Ranking decision making units based on the multi-directional efficiency measure

References

• Adler, N., Friedman, L., & Sinuany-Stern, Z. (2002). Review of ranking methods in the data envelopment analysis context. European Journal of Operational Research, 140(2), 249–265. https://doi.org/10.1016/S0377-2217(02)00068-1
   Web of Science ®Google Scholar
• Alcaraz, J., Monge, J. F., & Ramón, N. (2020). Ranking ranges in cross-efficiency evaluations: A metaheuristic approach. Journal of the Operational Research Society, 71(1) 1–15. https://doi.org/10.1080/01605682.2020.1860662
   Google Scholar
• Aldamak, A., & Zolfaghari, S. (2017). Review of efficiency ranking methods in data envelopment analysis. Measurement, 106, 161–172. https://doi.org/10.1016/j.measurement.2017.04.028
   Web of Science ®Google Scholar
• Andersen, P., & Petersen, N. 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
   Web of Science ®Google Scholar
• Asmild, M., Baležentis, T., & Hougaard, J. L. (2016). Multi-directional program efficiency: The case of Lithuanian family farms. Journal of Productivity Analysis, 45(1), 23–33. https://doi.org/10.1007/s11123-014-0419-6
   Web of Science ®Google Scholar
• Asmild, M., Hougaard, J. L., Kronborg, D., & Kvist, H. K. (2003). Measuring inefficiency via potential improvements. Journal of Productivity Analysis, 19(1), 59–76. https://doi.org/10.1023/A:1021822103696
   Web of Science ®Google Scholar
• Asmild, M., Kronborg, D., Mahbub, T., & Matthews, K. (2019). The efficiency patterns of Islamic banks during the global financial crisis: The case of Bangladesh. The Quarterly Review of Economics and Finance, 74, 67–74. https://doi.org/10.1016/j.qref.2018.04.004
   Web of Science ®Google Scholar
• Asmild, M., Kronborg, D., Mahbub, T., & Matthews, K. (2021). Inefficiency patterns in family-owned banks in Bangladesh. Journal of Economic Studies, 1–15. https://doi.org/10.1108/JES-06-2020-0286
   Google Scholar
• Asmild, M., & Pastor, J. T. (2010). Slack free MEA and RDM with comprehensive efficiency measures. Omega, 38(6), 475–483. https://doi.org/10.1016/j.omega.2009.12.004
   Web of Science ®Google Scholar
• Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092. https://doi.org/10.1287/mnsc.30.9.1078
   Web of Science ®Google Scholar
• Chambers, R. G., Chung, Y., & Färe, R. (1996). Benefit and distance functions. Journal of Economic Theory, 70(2), 407–419. https://doi.org/10.1006/jeth.1996.0096
   Web of Science ®Google Scholar
• Contreras, I., Lozano, S., & Hinojosa, M. A. (2021). A DEA cross-efficiency approach based on bargaining theory. Journal of the Operational Research Society, 72(5), 1156–1112. https://doi.org/10.1080/01605682.2020.1755898
   Web of Science ®Google Scholar
• de Blas, C. S., Martin, J. S., & Gonzalez, D. G. (2018). Combined social networks and data envelopment analysis for ranking. European Journal of Operational Research, 266(3), 990–999. https://doi.org/10.1016/j.ejor.2017.10.025
   Web of Science ®Google Scholar
• Dehnokhalaji, A., Hallaji, B., Soltani, N., & Sadeghi, J. (2017). Convex cone-based ranking of decision-making units in DEA. OR Spectrum, 39(3), 861–880. https://doi.org/10.1007/s00291-017-0477-z
   Web of Science ®Google Scholar
• Emrouznejad, A., Rostami-Tabar, B., & Petridis, K. (2016). A novel ranking procedure for forecasting approaches using data envelopment analysis. Technological Forecasting and Social Change, 111, 235–243. https://doi.org/10.1016/j.techfore.2016.07.004
   Web of Science ®Google Scholar
• Hinojosa, M. A., Lozano, S., Borrero, D. V., & Mármol, A. M. (2017). Ranking efficient DMUs using cooperative game theory. Expert Systems with Applications, 80, 273–283. https://doi.org/10.1016/j.eswa.2017.03.004
   Web of Science ®Google Scholar
• Holvad, T., Hougaard, J. L., Kronborg, D., & Kvist, H. K. (2004). Measuring inefficiency in the Norwegian bus industry using multi-directional efficiency analysis. Transportation, 31(3), 349–369. https://doi.org/10.1023/B:PORT.0000025400.33939.6d
   Web of Science ®Google Scholar
• Hosseinzadeh Lotfi, F., Jahanshahloo, G. R., Khodabakhshi, M., Rostamy-Malkhlifeh, M., Moghaddas, Z., & Vaez-Ghasemi, M. (2013). A review of ranking models in data envelopment analysis. Journal of Applied Mathematics, 2013, 1–20. https://doi.org/10.1155/2013/492421
   Google Scholar
• Hougaard, J. L., Kronborg, D., & Overgård, C. (2004). Improvement potential in Danish elderly care. Health Care Management Science, 7(3), 225–235. https://doi.org/10.1023/B:HCMS.0000039385.30011.8e
   Google Scholar
• Jahanshahloo, G. R., Lotfi, F. H., Rezai, H. Z., & Balf, F. R. (2005). Using Monte Carlo method for ranking efficient DMUs. Applied Mathematics and Computation, 162(1), 371–379. https://doi.org/10.1016/j.amc.2003.12.139
   Web of Science ®Google Scholar
• Kritikos, M. N. (2017). A full ranking methodology in data envelopment analysis based on a set of dummy decision making units. Expert Systems with Applications, 77, 211–225. https://doi.org/10.1016/j.eswa.2017.01.042
   Web of Science ®Google Scholar
• Lei, X., Zhang, X., Dai, Q., & Li, L. (2021). Dynamic evaluation on the energy and environmental performance of China's transportation sector: A ZSG-MEA window analysis. Environmental Science and Pollution Research International, 28(9), 11454–11468. https://doi.org/10.1007/s11356-020-11314-w
   Web of Science ®Google Scholar
• Li, F., Yan, Z., Zhu, Q., Yin, M., & Kou, G. (2020b). Allocating a fixed cost across decision making units with explicitly considering efficiency rankings. Journal of the Operational Research Society, 72(6), 1432–1415. https://doi.org/10.1080/01605682.2020.1718561
   Web of Science ®Google Scholar
• Li, Y., Shi, X., Emrouznejad, A., & Liang, L. (2020a). Ranking intervals for two-stage production systems. Journal of the Operational Research Society, 71(2), 209–224. https://doi.org/10.1080/01605682.2018.1535267
   Web of Science ®Google Scholar
• Liu, X., Chu, J., Yin, P., & Sun, J. (2017). DEA cross-efficiency evaluation considering undesirable output and ranking priority: A case study of eco-efficiency analysis of coal-fired power plants. Journal of Cleaner Production, 142, 877–885. https://doi.org/10.1016/j.jclepro.2016.04.069
   Web of Science ®Google Scholar
• Lozano, S., & Calzada-Infante, L. (2019). Efficiency ranking using dominance network and multiobjective optimization indexes. Expert Systems with Applications, 126, 83–91. https://doi.org/10.1016/j.eswa.2019.02.016
   Web of Science ®Google Scholar
• Lozano, S., & Soltani, N. (2018). DEA target setting using lexicographic and endogenous directional distance function approaches. Journal of Productivity Analysis, 50(1–2), 55–70. https://doi.org/10.1007/s11123-018-0534-x
   Web of Science ®Google Scholar
• Lozano, S., & Soltani, N. (2020). Efficiency assessment using a multidirectional DDF approach. International Transactions in Operational Research, 27(4), 2064–2080. https://doi.org/10.1111/itor.12617
   Web of Science ®Google Scholar
• Podinovski, V. V., & Bouzdine-Chameeva, T. (2017). Solving DEA models in a single optimization stage: Can the non-Archimedean infinitesimal be replaced by a small finite epsilon? European Journal of Operational Research, 257(2), 412–419. https://doi.org/10.1016/j.ejor.2016.09.022
   Web of Science ®Google Scholar
• Rakhshan, S. A. (2017). Efficiency ranking of decision making units in data envelopment analysis by using TOPSIS-DEA method. Journal of the Operational Research Society, 68(8), 906–918. https://doi.org/10.1057/s41274-017-0237-0
   Web of Science ®Google Scholar
• Rubinstein, R. Y., & Kroese, D. P. (2016). Simulation and the Monte Carlo method (3rd ed., Chapter 4). John Wiley & Sons. Inc.
   Google Scholar
• Sharp, J. A., Meng, W., & Liu, W. (2007). A modified slacks-based measure model for data envelopment analysis with ‘natural’ negative outputs and inputs. Journal of the Operational Research Society, 58(12), 1672–1677. https://doi.org/10.1057/palgrave.jors.2602318
   Web of Science ®Google Scholar
• Shirdel, G. H., & Ramezani-Tarkhorani, S. (2020). A new method for ranking decision making units using common set of weights: A developed criterion. Journal of Industrial & Management Optimization, 16(2), 633–265. https://doi.org/10.3934/jimo.2018171
   Web of Science ®Google Scholar
• Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498–509. https://doi.org/10.1016/S0377-2217(99)00407-5
   Web of Science ®Google Scholar
• Tone, K. (2002). A slacks-based measure of super-efficiency in data envelopment analysis. European Journal of Operational Research, 143(1), 32–41. https://doi.org/10.1016/S0377-2217(01)00324-1
   Web of Science ®Google Scholar
• Tone, K., Chang, T.-S., & Wu, C.-H. (2020). Handling negative data in slacks-based measure data envelopment analysis models. European Journal of Operational Research, 282(3), 926–935. https://doi.org/10.1016/j.ejor.2019.09.055
   Web of Science ®Google Scholar
• Wang, K., Wei, Y. M., & Zhang, X. (2013). Energy and emissions efficiency patterns of Chinese regions: A multi-directional efficiency analysis. Applied Energy, 104, 105–116. https://doi.org/10.1016/j.apenergy.2012.11.039
   Web of Science ®Google Scholar
• Wang, K., Xian, Y., Lee, C. Y., Wei, Y. M., & Huang, Z. (2019). On selecting directions for directional distance functions in a non-parametric framework: A review. Annals of Operations Research, 278(1–2), 43–76. https://doi.org/10.1007/s10479-017-2423-5
   Web of Science ®Google Scholar
• Wen, M., Zhang, Q., Kang, R., & Yang, Y. (2017). Some new ranking criteria in data envelopment analysis under uncertain environment. Computers & Industrial Engineering, 110, 498–504. https://doi.org/10.1016/j.cie.2017.05.034
   Web of Science ®Google Scholar
• Zhu, J. (2000). Multi-factor performance measure model with an application to Fortune 500 companies. European Journal of Operational Research, 123(1), 105–124. https://doi.org/10.1016/S0377-2217(99)00096-X
   Web of Science ®Google Scholar
• Zhu, N., Baležentis, T., Yu, Z., & Wu, W. (2020). Multi-directional meta-frontier DEA model for total factor productivity growth in the chinese banking sector: A disaggregation approach. Informatica, 31(1), 185–204. https://doi.org/10.15388/20-INFOR397
   Web of Science ®Google Scholar
• Zhu, N., Stjepcevic, J., Baležentis, T., Yu, Z., & Wang, B. (2017). How does corporate social responsibility impact banking efficiency: A case in China. Economics and Management, 20(4), 70–87. https://doi.org/10.15240/tul/001/2017-4-006
   Google Scholar
• Zohrehbandian, M., Makui, A., & Alinezhad, A. (2010). A compromise solution approach for finding common weights in DEA: An improvement to Kao and Hung’s approach. Journal of the Operational Research Society, 61(4), 604–610. https://doi.org/10.1057/jors.2009.4
   Web of Science ®Google Scholar