115
Views
0
CrossRef citations to date
0
Altmetric
Articles

Efficiency evaluation with cross-efficiency in the presence of undesirable outputs in stochastic environment

, , &
Pages 7691-7712 | Received 31 Dec 2019, Accepted 16 Jan 2021, Published online: 08 Feb 2021

References

  • Andersen, P., and N. C. Petersen. 1993. A procedure for ranking efficient units in data envelopment analysis. Management Science 39 (10):1261–64. doi:10.1287/mnsc.39.10.1261.
  • Banker, R. D., A. Charnes, and W. W. Cooper. 1984. Some method for estimating technical and scale inefficiencies in data envelopment analysis. Management Science 30 (9):1078–92. doi:10.1287/mnsc.30.9.1078.
  • Beck, A. 2014. Introduction to nonlinear optimization - theory, algorithms, and applications with MATLAB. Philadelphia, PA: MOS-SIAM series on optimization, SIAM.
  • Branda, M., and M. Kopa. 2016. DEA models equivalent to general Nth order stochastic dominance efficiency tests. Operations Research Letters 44 (2):285–89. doi:10.1016/j.orl.2016.02.007.
  • Bruni, M. E., D. Conforti, P. Beraldi, and E. Tundis. 2009. Probabilistically constrained models for efficiency and dominance in DEA. International Journal of Production Economics 117 (1):219–28. doi:10.1016/j.ijpe.2008.10.011.
  • Charles, V., and F. Cornillier. 2017. Value of the stochastic efficiency in data envelopment analysis. Expert Systems with Applications 81:349–57. doi:10.1016/j.eswa.2017.03.061.
  • Charnes, A., and W. W. Cooper. 1959. Chance-constrained programming. Management Science 6 (1):73–79. doi:10.1287/mnsc.6.1.73.
  • Charnes, A., W. W. Cooper, and E. Rhodes. 1978. Measuring the efficiency of decision making units. European Journal of Operational Research 2 (6):429–44. doi:10.1016/0377-2217(78)90138-8.
  • Charnes, A., W. W. Cooper, and G. H. Symonds. 1958. Cost horizons and certainty equivalents: An approach to stochastic programming of heating oil. Management Science 4 (3):235–195. doi:10.1287/mnsc.4.3.235.
  • Cook, W., Y. Roll, and A. Kazakov. 1990. DEA model for measuring the relative efficiencies of highway maintenance patrols. INFOR 28 (2):811–18.
  • Cooper, W. W., H. Deng, Z. Huang, and S. X. Li. 2002. Chance constrained programming approaches to technical efficiencies and inefficiencies in stochastic data envelopment analysis. Journal of the Operational Research Society 53 (12):1347–56. doi:10.1057/palgrave.jors.2601433.
  • Cooper, W. W., H. Deng, Z. M. Huang, and S. X. Li. 2004. Chance constrained programming approaches to congestion in stochastic data envelopment analysis. European Journal of Operational Research 155 (2):487–501. doi:10.1016/S0377-2217(02)00901-3.
  • Cooper, W. W., Z. M. Huang, V. Lelas, S. X. Li, and O. B. Olesen. 1998. Chance constrained programming formulations for stochastic characterizations of efficiency and dominance in DEA. Journal of Productivity Analysis 9 (1):53–79. doi:10.1023/A:1018320430249.
  • Cooper, W. W., Z. Huang, and S. Li. 1996. Satisficing DEA models under chance constraints. Annals of Operations Research 66 (4):279–95. doi:10.1007/BF02187302.
  • Dantzig, G. B. 1955. Linear programming under uncertainty. Management Science 1 (3/4):197–206. doi:10.1287/mnsc.1.3-4.197.
  • Davtalab-Olyaie, M., M. Asgharian, and V. Partovi Nia. 2019. Stochastic ranking and dominance in DEA. International Journal of Production Economics 214:125–38. doi:10.1016/j.ijpe.2019.04.004.
  • Despotis, D. K., and Y. G. Smirlis. 2002. Data envelopment analysis with imprecise data. European Journal of Operational Research 140 (1):24–36. doi:10.1016/S0377-2217(01)00200-4.
  • Dimitrov, S., and W. Sutton. 2013. Generalized symmetric weight assignment technique: Incorporating managerial preferences in data envelopment analysis using a penalty function. Omega 41 (1):48– 54. doi:10.1016/j.omega.2011.07.012.
  • Dotoli, M., N. Epicoco, M. Falagario, and F. Sciancalepore. 2016. A stochastic cross-efficiency data envelopment analysis approach for supplier selection under uncertainty. International Transactions in Operational Research 23 (4):725–48. doi:10.1111/itor.12155.
  • Doyle, J. R., and R. H. Green. 1994. Efficiency and cross-efficiency in DEA: Derivations, meanings and uses. The Journal of the Operational Research Society 45 (5):567–78. doi:10.2307/2584392.
  • Esmaeilzadeh, A., and A. Hadi-Vencheh. 2013. A super-efficiency model for measuring aggregative efficiency of multi-period production systems. Measurement 46 (10):3988–93. doi:10.1016/j.measurement.2013.07.023.
  • Esmaeilzadeh, A., and A. Hadi-Vencheh. 2015. A new method for complete ranking of DMUs. Optimization 64 (5):1177–93. doi:10.1080/02331934.2013.848860.
  • Hadi-Vencheh, A., and A. Esmaeilzadeh. 2013. A new super-efficiency model in the presence of negative data. Journal of the Operational Research Society 64 (3):396–401. doi:10.1057/jors.2012.22.
  • Hadi-Vencheh, A., P. Wanke, and A. Jamshidi. 2020a. What does cost structure have to say about thermal plant energy efficiency? The case from Angola. Energies 13 (9):2404. doi:10.3390/en13092404.
  • Hadi‐Vencheh, A., P. Wanke, A. Jamshidi, and Z. Chen. 2020b. Sustainability of Chinese airlines: A modified slack‐based measure model for CO2 emissions. Expert Systems 37 (3):e12302. doi:10.1111/exsy.12302.
  • Huang, Z., and S. X. Li. 1996. Dominance stochastic models in data envelopment analysis. European Journal of Operational Research 95 (2):390–403. doi:10.1016/0377-2217(95)00293-6.
  • Jin, J., D. Zhou, and P. Zhou. 2014. Measuring environmental performance with stochastic environmental DEA: The case of APEC economies. Economic Modelling 38:80–86. doi:10.1016/j.econmod.2013.12.017.
  • Jradi, S., and J. Ruggiero. 2019. Stochastic data envelopment analysis: A quantile regression approach to estimate the production frontier. European Journal of Operational Research 278 (2):385–93. doi:10.1016/j.ejor.2018.11.017.
  • Kao, C., and S. Liu. 2019. Stochastic efficiency measures for production units with correlated data. European Journal of Operational Research 273 (1):278–87. doi:10.1016/j.ejor.2018.07.051.
  • Kuah, C. T., K. Y. Wong, and W. P. Wong. 2012. Monte Carlo data envelopment analysis with genetic algorithm for knowledge management performance measurement. Expert Systems with Applications 39 (10):9348–58. doi:10.1016/j.eswa.2012.02.140.
  • Land, K. C., C. A. K. Lovell, and S. Thore. 1993. Chance constrained data envelopment analysis. Managerial and Decision Economics 14 (6):541–54. doi:10.1002/mde.4090140607.
  • Lertworasirikul, S., S. C. Fang, J. A. Joines, and H. L. W. Nuttle. 2003. Fuzzy data envelopment analysis (DEA): A possibility approach. Fuzzy Sets and Systems 139 (2):379–94. doi:10.1016/S0165-0114(02)00484-0.
  • Liu, X. H., J. Chu, P. Yin, and J. Sun. 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 142:877–85. doi:10.1016/j.jclepro.2016.04.069.
  • Liu, W., Y. Wang, and S. Lyu. 2017. The upper and lower bound evaluation based on the quantile efficiency in stochastic data envelopment analysis. Expert Systems with Applications 85:14–24. doi:10.1016/j.eswa.2017.05.023.
  • Lu, C. C., Y. H. Chiu, M. K. Shyu, and J. H. Lee. 2013. Measuring CO2 emission efficiency in OECD countries: Application of the hybrid efficiency model. Economic Modelling 32:130–35. doi:10.1016/j.econmod.2013.01.047.
  • Mandal, S. K. 2010. Do undesirable output and environmental regulation matter in energy efficiency analysis? Evidence from Indian cement industry. Energy Policy 38 (10):6076–83. doi:10.1016/j.enpol.2010.05.063.
  • Morita, H., and L. M. Seiford. 1999. Characteristics on stochastic DEA efficiency-Reliability and probability being efficient. Journal of the Operations Research Society of Japan 42 (4):389–404. doi:10.1016/S0453-4514(00)87109-4.
  • Olesen, O. B. 2006. Comparing and combining two approaches for chance constrained DEA. Journal of Productivity Analysis 26 (2):103–19. doi:10.1007/s11123-006-0008-4.
  • Olesen, O. B., and N. C. Petersen. 1995. Chance constrained efficiency evaluation. Management Science 41 (3):442–57. doi:10.1287/mnsc.41.3.442.
  • Olesen, O. B., and N. Petersen. 2016. Stochastic data envelopment analysis – a review. European Journal of Operational Research 251 (1):2–13. doi:10.1016/j.ejor.2015.07.058.
  • Park, S., C. Ok, and C. Ha. 2018. A stochastic simulation-based holistic evaluation approach with DEA for vendor selection. Computers & Operations Research 100:368–78. doi:10.1016/j.cor.2017.08.005.
  • Ruiz, J. L., and I. Sirvent. 2012. On the DEA total weight flexibility and the aggregation in cross-efficiency evaluations. European Journal of Operational Research 223 (3):732–38. doi:10.1016/j.ejor.2012.06.011.
  • Sexton, T. R., R. H. Silkman, and A. J. Hogan. 1986. Data envelopment analysis: Critique and extensions. In: R. H. Silkman (Ed.), Measuring efficiency: An assessment of data envelopment analysis, 73–105. San Francisco, CA: Jossey-Bass.
  • Shi, G. M., J. Bi, and J. N. Wang. 2010. Chinese regional industrial energy efficiency evaluation based on a DEA model of fixing non-energy inputs. Energy Policy 38 (10):6172–79. doi:10.1016/j.enpol.2010.06.003.
  • Simar, L., I. Keilegom, and W. Zelenyuk. 2017. Nonparametric least squares methods for stochastic frontier models. Journal of Productivity Analysis 47 (3):189–204. doi:10.1007/s11123-016-0474-2.
  • Simar, L., and V. Zelenyuk. 2011. Stochastic FDH/DEA estimators for frontier analysis. Journal of Productivity Analysis 36 (1):1–20. doi:10.1007/s11123-010-0170-6.
  • Steuer, R. E. 1986. Multiple criteria optimization. Theory, computation and applications. San Francisco, CA: Wiley.
  • Sueyoshi, T., and M. Goto. 2010. Should the US clean air act include CO2 emission control? Examination by data envelopment analysis. Energy Policy 38 (10):5902–11. doi:10.1016/j.enpol.2010.05.044.
  • Unsal, M. G., D. Friesner, and R. Rosenman. 2019. New posterior distributions for the incidence of inefficiency in DEA scores. Communications in Statistics-Theory and Methods. Advance online publication. doi:10.1080/03610926.2019.1653920.
  • Wang, Y. M., and K. S. Chin. 2010. A neutral DEA model for cross-efficiency evaluation and its extension. Expert Systems with Applications 37 (5):3666–75. doi:10.1016/j.eswa.2009.10.024.
  • Wanke, P., Y. Tan, J. Antunes, and A. Hadi-Vencheh. 2020. Business environment drivers and technical efficiency in the Chinese energy industry: A robust Bayesian stochastic frontier analysis. Computers & Industrial Engineering 106487.
  • Wong, W. P. 2009. Performance evaluation of supply chain in stochastic environment: Using a simulation-based DEA framework. International Journal of Business Performance and Supply Chain Modelling 1 (2/3):203–28. doi:10.1504/IJBPSCM.2009.030642.
  • Wu, J., J. Chu, J. Sun, and Q. Zhu. 2016. DEA cross-efficiency evaluation based on Pareto improvement. European Journal of Operational Research 248 (2):571–79. doi:10.1016/j.ejor.2015.07.042.
  • Wu, C., Y. Li, Q. Liu, and K. Wang. 2013. A stochastic DEA model considering undesirable outputs with weak disposability. Mathematical and Computer Modelling 58 (5-6):980–89. doi:10.1016/j.mcm.2012.09.022.
  • Zhou, Z., L. Lin, H. Xiao, C. Ma, and S. Wu. 2017. Stochastic network DEA models for two-stage systems under the centralized control organization mechanism. Computers & Industrial Engineering 110:404–12. ‏ doi:10.1016/j.cie.2017.06.005.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.