countries. American Economic Review, 60(5), 895– https://doi.org/10.1093/acprof:osobl/ 9780199754359.003.0004 (1970).
  • Henriques, A. A., Fontes, M., Camanho, A. S., D’Inverno, G., Amorim, P., & Silva, J. G. (2022). Performance evaluation of problematic samples: a robust nonparametric approach for wastewater treatment plants. Annals of Operations Research, 315(1), 193– https://doi.org/10.1007/s10479-022-04629-z
  • Herrera-Restrepo, O., Triantis, K., Trainor, J., Murray-Tuite, P., & Edara, P. (2016). A multi-perspective dynamic network performance efficiency measurement of an
  • evacuation: A dynamic network-DEA approach. Omega, 60, 45– https://doi.org/ 10.1016/j.omega.2015.04.019
  • Horsky, D., & Nelson, P. (2006). Testing the Statistical Significance of Linear Programming Estimators. Management Science, 52(1), 128– https://doi.org/ 10.1287/mnsc.1050.0444
  • Huang, C. J., Huang, T. H., & Liu, H. (2014). A new approach to estimating the metafrontier production function based on a stochastic frontier framework. Journal of productivity Analysis, 42, 241–254. https://www.jstor.org/stable/43550036.
  • Huang, C. W., Ting, C. T., Lin, C. H., & Lin, C. T. (2013). Measuring non-convex metafrontier efficiency in international tourist hotels. Journal of the Operational Research Society, 64(2), 250– https://doi.org/10.1057/jors.2012.52 (2013).
  • Ignatius, J., Ghasemi, M. R., Zhang, F., Emrouznejad, A., & Hatami-Marbini, A. (2016). Carbon efficiency evaluation: An analytical framework using fuzzy DEA. European Journal of Operational Research, 253(2), 428– https://doi.org/10.1016/j. ejor.2016.02.014
  • Imanirad, R., Cook, W. D., Aviles-Sacoto, S. V., & Zhu, J. (2015). Partial input to output impacts in DEA: The case of DMU-specific impacts. European Journal of Operational Research, 244(3), 837– https://doi.org/10.1016/j.ejor.2015.02.002
  • Jahanshahloo, G. R., Soleimani-Damaneh, M., & Ghobadi, S. (2015). Inverse DEA under inter-temporal dependence using multiple-objective programmin European Journal of Operational Research, 240(2), 447–456. https://doi.org/10.1016/j. ejor.2014.07.002
  • Jeong, S. O., Park, U., & Simar, L. (2010). Nonparametric conditional efficiency measures: asymptotic properties. Annals of Operations Research, 173(1), 105–122. https://doi.org/10.1007/s10479-008-0359-5
  • Johnson, A. L., & McGinnis, L. F. (2008). Outlier detection in two-stage semiparametric DEA models. European Journal of Operational Research, 187(2), 629– https://doi. org/10.1016/j.ejor.2007.03.041
  • Johnson, A. L., & McGinnis, L. F. (2009). The hyperbolic-oriented efficiency measure as a remedy to infeasibility of super efficiency models. Journal of the Operational Research Society, 60(11), 1511– https://doi.org/10.1057/jors.2009.71
  • Jradi, S., & Ruggiero, J. (2019). Stochastic data envelopment analysis: A quantile regression approach to estimate the production frontier. European Journal of Operational Research, 278(2), 385– https://doi.org/10.1016/j.ejor.2018.11.017
  • Kaffash, S., Kazemi Matin, R., & Tajik, M. (2018). A directional semi-oriented radial DEA measure: an application on financial stability and the efficiency of banks. Annals of Operations Research, 264(1–2), 213– https://doi.org/10.1007/s10479-017-2719-5
  • Kang, H. J., Kim, C., & Choi, K. (2024). Combining bootstrap data envelopment analysis with social networks for rank discrimination and suitable potential benchmarks. European Journal of Operational Research, 312(1), 283– https://doi.org/ 10.1016/j.ejor.2023.06.038
  • Kao, C. (2009). Efficiency decomposition in network data envelopment analysis: A relational model. European Journal of Operational Research, 192(3), 949– https:// doi.org/10.1016/j.ejor.2007.10.008
  • Kao, C. (2010). Malmquist productivity index based on common-weights DEA: The case of Taiwan forests after reorganization. Omega, 38(6), 484– https://doi.org/ 10.1016/j.omega.2009.12.005
  • Kao, C. (2017). Measurement and decomposition of the Malmquist productivity index for parallel production systems. Omega, 67, 54– https://doi.org/10.1016/j. omega.2016.04.001
  • Kao, C., & Hwang, S. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185(1), 418–429. https://doi.org/10.1016/j. ejor.2006.11.041
  • Kao, C., & Hwang, S. (2014). Multi-period efficiency and Malmquist productivity index in two-stage production systems. European Journal of Operational Research, 232 (3), 512–521. https://doi.org/10.1016/j.ejor.2013.07.030
  • Kao, C., & Hwang, S. (2021). Measuring the effects of undesirable outputs on the efficiency of production units. European Journal of Operational Research, 292(3), 996–1003. https://doi.org/10.1016/j.ejor.2020.11.026
  • Kao, C., & Hwang, S. (2023). Separating the effect of undesirable outputs generation from the inefficiency of desirable outputs production in efficiency measurement. European Journal of Operational Research, 311(3), 1097–1102. https://doi.org/ 10.1016/j.ejor.2023.06.012
  • Kao, C., & Lin, P. H. (2011). Qualitative factors in data envelopment analysis: A fuzzy number approach. European Journal of Operational Research, 211(3), 586– https://doi.org/10.1016/j.ejor.2010.12.004
  • Kao, C., & Liu, S. T. (2019). Cross efficiency measurement and decomposition in two basic network systems. Omega, 83, 70– https://doi.org/10.1016/j. omega.2018.02.004
  • Karimi, , Azadi, M., Farzipoor Saen, R., & Fosso Wamba, S. (2022). Theory of binary-valued data envelopment analysis: an application in assessing the sustainability of suppliers. Industrial Management & Data Systems, 122(3), 682–701. https://doi.org/ 10.1108/IMDS-09-2021-0555
  • Karimi, , Khorram, E., & Moeini, M. (2016). Identification of congestion by means of integer-valued data envelopment analysis. Computers & Industrial Engineering, 98, 513–521. https://doi.org/10.1016/j.cie.2016.06.017
  • Kazemi Matin, R., & Emrouznejad, A. (2011). An integer-valued data envelopment analysis model with bounded outputs. International Transactions in Operational Research, 18(6), 741– https://doi.org/10.1111/j.1475-3995.2011.00828.x
  • Kazemi Matin, R., & Kuosmanen, T. (2009). Theory of integer-valued data envelopment analysis under alternative returns to scale axioms. Omega, 37(5), 988– https:// doi.org/10.1016/j.omega.2008.11.002
  • Kerstens, K., & Van de Woestyne, I. (2014). A note on a variant of radial measure capable of dealing with negative inputs and outputs in DEA. European Journal of Operational Research, 234(1), 341– https://doi.org/10.1016/j.ejor.2013.10.067
  • Kerstens, K., & Vanden Eeckaut, P. (1999). Estimating returns to scale using non-parametric deterministic technologies: A new method based on goodness-of-fit. European Journal of Operational Research, 113(1), 206– https://doi.org/ 10.1016/S0377-2217(97)00428-1
  • Kerstens, K., Sadeghi, J., Van de Woestyne, I., & Zhang, L. (2022). Malmquist productivity indices and plant capacity utilisation: new proposals and empirical application. Annals of Operations Research, 315(1), 221– https://doi.org/ 10.1007/s10479-022-04771-8
  • Kevork, I. S., Pange, J., Tzeremes, P., & Tzeremes, G. (2017). Estimating Malmquist productivity indexes using probabilistic directional distances: An application to the European banking sector. European Journal of Operational Research, 261(3),
  • 1125– https://doi.org/10.1016/j.ejor.2017.03.012
  • Khezrimotlagh, D., Cook, W. D., & Zhu, J. (2020). A nonparametric framework to detect outliers in estimating production frontiers. European Journal of Operational Research, 286(1), 375– https://doi.org/10.1016/j.ejor.2020.03.014
  • Khoveyni, M., Eslami, R., Fukuyama, H., Yang, G., & Sahoo, K. (2019). Integer data in DEA: Illustrating the drawbacks and recognizing congestion. Computers & Industrial Engineering, 135, 675–688. https://doi.org/10.1016/j.cie.2019.06.046
  • Kim, S. H., Park, C. G., & Park, K. S. (1999). An application of data envelopment analysis in telephone officesevaluation with partial data. Computers & Operations Research, 26 (1), 59– https://doi.org/10.1016/S0305-0548(98)00041-0
  • Kohl, S., & Brunner, J. O. (2020). Benchmarking the benchmarks – Comparing the accuracy of Data Envelopment Analysis models in constant returns to scale settings. European Journal of Operational Research, 285(3), 1042– https://doi.org/ 10.1016/j.ejor.2020.02.031
  • Korhonen, P. J., Soleimani-damaneh, M., & Wallenius, J. (2011). Ratio-based RTS determination in weight-restricted DEA models. European Journal of Operational Research, 215(2), 431– https://doi.org/10.1016/j.ejor.2011.06.017
  • Korhonen, P., & Syrjanen, M. (2004). Resource allocation based on efficiency analysis. Management science, 50(8), 1134–
  • Kremantzis, M. D., Beullens, P., & Klein, J. (2022). A fairer assessment of DMUs in a generalised two-stage DEA structure. Expert Systems with Applications, 187, Article https://doi.org/10.1016/j.eswa.2021.115921
  • Kumar, C. K., & Sinha, K. (1999). Efficiency based production planning and control models. European Journal of Operational Research, 117(3), 450–469. https://doi.org/ 10.1016/S0377-2217(98)00235-5
  • Kumbhakar, S. C., Parmeter, C. F., & Zelenyuk, V. (2021). Stochastic Frontier Analysis: Foundations and Advances I. Handbook of Production Economics (pp. 1–40). Singapore: Springer. https://doi.org/1007/978-981-10-3450-3_9-2
  • Kuosmanen, T. (2005). Weak disposability in nonparametric production analysis with undesirable outputs. American Journal of Agricultural Economics, 87(4), 1077– https://doi.org/10.1111/j.1467-8276.2005.00788.x
  • Kuosmanen, T., & Johnson, A. L. (2010). Data Envelopment Analysis as Nonparametric Least-Squares Regression. Operations Research, 58(1), 149– https://doi.org/ 10.1287/opre.1090.0722
  • Kuosmanen, T., & Kortelainen, M. (2012). Stochastic non-smooth envelopment of data: semi-parametric frontier estimation subject to shape constraints. Journal of Productivity Analysis, 38(1), 11– https://doi.org/10.1007/s11123-010-0201-3
  • Kuosmanen, T., & Matin, R. K. (2009). Theory of integer-valued data envelopment analysis. European Journal of Operational Research, 192(2), 658– https://doi.org/ 10.1016/j.ejor.2007.09.040
  • Lamb, J. D., & Tee, K. H. (2012). Resampling DEA estimates of investment fund performance. European Journal of Operational Research, 223(3), 834– https:// doi.org/10.1016/j.ejor.2012.07.015
  • Lampe, H. W., & Hilgers, D. (2015). Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA. European Journal of Operational Research, 240 (1), 1– https://doi.org/10.1016/j.ejor.2014.04.041
  • Langford, I. H., & Lewis, T. (1998). Outliers in Multilevel Data. Journal of the Royal Statistical Society Series A: Statistics in Society, 161(2), 121– https://doi.org/ 10.1111/1467-985X.00094
  • Lee, C. Y., & Cai, J. Y. (2020). LASSO variable selection in data envelopment analysis with small datasets. Omega, 91, Article https://doi.org/10.1016/j. omega.2018.12.008
  • Lee, H. S., & Zhu, J. (2012). Super-efficiency infeasibility and zero data in DEA. European Journal of Operational Research, 216(2), 429– https://doi.org/10.1016/j. ejor.2011.07.050
  • Li, F., Emrouznejad, A., Yang, G. L., & Li, Y. (2020). Carbon emission abatement quota allocation in Chinese manufacturing industries: An integrated cooperative game data envelopment analysis approach. Journal of the operational research society, 71(8), 1259– https://doi.org/10.1080/01605682.2019.1609892
  • Li, W. H., Liang, L., & Cook, W. D. (2017). Measuring efficiency with products, by-products and parent-offspring relations: A conditional two-stage DEA model. Omega, 68, 95– https://doi.org/10.1016/j.omega.2016.06.006
  • Li, Y., Shi, X., Yang, M., & Liang, L. (2017). Variable selection in data envelopment analysis via Akaike’s information criteria. Annals of Operations Research, 253(1), 453– https://doi.org/10.1007/s10479-016-2382-2
  • Li, F., Wang, Y., Emrouznejad, A., Zhu, Q., & Kou, G. (2022). Allocating a fixed cost across decision-making units with undesirable outputs: A bargaining game approach. Journal of the Operational Research Society, 73(10), 2309– https://doi.org/ 10.1080/01605682.2021.1981781
  • Li, Y., Xie, J., Wang, M., & Liang, L. (2016). Super efficiency evaluation using a common platform on a cooperative game. European Journal of Operational Research, 255(3), 884– https://doi.org/10.1016/j.ejor.2016.06.001
  • Li, F., Zhu, Q., Chen, Z., & Xue, H. (2018). A balanced data envelopment analysis cross-efficiency evaluation approach. Expert Systems with Applications, 106, 154– https://doi.org/10.1016/j.eswa.2018.04.009
  • Liang, L., Cook, W. D., & Zhu, J. (2008). DEA models for two-stage processes: Game approach and efficiency decomposition. Naval research logistics (NRL), 55(7), 643– https://doi.org/10.1002/nav.20308
  • Lim, D. J. (2016). Inverse DEA with frontier changes for new product target settin European Journal of Operational Research, 254(2), 510–516. https://doi.org/ 10.1016/j.ejor.2016.03.059
  • Lin, S. W., & Lu, W. M. (2024). Efficiency assessment of public sector management and culture-led urban regeneration using the enhanced Russell-based directional distance function with stochastic data. Journal of the Operational Research Society, 75 (8), 1624– https://doi.org/10.1080/01605682.2023.2267080
  • Lin, R., & Peng, Y. (2024). A new cross-efficiency meta-frontier analysis method with good ability to identify technology gaps. European Journal of Operational Research, 314(2), 735– https://doi.org/10.1016/j.ejor.2023.10.034
  • Lin, R., & Tu, C. (2021). Cross-efficiency evaluation and decomposition with directional distance function in series and parallel systems. Expert Systems with Applications, 177, Article https://doi.org/10.1016/j.eswa.2021.114933
  • Liu, J. S., Lu, L. Y. Y., Lu, W. M., & Lin, J. Y. (2013). A survey of DEA applications. Omega, 41(5), 893–902. https://doi.org/10.1016/j.omega.2012.11.004
  • Liu, W., Zhou, Z., Ma, C., Liu, D., & Shen, W. (2015). Two-stage DEA models with undesirable input-intermediate-outputs. Omega, 56, 74– https://doi.org/ 10.1016/j.omega.2015.03.009
  • Nasrabadi, , Dehnokhalaji, A., Korhonen, P., Lokman, B., & Wallenius, J. (2022). Robustness of efficiency scores in data envelopment analysis with interval scale data. European Journal of Operational Research, 297(3), 1151–1161. https://doi.org/ 10.1016/j.ejor.2021.06.049
  • Nataraja, R., & Johnson, A. L. (2011). Guidelines for using variable selection techniques in data envelopment analysis. European Journal of Operational Research, 215(3), 662–669. https://doi.org/10.1016/j.ejor.2011.06.045
  • Ngo, T., & Tsui, K. W. H. (2022). Estimating the confidence intervals for DEA efficiency scores of Asia-Pacific airlines. Operational Research, 22(4), 3411– https://doi. org/10.1007/s12351-021-00667-w
  • Nieswand, M., & Seifert, S. (2018). Environmental factors in frontier estimation – A Monte Carlo analysis. European Journal of Operational Research, 265(1), 133– https://doi.org/10.1016/j.ejor.2017.07.047
  • O’Donnell, C., Rao, D., & Battese, G. (2008). Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empirical Economics, 34(2), 231– https://doi.org/10.1007/s00181-007-0119-4 (2008).
  • Odeck, J. (2000). Assessing the relative efficiency and productivity growth of vehicle inspection services: An application of DEA and Malmquist indices. European Journal of Operational Research, 126(3), 501– https://doi.org/10.1016/S0377-2217(99) 00305-7
  • Odeck, J. (2006). Identifying traffic safety best practice: an application of DEA and Malmquist indices. Omega, 34(1), 28– https://doi.org/10.1016/j. omega.2004.07.017
  • Odeck, J. (2009). Statistical precision of DEA and Malmquist indices: A bootstrap application to Norwegian grain producers. Omega, 37(5), 1007– https://doi. org/10.1016/j.omega.2008.11.003
  • Olesen, O. , & Petersen, N. C. (1995). Incorporating quality into data envelopment analysis: a stochastic dominance approach. International journal of production economics, 39(1-2), 117–135. https://doi.org/10.1016/0925-5273(94)00065-I
  • Olesen, O. , & Petersen, N. Cc (2016). Stochastic Data Envelopment Analysis—A review. European Journal of Operational Research, 251(1), 2–21. https://doi.org/ 10.1016/j.ejor.2015.07.058
  • Olesen, O. , Petersen, N. C., & Podinovski, V. V. (2015). Efficiency analysis with ratio measures. European Journal of Operational Research, 245(2), 446–462. https://doi. org/10.1016/j.ejor.2015.03.013
  • Olesen, O. , Petersen, N. C., & Podinovski, V. V. (2017). Efficiency measures and computational approaches for data envelopment analysis models with ratio inputs and outputs. European Journal of Operational Research, 261(2), 640–655. https://doi. org/10.1016/j.ejor.2017.02.021
  • Omrani, H., Shafaat, K., & Emrouznejad, A. (2018). An integrated fuzzy clustering cooperative game data envelopment analysis model with application in hospital efficiency. Expert Systems with Applications, 114, 615– https://doi.org/10.1016/ j.eswa.2018.07.074
  • Omrani, H., Alizadeh, A., Emrouznejad, A., & Teplova, T. (2021). Data envelopment analysis model with decision makers’ preferences: a robust credibility approach. Annals of Operations Research. https://doi.org/1007/s10479-021-04262-2
  • Omrani, H., Oveysi, Z., Emrouznejad, A., & Teplova, T. (2023). A mixed-integer network DEA with shared inputs and undesirable outputs for performance evaluation: Efficiency measurement of bank branches. Journal of the Operational Research Society, 74(4), 1150– https://doi.org/10.1080/01605682.2022.2064783
  • Orkcü, H. H., Ozsoy, V. S., Orkcü, M., & Bal, H. (2019). A neutral cross efficiency approach for basic two stage production systems. Expert Systems with Applications, 125, 333– https://doi.org/10.1016/j.eswa.2019.01.067
  • Orme, C., & Smith, P. (1996). The Potential for Endogeneity Bias in Data Envelopment Analysis. Journal of the Operational Research Society, 47(1), 73– https://doi.org/ 10.1057/jors.1996.7
  • Ouellette, P., & Vierstraete, V. (2004). Technological change and efficiency in the presence of quasi-fixed inputs: A DEA application to the hospital sector. European Journal of Operational Research, 154(3), 755– https://doi.org/10.1016/S0377-2217(02)00712-9
  • Papaioannou, G., & Podinovski, V. V. (2023). Production technologies with ratio inputs and outputs. European Journal of Operational Research, 310(3), 1164– https:// doi.org/10.1016/j.ejor.2023.04.011
  • Paradi, J. C., & Zhu, H. (2013). A survey on bank branch efficiency and performance research with data envelopment analysis. Omega, 41(1), 61– https://doi.org/ 10.1016/j.omega.2011.08.010
  • Park, U., & Simar, L. (1994). Efficient Semiparametric Estimation in a Stochastic Frontier Model. Journal of the American Statistical Association, 89(427), 929–936. https://doi.org/10.1080/01621459.1994.10476826
  • Park, K. S. (2010). Duality, efficiency computations and interpretations in imprecise DEA. European Journal of Operational Research, 200(1), 289– https://doi.org/ 10.1016/j.ejor.2008.11.028
  • Park, K. S., Lee, K. W., Park, M. S., & Kim, D. (2009). Joint use of DEA and constrained canonical correlation analysis for efficiency valuations involving categorical variables. Journal of the Operational Research Society, 60(12), 1775– https:// doi.org/10.1057/jors.2008.136
  • Parmeter, C. F., & Zelenyuk, V. (2019). Combining the Virtues of Stochastic Frontier and Data Envelopment Analysis. Operations Research, 67(6), 1628– https://doi.org/ 10.1287/opre.2018.1831
  • Pastor, J. T., & Lovell, C. A. K. (2005). A global Malmquist productivity index. Economics Letters, 88(2), 266– https://doi.org/10.1016/j.econlet.2005.02.013
  • Pastor, J. T., Lovell, C. A. K., & Aparicio, J. (2020). Defining a new graph inefficiency measure for the proportional directional distance function and introducing a new
  • Malmquist productivity index. European Journal of Operational Research, 281(1), 222– https://doi.org/10.1016/j.ejor.2019.08.021
  • Pastor, J. T., Ruiz, J. L., & Sirvent, I. (1999). An enhanced DEA Russell graph efficiency measure. European Journal of Operational Research, 115(3), 596– https://doi. org/10.1016/S0377-2217(98)00098-8
  • Pastor, J. T., Ruiz, J. L., & Sirvent, I. (2002). A Statistical Test for Nested Radial Dea Models. Operations Research, 50(4), 728– https://doi.org/10.1287/ opre.50.4.728.2866
  • Perelman, S., & Santín, D. (2009). How to generate regularly behaved production data? A Monte Carlo experimentation on DEA scale efficiency measurement. European Journal of Operational Research, 199(1), 303– https://doi.org/10.1016/j. ejor.2008.11.013
  • Petersen, C. (2018). Directional Distance Functions in DEA with Optimal Endogenous Directions. Operations Research, 66(4), 1068–1085. https://doi.org/10.1287/ opre.2017.1711
  • Peykani, P., Emrouznejad, A., Mohammadi, E., & Gheidar-Kheljani, J. (2022). A novel robust network data envelopment analysis approach for performance assessment of mutual funds under uncertainty. Annals of Operations Research. https://doi.org/ 1007/s10479-022-04625-3
  • Peykani, P., Gheidar-Kheljani, J., Farzipoor Saen, R., & Mohammadi, E. (2022). Generalized robust window data envelopment analysis approach for dynamic performance measurement under uncertain panel data. Operational Research, 22(5), 5529– https://doi.org/10.1007/s12351-022-00729-7
  • Peykani, P., Mohammadi, E., & Emrouznejad, A. (2021). An adjustable fuzzy chance-constrained network DEA approach with application to ranking investment firms. Expert systems with applications, 166, Article https://doi.org/10.1016/j. eswa.2020.113938
  • Peyrache, A. (2013). Industry structural inefficiency and potential gains from mergers and break-ups: A comprehensive approach. European Journal of Operational Research, 230(2), 422– https://doi.org/10.1016/j.ejor.2013.04.034
  • Peyrache, A., Rose, C., & Sicilia, G. (2020). Variable selection in Data Envelopment Analysis. European Journal of Operational Research, 282(2), 644– https://doi. org/10.1016/j.ejor.2019.09.028
  • Pham, M., Simar, L., & Zelenyuk, V. (2023). Statistical Inference for Aggregation of Malmquist Productivity Indices. Operations Research. https://doi.org/1287/ opre.2022.2424
  • Podinovski, V. V. (2004a). Bridging the gap between the constant and variable returns-to-scale models: selective proportionality in data envelopment analysis. Journal of the Operational Research Society, 55(3), 265– https://doi.org/10.1057/palgrave. jors.2601691
  • Podinovski, V. V. (2004b). Production trade-offs and weight restrictions in data envelopment analysis. Journal of the Operational Research Society, 55(12), 1311– https://doi.org/10.1057/palgrave.jors.2601794
  • Podinovski, V. V. (2004c). Suitability and redundancy of non-homogeneous weight restrictions for measuring the relative efficiency in DEA. European Journal of Operational Research, 154(2), 380– https://doi.org/10.1016/S0377-2217(03) 00176-0
  • Podinovski, V. V. (2007a). Computation of efficient targets in DEA models with production trade-offs and weight restrictions. European Journal of Operational Research, 181(2), 586– https://doi.org/10.1016/j.ejor.2006.06.041
  • Podinovski, V. V. (2007b). Improving data envelopment analysis by the use of production trade-offs. Journal of the Operational Research Society, 58(10), 1261– https://doi.org/10.1057/palgrave.jors.2602302
  • Podinovski, V. V. (2015). DEA Models with Production Trade–offs and Weight Restrictions (pp. 105–144). https://doi.org/1007/978-1-4899-7553-9_5
  • Podinovski, V. V. (2016). Optimal weights in DEA models with weight restrictions. European Journal of Operational Research, 254(3), 916– https://doi.org/ 10.1016/j.ejor.2016.04.035
  • Podinovski, V. V., & Bouzdine-Chameeva, T. (2013). Weight Restrictions and Free Production in Data Envelopment Analysis. Operations Research, 61(2), 426– https://doi.org/10.1287/opre.1120.1122
  • Podinovski, V. V., & Bouzdine-Chameeva, T. (2015). Consistent weight restrictions in data envelopment analysis. European Journal of Operational Research, 244(1),
  • 201– https://doi.org/10.1016/j.ejor.2015.01.037
  • Podinovski, V. V., & Bouzdine-Chameeva, T. (2016). On single-stage DEA models with weight restrictions. European Journal of Operational Research, 248(3), 1044– https://doi.org/10.1016/j.ejor.2015.07.050
  • Podinovski, V. V., & Wan Husain, W. R. (2017). The hybrid returns-to-scale model and its extension by production trade-offs: an application to the efficiency assessment of public universities in Malaysia. Annals of Operations Research, 250(1), 65– https://doi.org/10.1007/s10479-015-1854-0
  • Podinovski, V. V., Wu, J., & Argyris, (2024). Production trade-offs in models of data envelopment analysis with ratio inputs and outputs: An application to schools in England. European Journal of Operational Research, 313(1), 359–372. https://doi.org/ 10.1016/j.ejor.2023.08.019
  • Portela, M. C. A. S., & Thanassoulis, E. (2001). Decomposing school and school type efficiencies. European Journal of Operational Research, 132(2), 357– https://doi. org/10.1016/S0377-2217(00)00157-0
  • Portela, M. C. A. S., Thanassoulis, E., & Simpson, G. (2004). Negative data in DEA: a directional distance approach applied to bank branches. Journal of the Operational Research Society, 55(10), 1111– https://doi.org/10.1057/palgrave. jors.2601768
  • Post, T., Cherchye, L., & Kuosmanen, T. (2002). Nonparametric Efficiency Estimation In Stochastic Environments. Operations Research, 50(4), 645– https://doi.org/ 10.1287/opre.50.4.645.2854
  • Pourmahmoud, J., & Gholam Azad, M. (2022). Data envelopment analysis using the binary-data. Journal of Modelling in Management, 17(1), 49– https://doi.org/ 10.1108/JM2-10-2019-0246
  • Prieto, A. M., & Zofío, J. L. (2007). Network DEA efficiency in input–output models: With an application to OECD countries. European Journal of Operational Research, 178(1), 292– https://doi.org/10.1016/j.ejor.2006.01.015
  • Pulina, M., Detotto, C., & Paba, A. (2010). An investigation into the relationship between size and efficiency of the Italian hospitality sector: A window DEA approach. European Journal of Operational Research, 204(3), 613– https://doi.org/ 10.1016/j.ejor.2009.11.006
  • Ray, S. C. (1991). Resource-Use Efficiency in Public Schools: A Study of Connecticut Data. Management Science, 37(12), 1620– https://doi.org/10.1287/ mnsc.37.12.1620
  • Ray, S. C., & Ghose, A. (2014). Production efficiency in Indian agriculture: An assessment of the post green revolution years. Omega, 44, 58– https://doi.org/10.1016/j. omega.2013.08.005
  • Reinhard, S., Knox Lovell, C. A., & Thijssen, G. J. (2000). Environmental efficiency with multiple environmentally detrimental variables; estimated with SFA and DEA. European Journal of Operational Research, 121(2), 287– https://doi.org/ 10.1016/S0377-2217(99)00218-0
  • Rezaeiani, M. J., & Foroughi, A. A. (2018). Ranking efficient decision making units in data envelopment analysis based on reference frontier share. European Journal of Operational Research, 264(2), 665– https://doi.org/10.1016/j.ejor.2017.06.064
  • Rogge, (2018a). Composite indicators as generalized benefit-of-the-doubt weighted averages. European Journal of Operational Research, 267(1), 381–392. https://doi. org/10.1016/j.ejor.2017.11.048
  • Rogge, (2018b). On aggregating Benefit of the Doubt composite indicators. European Journal of Operational Research, 264(1), 364–369. https://doi.org/10.1016/j. ejor.2017.06.035
  • Rogge, , & Archer, G. (2021). Measuring and analyzing country change in establishing ease of doing business using a revised version of World Bank’s ease of doing business index. European Journal of Operational Research, 290(1), 373–385. https://doi.org/ 10.1016/j.ejor.2020.07.065
  • Ruggiero, J. (1996). On the measurement of technical efficiency in the public sector. European Journal of Operational Research, 90(3), 553– https://doi.org/10.1016/ 0377-2217(94)00346-7
  • Ruggiero, J. (1998). Non-discretionary inputs in data envelopment analysis. European Journal of Operational Research, 111(3), 461– https://doi.org/10.1016/S0377-2217(97)00306-8
  • Sahoo, K., Khoveyni, M., Eslami, R., & Chaudhury, P. (2016). Returns to scale and most productive scale size in DEA with negative data. European Journal of Operational Research, 255(2), 545–558. https://doi.org/10.1016/j.ejor.2016.05.065
  • Santín, D., & Sicilia, G. (2017). Dealing with endogeneity in data envelopment analysis applications. Expert Systems with Applications, 68, 173– https://doi.org/ 10.1016/j.eswa.2016.10.002
  • Schaffnit, C., Rosen, D., & Paradi, J. C. (1997). Best practice analysis of bank branches: An application of DEA in a large Canadian bank. European Journal of Operational Research, 98(2), 269– https://doi.org/10.1016/S0377-2217(96)00347-5
  • Scheel, H. (2001). Undesirable outputs in efficiency valuations. European Journal of Operational Research, 132(2), 400– https://doi.org/10.1016/S0377-2217(00) 00160-0
  • Schiltz, F., De Witte, K., & Mazrekaj, D. (2020). Managerial efficiency and efficiency differentials in adult education: a conditional and bias-corrected efficiency analysis. Annals of Operations Research, 288(2), 529– https://doi.org/10.1007/s10479-019-03269-0
  • Seiford, L. M., & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 142(1), 16– https://doi.org/10.1016/ S0377-2217(01)00293-4
  • Sekitani, K., & Zhao, Y. (2023). Least-distance approach for efficiency analysis: A framework for nonlinear DEA models. European Journal of Operational Research, 306 (3), 1296– https://doi.org/10.1016/j.ejor.2022.09.001
  • Sengupta, J. K. (1992a). A fuzzy systems approach in data envelopment analysis. Computers & Mathematics with Applications, 24(8–9), 259– https://doi.org/ 10.1016/0898-1221(92)90203-T
  • Sengupta, J. K. (1992b). Measuring efficiency by a fuzzy statistical approach. Fuzzy Sets and Systems, 46(1), 73– https://doi.org/10.1016/0165-0114(92)90268-9
  • Sengupta, J. K. (1994). Measuring dynamic efficiency under risk aversion. European Journal of Operational Research, 74(1), 61– https://doi.org/10.1016/0377-2217 (94)90203-8
  • Sengupta, J. K. (1999). A dynamic efficiency model using data envelopment analysis. International Journal of Production Economics, 62(3), 209– https://doi.org/ 10.1016/S0925-5273(98)00244-8
  • Sexton TR, Silkman RH, Hogan AJ. (1986) Data envelopment analysis: critique and extensions. In: Silkman Richard H, editor. Measuring efficiency: an assessment of data envelopment analysis. San Francisco: Jossey-Bass; 1986: 73-
  • Shabani, A., Visani, F., Barbieri, P., Dullaert, W., & Vigo, D. (2019). Reliable estimation of suppliers’ total cost of ownership: An imprecise data envelopment analysis model with common weights. Omega, 87, 57– https://doi.org/10.1016/j. omega.2018.08.002
  • Sherman, H. D., & Rupert, T. J. (2006). Do bank mergers have hidden or foregone value? Realized and unrealized operating synergies in one bank merger. European Journal of Operational Research, 168(1), 253– https://doi.org/10.1016/j.ejor.2004.05.002
  • Shiraz, R. K., Hatami-Marbini, A., Emrouznejad, A., & Fukuyama, H. (2020). Chance-constrained cost efficiency in data envelopment analysis model with random inputs and outputs. Operational research, 20, 1863– https://doi.org/10.1007/s12351-018-0378-1
  • Silva, M. C. A., Camanho, A. S., & Barbosa, F. (2020). Benchmarking of secondary schools based on Students’ results in higher education. Omega, 95, Article https:// doi.org/10.1016/j.omega.2019.102119
  • Simar, L. (2003). Detecting Outliers in Frontier Models: A Simple Approach. Journal of Productivity Analysis, 20(3), 391– https://doi.org/10.1023/A:1027308001925
  • Simar, L., Vanhems, A., & Wilson, P. W. (2012). Statistical inference for DEA estimators of directional distances. European Journal of Operational Research, 220(3), 853– https://doi.org/10.1016/j.ejor.2012.02.030
  • Simar, L., & Wilson, P. W. (1998). Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models. Management Science, 44(1), 49– https://doi.org/10.1287/mnsc.44.1.49
  • Simar, L., & Wilson, P. W. (1999). Estimating and bootstrapping Malmquist indices. European Journal of Operational Research, 115(3), 459– https://doi.org/ 10.1016/S0377-2217(97)00450-5
  • Simar, L., & Wilson, P. W. (2002). Non-parametric tests of returns to scale. European Journal of Operational Research, 139(1), 115– https://doi.org/10.1016/S0377-2217(01)00167-9
  • Simar, L., & Wilson, P. W. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136(1), 31– https://doi.org/10.1016/j.jeconom.2005.07.009
  • Simar, L., & Zelenyuk, V. (2018). Central Limit Theorems for Aggregate Efficiency. Operations Research, 66(1), 137– https://doi.org/10.1287/opre.2017.1655
  • Simon, J., Simon, C., & Arias, A. (2011). Changes in productivity of Spanish university libraries. Omega, 39(5), 578– https://doi.org/10.1016/j.omega.2010.12.003
  • Smirlis, Y. G., Maragos, E. K., & Despotis, D. K. (2006). Data envelopment analysis with missing values: An interval DEA approach. Applied Mathematics and Computation, 177 (1), 1– https://doi.org/10.1016/j.amc.2005.10.028
  • Soltanifar, M., Ghiyasi, M., Emrouznejad, A., & Sharafi, H. (2024). A novel model for merger analysis and target setting: A CSW-Inverse DEA approach. Expert Systems with Applications, 249, Article https://doi.org/10.1016/j.eswa.2024.123326
  • Song, L., & Liu, F. (2018). An improvement in DEA cross-efficiency aggregation based on the Shannon entropy. International Transactions in Operational Research, 25(2),
  • 705– https://doi.org/10.1111/itor.12361
  • Su, W., Chen, S., Zhang, C., & Li, K. W. (2023). A subgroup dominance-based benefit of the doubt method for addressing rank reversals: A case study of the human development index in Europe. European Journal of Operational Research, 307(3), 1299– https://doi.org/10.1016/j.ejor.2022.11.030
  • Sueyoshi, T., & Goto, M. (2001). Slack-adjusted DEA for time series analysis: Performance measurement of Japanese electric power generation industry in
  • 1984– European Journal of Operational Research, 133(2), 232–259. https://doi. org/10.1016/S0377-2217(00)00295-2
  • Sueyoshi, T., & Goto, M. (2011). Measurement of Returns to Scale and Damages to Scale for DEA-based operational and environmental assessment: How to manage desirable (good) and undesirable (bad) outputs? European Journal of Operational Research, 211 (1), 76– https://doi.org/10.1016/j.ejor.2010.11.013
  • Sueyoshi, T., & Sekitani, K. (2005). Returns to scale in dynamic DEA. European Journal of Operational Research, 161(2), 536– https://doi.org/10.1016/j.ejor.2003.08.055
  • Taleb, M., Ramli, R., & Khalid, R. (2018). Developing a two-stage approach of super efficiency slack-based measure in the presence of non-discretionary factors and mixed integer-valued data envelopment analysis. Expert Systems with Applications, 103, 14– https://doi.org/10.1016/j.eswa.2018.02.037
  • Tan, Y., Wanke, P., Antunes, J., & Emrouznejad, A. (2021). Unveiling endogeneity between competition and efficiency in Chinese banks: a two-stage network DEA and regression analysis. Annals of Operations Research, 306(1–2), 131– https://doi. org/10.1007/s10479-021-04104-1
  • Thanassoulis, E., & Allen, R. (1998). Simulating Weights Restrictions in Data Envelopment Analysis by Means of Unobserved DMUs. Management Science, 44(4), 586– https://doi.org/10.1287/mnsc.44.4.586
  • Thanassoulis, E., Portela, M. C., & Allen, R. (2004). Incorporating Value Judgments in DEA (pp. 99–138). https://doi.org/1007/1-4020-7798-X_4
  • Thanassoulis, E., Shiraz, R. K., & Maniadakis, (2015). A cost Malmquist productivity index capturing group performance. European Journal of Operational Research, 241 (3), 796–805. https://doi.org/10.1016/j.ejor.2014.09.002
  • Thompson, R. G., Dharmapala, P. S., & Thrall, R. M. (1993). Importance for DEA of zeros in data, multipliers, and solutions. Journal of Productivity Analysis, 4(4), 379– https://doi.org/10.1007/BF01073546
  • Trinh, K. D. T., & Zelenyuk, V. (2015). Bootstrap–based testing for network DEA: Some Theory and Applications (WP05/2015; Working Paper Series).
  • Tsionas, M. G. (2022). Convex non-parametric least squares, causal structures and productivity. European Journal of Operational Research, 303(1), 370– https://doi. org/10.1016/j.ejor.2022.02.020
  • Tulkens, H. (1993). On FDH efficiency analysis: Some methodological issues and applications to retail banking, courts, and urban transit. Journal of Productivity Analysis, 4(1–2), 183– https://doi.org/10.1007/BF01073473
  • Valero-Carreras, D., Aparicio, J., & Guerrero, M. (2021). Support vector frontiers: A new approach for estimating production functions through support vector machines. Omega, 104, Article 102490. https://doi.org/10.1016/j.omega.2021.102490
  • Van Puyenbroeck, T., Montalto, V., & Saisana, M. (2021). Benchmarking culture in Europe: A data envelopment analysis approach to identify city-specific strengths. European Journal of Operational Research, 288(2), 584– https://doi.org/ 10.1016/j.ejor.2020.05.058
  • Verbunt, P., & Rogge, (2018). Geometric composite indicators with compromise Benefit-of-the-Doubt weights. European Journal of Operational Research, 264(1), 388–401. https://doi.org/10.1016/j.ejor.2017.06.061
  • Villanueva-Cantillo, J., & Munoz-Marquez, M. (2021). Methodology for calculating critical values of relevance measures in variable selection methods in data envelopment analysis. European Journal of Operational Research, 290(2), 657– https://doi.org/10.1016/j.ejor.2020.08.021
  • Wagner, J. M., & Shimshak, D. G. (2007). Stepwise selection of variables in data envelopment analysis: Procedures and managerial perspectives. European Journal of Operational Research, 180(1), 57– https://doi.org/10.1016/j.ejor.2006.02.048
  • Walheer, (2018). Disaggregation of the cost Malmquist productivity index with joint and output-specific inputs. Omega, 75, 1–12. https://doi.org/10.1016/j. omega.2017.01.012
  • Walheer, (2022). Global Malmquist and cost Malmquist indexes for group comparison. Journal of Productivity Analysis, 58(1), 75–93. https://doi.org/10.1007/s11123-022-00640-5
  • Wanke, P., Barros, C. P., & Emrouznejad, A. (2016). Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping: A case of Mozambican banks. European Journal of Operational Research, 249(1), 378– https://doi.org/ 10.1016/j.ejor.2015.10.018
  • Wei, Q., Zhang, J., & Zhang, X. (2000). An inverse DEA model for inputs/outputs estimate. European journal of operational research, 121(1), 151– https://doi.org/ 10.1016/S0377-2217(99)00007-7
  • Whittaker, G., Fare, R., Grosskopf, S., Barnhart, , Bostian, M., Mueller-Warrant, G., & Griffith, S. (2017). Spatial targeting of agri-environmental policy using bilevel evolutionary optimization. Omega, 66, 15–27. https://doi.org/10.1016/j. omega.2016.01.007
  • Wilson, P. W. (2008). FEAR: A software package for frontier efficiency analysis with R. Socio-Economic Planning Sciences, 42(4), 247– https://doi.org/10.1016/j. seps.2007.02.001
  • Wu, D., Baron, O., & Berman, O. (2009). Bargaining in competing supply chains with uncertainty. European Journal of Operational Research, 197(2), 548– https://doi. org/10.1016/j.ejor.2008.06.032
  • Wu, J., Pan, Y., & Zhou, Z. (2023). Assessing environmental performance with big data: A DEA model with multiple data resources. Computers & Industrial Engineering, 177, Article https://doi.org/10.1016/j.cie.2023.109041
  • Wu, J., Sun, J., & Liang, L. (2012). DEA cross-efficiency aggregation method based upon Shannon entropy. International Journal of Production Research, 50(23), 6726– https://doi.org/10.1016/j.cie.2023.109041
  • Wu, J., & Zhou, Z. (2015). A mixed-objective integer DEA model. Annals of Operations Research, 228(1), 81– https://doi.org/10.1007/s10479-011-0938-8
  • Yan, H., Wei, Q., & Hao, G. (2002). DEA models for resource reallocation and production input/output estimation. European Journal of Operational Research, 136(1), 19– https://doi.org/10.1016/S0377-2217(01)00046-7
  • Yang, H., & Pollitt, M. (2009). Incorporating both undesirable outputs and uncontrollable variables into DEA: The performance of Chinese coal-fired power plants. European Journal of Operational Research, 197(3), 1095– https://doi. org/10.1016/j.ejor.2007.12.052
  • Yang, Z., Wang, X., & Sun, D. (2010). Using the bootstrap method to detect influential DMUs in data envelopment analysis. Annals of Operations Research, 173(1), 89– https://doi.org/10.1007/s10479-009-0520-9
  • Yin, P., Chu, J., Wu, J., Ding, J., Yang, M., & Wang, Y. (2020). A DEA-based two-stage network approach for hotel performance analysis: An internal cooperation perspective. Omega, 93, Article https://doi.org/10.1016/j. omega.2019.02.004
  • Yu, M. M., See, K. F., & Hsiao, (2022). Integrating group frontier and metafrontier directional distance functions to evaluate the efficiency of production units. European Journal of Operational Research, 301(1), 254–276. https://doi.org/ 10.1016/j.ejor.2021.10.054
  • Yu, M. M., & Nguyen, M. A. T. (2023). Productivity changes of Asia-Pacific airlines: A Malmquist productivity index approach for a two-stage dynamic system. Omega, 115, Article https://doi.org/10.1016/j.omega.2022.102774
  • Zanella, A., Camanho, A. S., & Dias, T. G. (2015). Undesirable outputs and weighting schemes in composite indicators based on data envelopment analysis. European Journal of Operational Research, 245(2), 517– https://doi.org/10.1016/j. ejor.2015.03.036
  • Zarrin, M., & Brunner, J. O. (2023). Analyzing the accuracy of variable returns to scale data envelopment analysis models. European Journal of Operational Research, 308(3), 1286– https://doi.org/10.1016/j.ejor.2022.12.015
  • Zelenyuk, V. (2006). Aggregation of Malmquist productivity indexes. European Journal of Operational Research, 174(2), 1076– https://doi.org/10.1016/j. ejor.2005.02.061
  • Zha, Y., & Liang, L. (2010). Two-stage cooperation model with input freely distributed among the stages. European Journal of Operational Research, 205(2), 332– https://doi.org/10.1016/j.ejor.2010.01.010
  • Zhang, Y. J., & Chen, M. Y. (2018). Evaluating the dynamic performance of energy portfolios: Empirical evidence from the DEA directional distance function. European Journal of Operational Research, 269(1), 64– https://doi.org/10.1016/j. ejor.2017.08.008
  • Zhao, Y., Morita, H., & Maruyama, Y. (2019). The measurement of productive performance with consideration for allocative efficiency. Omega, 89, 21– https:// doi.org/10.1016/j.omega.2018.09.012
  • Zhou, P., Ang, W., & Poh, K. L. (2008). A survey of data envelopment analysis in energy and environmental studies. European Journal of Operational Research, 189(1), 1–18. https://doi.org/10.1016/j.ejor.2007.04.042
  • Zhou, Z., Gao, M., Xiao, H., Wang, R., & Liu, W. (2021). Big data and portfolio optimization: A novel approach integrating DEA with multiple data sources. Omega, 104, Article https://doi.org/10.1016/j.omega.2021.102479
  • Zhou, Z., Guo, X., Wu, H., & Yu, J. (2018). Evaluating air quality in China based on daily data: Application of integer data envelopment analysis. Journal of Cleaner Production, 198, 304– https://doi.org/10.1016/j.jclepro.2018.06.180
  • Zhu, J. (2003). Imprecise data envelopment analysis (IDEA): A review and improvement with an application. European Journal of Operational Research, 144(3), 513– https://doi.org/10.1016/S0377-2217(01)00392-7
  • Zhu, J. (2004). Imprecise DEA via Standard Linear DEA Models with a Revisit to a Korean Mobile Telecommunication Company. Operations Research, 52(2), 323– https:// doi.org/10.1287/opre.1030.0072
  • Zhu, J. (2022). DEA under big data: data enabled analytics and network data envelopment analysis. Annals of Operations Research, 309(2), 761– https://doi. org/10.1007/s10479-020-03668-8
  • Zhu, J., & Cook, W. D. (2007). Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis (J. Zhu & W. D. Cook, Eds.). Springer US. https://doi. org/1007/978-0-387-71607-7.
  • Zhu, , Zhu, C., & Emrouznejad, A. (2021). A combined machine learning algorithms and DEA method for measuring and predicting the efficiency of Chinese manufacturing listed companies. Journal of Management Science and Engineering, 6(4), 435–448. https://doi.org/10.1016/j.jmse.2020.10.001