Measuring the efficiency of two-stage network processes: A satisficing DEA approach

References

• Banker, R. D., & Morey, R. C. (1986). Efficiency analysis for exogenously fixed inputs and outputs. Operations Research, 34(4), 513–521. doi:10.1287/opre.34.4.513
   Web of Science ®Google Scholar
• Charles, V., & Kumar, M. (2012). An introduction to data envelopment analysis. In V. Charles & M. Kumar (Eds.), Data envelopment analysis and its applications to management (pp. 1–28). Newcastle upon Tyne, UK: Cambridge Scholars Publishing.
   Google Scholar
• Charles, V., & Kumar, M. (2014). Satisficing data envelopment analysis: An application to SERVQUAL efficiency. Measurement, 51, 71–80. 10.1016/j.measurement.2014.01.023
   Web of Science ®Google Scholar
• Charles, V., Peretto, C., & Gherman, T. (2016). Technical efficiency analytics for the public banks in Argentina. Public Enterprise Half-Yearly Journal, 22(1), 118–139. doi:10.21571/pehyj.2016.2201.07
   Google Scholar
• Charles, V., Tsolas, I. E., & Gherman, T. (2018). Satisficing data envelopment analysis: A Bayesian approach for peer mining in the banking sector. Annals of Operations Research, 269(1-2), 81–102. doi:10.1007/s10479-017-2552-x
   Web of Science ®Google Scholar
• Charnes, A., & Cooper, W. W. (1963). Deterministic equivalents for optimizing and satisficing under chance constraints. Operations Research, 11(1), 18–39. doi:10.1287/opre.11.1.18
   Web of Science ®Google Scholar
• Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. doi:10.1016/0377-2217(78)90138-8
   Web of Science ®Google Scholar
• Chen, Z., Matousek, R., & Wanke, P. (2018). Chinese bank efficiency during the global financial crisis: A combined approach using satisficing DEA and Support Vector Machines⋆. The North American Journal of Economics and Finance, 43, 71–86. doi:10.1016/j.najef.2017.10.003
   Web of Science ®Google Scholar
• Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-Solver Software. Boston, MA: Kluwer Academic Publishers.
   Google Scholar
• Cooper, W. W., Huang, Z., & Li, S. X. (1996). Satisficing DEA models under chance constraints. Annals of Operations Research, 66(4), 279–295. doi:10.1007/BF02187302
   Web of Science ®Google Scholar
• Cooper, W. W., Huang, Z., & Li, S. X. (2011). Chance-constrained DEA. In W. W. Cooper, L. M. Seiford, & J. Zhu (Eds.), Handbook on data envelopment analysis (pp. 211–240). Boston, MA: Springer.
   Google Scholar
• Kao, C., & Liu, S.-T. (2011). Efficiencies of two-stage systems with fuzzy data. Fuzzy Sets and Systems, 176(1), 20–35. doi:10.1016/j.fss.2011.03.003
   Web of Science ®Google Scholar
• Khalili-Damghani, K., & Shahmir, Z. (2015). Uncertain network data envelopment analysis with undesirable outputs to evaluate the efficiency of electricity power production and distribution processes. Computers & Industrial Engineering, 88, 131–150. doi:10.1016/j.cie.2015.06.013
   Web of Science ®Google Scholar
• Liang, L., Yang, F., Cook, W. D., & Zhu, J. (2006). DEA models for supply chain efficiency evaluation. Annals of Operations Research, 145(1), 35–49. doi:10.1007/s10479-006-0026-7
   Web of Science ®Google Scholar
• Liang, L., Cook, W. D., & Zhu, J. (2008). DEA models for two-stage processes: Game approach and efficiency decomposition. Naval Research Logistics, 55(7), 643–653. doi:10.1002/nav.20308
   Web of Science ®Google Scholar
• Liu, S.-T. (2014). Restricting weight flexibility in fuzzy two-stage DEA. Computers & Industrial Engineering, 74, 149–160. doi:10.1016/j.cie.2014.05.011
   Web of Science ®Google Scholar
• Lozano, S. (2014). Process efficiency of two-stage systems with fuzzy data. Fuzzy Sets and Systems, 243, 36–49. doi:10.1016/j.fss.2013.05.012
   Web of Science ®Google Scholar
• Olesen, O. B. (2006). Comparing and combining two approaches for chance-constrained DEA. Journal of Productivity Analysis, 26(2), 103–119. doi:10.1007/s11123-006-0008-4
   Web of Science ®Google Scholar
• Olesen, O. B., & Petersen, N. (1995). Chance constrained efficiency evaluation. Management Science, 41(3), 442–457. doi:10.1287/mnsc.41.3.442
   Web of Science ®Google Scholar
• Paradi, J. C., & Zhu, H. (2013). A survey on bank branch efficiency and performance research with data envelopment analysis. Omega, 41(1), 61–79. doi:10.1016/j.omega.2011.08.010
   Web of Science ®Google Scholar
• Simon, H. A. (1957). Models of man, social and rational. New York, NY: Wiley.
   Google Scholar
• Tsolas, I. E., & Charles, V. (2015). Incorporating risk into bank efficiency: A satisficing DEA approach to assess the Greek banking crisis. Expert Systems with Applications, 42(7), 3491–3500. doi:10.1016/j.eswa.2014.12.033
   Web of Science ®Google Scholar
• Udhayakumar, A., Charles, V., & Kumar, M. (2011). Stochastic simulation based genetic algorithm for chance-constrained data envelopment analysis problems. Omega, 39(4), 387–397. doi:10.1016/j.omega.2010.09.002
   Web of Science ®Google Scholar
• Yang, F., Wu, D., Liang, L., Bi, G., & Wu, D. D. (2011). Supply chain DEA: Production possibility set and performance evaluation model. Annals of Operations Research, 185(1), 195–211. doi:10.1007/s10479-008-0511-2
   Web of Science ®Google Scholar
• Zhou, Z., Lin, L., Xiao, H., Ma, C., & Wu, S. (2017). Stochastic network DEA models for two-stage systems under the centralized control organization mechanism. Computers & Industrial Engineering, 110, 404–412. doi:10.1016/j.cie.2017.06.005
   Web of Science ®Google Scholar
• Zhu, J. (2009). Quantitative models for performance evaluation and benchmarking: Data envelopment analysis with spreadsheets. International Series in Operations Research & Management Science, 126, 14–40.
   Google Scholar
• Zhu, Y., Li, Y., & Liang, L. (2017). A variation of two-stage SBM with leader–follower structure: An application to Chinese commercial banks. Journal of the Operational Research Society, 69, 840–848. doi:10.1057/s41274-017-0262-z
   Web of Science ®Google Scholar