Abstract
Data Envelopment Analysis (DEA) methods have been widely used in many fields, including operations research, optimization, operations management, industrial engineering, accounting, management, and economics. This chapter starts with an introduction to common DEA-based models in the envelopment and multiplier forms to illustrate the importance of these models. Then, we provide details of the recent theoretical developments including Network DEA, Stochastic DEA, Fuzzy DEA, Bootstrapping, Directional measures, desirable (good) and undesirable (bad) factors, and Directional returns to scale. This is followed by the presentation of some novel applications of DEA to provide direction for future developments in this field. In summary, this chapter aims to discuss some of the latest developments in DEA and provide direction for future research.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Di Giorgio et al. (2016). The potential to expand antiretroviral therapy by improving health facility efficiency: evidence from Kenya, Uganda, and Zambia, BMC Medicine 14, 108. DOI 10.1186/s12916-016-0653-z.
References
Al-Mezeini, N.K., Oukil, A., Al-Ismaili, A.M. (2020). Investigating the efficiency of greenhouse production in Oman: A two-stage approach based on data envelopment analysis and double bootstrapping. Journal of Cleaner Production, 247, 119160.
Asmild, M., Pastor, J.T. (2010). Slack free MEA and RDM with comprehensive efficiency measures. Omega, 38(6), 475–483.
Banker, R.D. (1984). Estimating most productive scale size using data envelopment analysis. Journal of Operational Research, 17, 35–44.
Banker, R.D., Charnes, A., Cooper, W.W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30, 1078–1092.
Charnes, A., Cooper, W.W., Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.
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), 297–304.
Emrouznejad, A. and Tavana, M. (2014). Performance measurement with Fuzzy Data envelopment analysis. In the series of “Studies in Fuzziness and Soft Computing", Springer-Verlag, ISBN 978-3-642-41371-1.
Färe, R., Grosskopf. S. (1985). A nonparametric cost approach to scale efficiency. Scandinavian Journal of Economics, 87, 594–604.
Färe, R., Grosskopf. S. (2000). Network DEA, Socio-Economic Planning Sciences, 3, 249–267.
Färe, R., Grosskopf. S., Lovell, C.A.K. (1994). Production frontiers. Cambridge, UK: Cambridge University Press.
Guo, C., Wei, F., Ding, T., Zhang, L., Liang, L. (2017). Multistage network DEA: Decomposition and aggregation weights of component performance. Computers & Industrial Engineering, 113, 64–74.
Guo, C., Zhang, J., Zhang, L. (2020). Two-stage additive network DEA: Duality, frontier projection and divisional efficiency. Expert Systems with Applications, 157, 113478.
Izadikhah, M., Saen, F.R. (2018). Assessing sustainability of supply chain by chance-constrained two-stage DEA model in the presence of undesirable factors. Computers and Operations Research, 100, 343–367.
Khodadadipour, M., Hadi-Vencheh, A., Behzadi, M.H., Rostamy-Malekhalifeh, M. (2021). Undesirable factors in stochastic DEA cross efficiency evaluation: An application to thermal power plant energy efficiency. Economic Analysis and Policy, 69, 613–628.
Li, Y. (2020). Analyzing efficiencies of city commercial banks in China: An application of the bootstrapped DEA approach. Pacific-Basin Finance Journal, 62, 101372.
Li, Y., Liu, J., Ang, S., Yang, f. (2021). Performance evaluation of two-stage network structures with fixed-sum outputs: An application to the 2018 winter Olympic Games. Omega, 102, 102342.
Lin, R., Chen, Z. (2017). A directional distance based super-efficiency DEA model handling negative data. Journal of the operational Research Society, 68, 1312–1322.
Moradi-Motlagh, A., Emrouznejad, A. (2022). The origins and development of statistical approaches in non-parametric frontier models: A survey of the first two decades of scholarly literature (1998–2020). Annals of Operations Research, https://doi.org/10.1007/s10479-022-04659-7.
Olesen, O.B., Petersen, N.C. (2016). Stochastic Data Envelopment Analysis-A review. European Journal of Operational Research, 251(1), 2–21.
Oukil, A., Channouf, N., Al-Zaidi, A. (2016). Performance evaluation of the hotel industry in an emerging tourism destination: The case of Oman. Journal of Hospitality and Tourism Management, 29, 60–68.
Panzar, J.C., Willig, R.D. (1977). Economies of scale in multi-output production. Quarterly Journal of Economics, 91(3), 481–493.
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, 113938.
Peykani, P., Mohammadi, E., Emrouznejad, A., Pishvaee, M.S., Rostami-Malkhalifeh, M. (2019). Fuzzy data envelopment analysis: An adjustable approach. Expert Systems with Applications, 136, 439–452.
Podinovski, V. V. (2004). 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, 265–276.
Podinovski, V. V. (2009). Production technologies based on combined proportionality assumptions. Journal of Productivity Analysis, 32, 21–26.
Podinovski, V. V., Ismail, I., Bouzdine-Chameeva, T., Wenjuan Zhang, W.J. (2014). Combining the assumptions of variable and constant returns to scale in the efficiency evaluation of secondary schools. European Journal of Operational Research, 239, 504–513.
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), 1111–1121.
Seitz, W.D. (1970). The measurement of efficiency relative to a frontier production function. American Journal of Agricultural Economics, 52, 505–511.
Simar, L., Wilson, P.W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44(1), 49–61.
Simar, L., Wilson, P.W. (2000). Statistical inference in nonparametric frontier models: The state of the art. Journal of Productivity Analysis, 13, 49–78.
Simar, L., Wilson, P.W. (2007). Estimation and inference in two-stage, semi-para-metric models of production processes. Journal of Economics, 136(1), 31–64.
Singh, S. (2018). Intuitionistic fuzzy DEA/AR and its application to flexible manufacturing systems. RAIRO—Operations Research, 52(1), 241–257.
Sueyoshi, T. (1999). DEA duality on returns to scale (RTS) in production and cost analyses: An occurrence of multiple solutions and differences between production-based and cost-based RTS estimates. Management Science, 45, 1593–1608.
Tavana, M., Izadikhah, M., Toloo, M., Roostaee, R. (2021). A new non-radial directional distance model for data envelopment analysis problems with negative and flexible measures. Omega, 102, 102355.
Tavassoli, M., Farzipoor Saen, R., Mohamadi Zanjirani, D. (2020). Assessing sustainability of suppliers: A novel stochastic-fuzzy DEA model. Sustainable Production and Consumption, 21, 78–91.
Wang, Q., Wu, Z., Chen, X. (2019). Decomposition weights and overall efficiency in a two-stage DEA model with shared resources. Computers & Industrial Engineering, 136, 135–148.
Yang, G.L., Rousseau, R., Yang, L.Y., Liu, W.B. (2014). A Study on directional returns to scale. Journal of Informetrics, 8, 628–641.
Yang, G.L. (2012). On relative efficiencies and directional returns to scale for research institutions. Ph.D thesis. University of Chinese Academy of Sciences, Beijing (in Chinese).
Yang, G.L., Liu, W.B. (2017). Estimating directional returns to scale in DEA. INFOR: Information Systems and Operational Research, 55(3), 243–273.
Yang, G.L., Ren, Khoveyni, M., Eslami, R. (2020). Directional congestion in the framework of data envelopment analysis. Journal of Management Science and Engineering, 5(1), 57–75.
Zhou, Y., Liu, W., Lv, X., Chen, X., Shen, M. (2019). Investigating interior driving factors and cross-industrial linkages of carbon emission efficiency in China’s construction industry: Based on Super-SBM DEA and GVAR model. Journal of Cleaner Production, 241, 118322.
Zhou, Z., Sun, W., Xiao, H., Jin, Q., Liu, W. (2021). Stochastic leader-follower DEA models for two-stage systems. Journal of Management Science and Engineering, in press. https://doi.org/10.1016/j.jmse.2021.02.004.
Acknowledgements
We would like to acknowledge the support from the National Natural Science Foundation of China (NSFC, No. 72071196).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Emrouznejad, A., Yang, Gl., Khoveyni, M., Michali, M. (2022). Data Envelopment Analysis: Recent Developments and Challenges. In: Salhi, S., Boylan, J. (eds) The Palgrave Handbook of Operations Research . Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-96935-6_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-96935-6_10
Published:
Publisher Name: Palgrave Macmillan, Cham
Print ISBN: 978-3-030-96934-9
Online ISBN: 978-3-030-96935-6
eBook Packages: Business and ManagementBusiness and Management (R0)