Abstract
Data envelopment analysis (DEA) is a data based mathematical approach, which handles large numbers of variables, constraints, and data. Hence, data play an important and critical role in DEA. Given a set of decision making units (DMUs) and identified inputs and outputs (performance measures), DEA evaluates each DMU in comparison with all DMUs. According to some statistical and empirical rules, a balance between the number of DMUs and the number of performance measures should exist. However, in some situations the number of performance measures is relatively large in comparison with the number of DMUs. These cases lead us to choose some inputs and outputs in a way that produces acceptable results. We refer to these selected inputs and outputs as selective measures. This paper presents an approach toward a large number of inputs and outputs. Individual DMU and aggregate models are recommended and expanded separately for developing the idea of selective measures. The practical aspect of the new approach is illustrated by two real data set applications.
Similar content being viewed by others
References
Amin, G. R., & Toloo, M. (2007). Finding the most efficient DMUs in DEA: An improved integrated model. Computers & Industrial Engineering, 52, 71–77.
Amirteimoori, A., Emrouznejad, A., & Khoshandam, L. (2013). Classifying flexible measures in data envelopment analysis: A slacks-based measure. Measurement, 46(10), 4100–4107.
Asmild, M., Hougaard, J. L., & Kronborg, D. (2013). Do efficiency scores depend on input mix? A statistical test and empirical illustration. Annals of Operations Research, 211, 37–48.
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Models for estimation of technical and scale inefficiencies in data envelopment analysis. Management Science, 30, 1078–1092.
Chao, C. M., Yu, M. M., & Chen, M. C. (2010). Measuring the performance of financial holding companies. The Service Industries Journal, 30, 811–829.
Charnes, A., & Cooper, W. W. (1962). Programming with linear fractional functional. Naval Research Logistics Quarterly, 9, 181–186.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444.
Chen, Y. (2004). Ranking efficient units in DEA. Omega, 32, 213–219.
Chen, Y., Dub, J., Sherman, H. D., & Zhu, J. (2010). DEA model with shared resources and efficiency decomposition. European Journal of Operational Research, 207, 339–349.
Cook, W. D., & Kress, M. (1990). A data envelopment analysis for aggregating preference rankings. Management Science, 36, 1302–1310.
Cook, W. D., & Zhu, J. (2006). Rank order data in DEA: A general framework. European Journal of Operational Research, 174, 1021–1038.
Cook, W. D., & Zhu, J. (2007). Classifying inputs and outputs in data envelopment analysis. European Journal of Operational Research, 180, 692–699.
Cook, W. D., & Zhu, J. (2010). Context-dependent performance standard in DEA. Annals of Operations Research, 173, 163–175.
Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-solver software (2nd ed.). Berlin: Springer.
Du, J., Wang, J., Chen, Y., Chou, S. Y., & Zhu, J. (2011). Incorporating health outcomes in Pennsylvania hospital efficiency: An additive super-efficiency DEA approach. Annals of Operations Research. doi:10.1007/s10479-011-0838-y.
Dua, J., Liang, L., Chen, Y., & Bi, G. (2010). DEA-based production planning. Omega, 38, 105–112.
Emrouznejad, A., & Anouze, A. L. (2009). A note on the modeling the efficiency of top Arab banks. Expert Systems with Applications, 36, 5741–5744.
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, 297–304.
Flokou, A., Kontodimopoulos, N., & Niakas, D. (2011). Employing post-DEA cross-evaluation and cluster analysis in a sample of Greek NHS hospital. Journal of Medical System, 35, 1001–1014.
Forsund, F. R., & Hjalamarsson, L. (2004). Are all scales optimal in DEA? Theory and empirical evidence. Journal of Productivity Analysis, 21, 25–48.
de França, J. M. F., de Figueiredo, J. N., & Lapa, J. S. (2010). A DEA methodology to evaluate the impact of information asymmetry on the efficiency of not-for-profit organizations with an application to higher education in Brazil. Annals of Operations Research, 173, 39–56.
Geymueller, P. V. (2009). Static versus dynamic DEA in electricity regulation: The case of US transmission system operators. Central European Journal of Operations Research, 17, 397–413.
Lee, H. S., & Zhu, J. (2012). Super-efficiency infeasibility and zero data in DEA. European Journal of Operational Research, 216, 429–433.
Liang, L., Wu, J., Cook, W. D., & Zhu, J. (2008). Alternative secondary goals in DEA cross-efficiency evaluation. International Journal of Production Economics, 113, 1025–1030.
Lin, H. T. (2009). Efficiency measurement and ranking of the tutorial system using IDEA. Expert Systems with Applications, 36, 11233–11239.
Liu, S. T. (2009). Slacks-based efficiency measures for predicting bank performance. Expert Systems with Applications, 36, 2813–2818.
Lozano, S., & Villa, G. (2004). Centralized resource allocation using data envelopment analysis. Journal of Productivity Analysis, 22, 143–161.
Lozano, S., Iribarren, D., Moreira, M. T., & Feijoo, G. (2009). The link between operational efficiency and environmental impacts: A joint application of Life Cycle Assessment and Data Envelopment Analysis. Science of the Total Environment, 407, 1744–1754.
Moreno, P., & Lozano, S. (2012). A network DEA assessment of team efficiency in the NBA. Annals of Operations Research. doi:10.1007/s10479-012-1074-9.
Mostafa, M. M. (2009). Modeling the efficiency of top Arab banks: A DEA-neural network approach. Expert Systems with Applications, 36, 309–320.
Nemoto, J., & Goto, M. (2003). Measurement of dynamic efficiency in production: An application of data envelopment analysis to Japanese electric utilities. Journal of Productivity Analysis, 19, 191–210.
Ozcan, Y. A., Lins, M. E., Stella, M., Lobo, C., da Silva, A. C. M., Fiszman, R., et al. (2010). Evaluating the performance of Brazilian university hospitals. Annals of Operations Research, 178, 247–261.
Paradi, J. C., & Tam, F. K. (2012). The examination of pseudo-allocative and pseudo-overall efficiencies in DEA using shadow prices. Journal of Productivity Analysis, 37, 115–123.
Portela, M. C. A. S., Borges, P. C., & Thanassoulis, E. (2003). Finding closest targets in non-oriented DEA models: The case of convex and non-convex technologies. Journal of Productivity Analysis, 19, 251–269.
Prior, D. (2006). Efficiency and total quality management in health care organizations: A dynamic frontier approach. Annals of Operations Research, 145, 281–299.
Ramon, N., Ruiz, J. L., & Sirvent, I. (2011). Reducing differences between profiles of weights: A “peer-restricted” cross-efficiency evaluation. Omega, 39, 634–641.
Renner, A., Kirigia, J. M., Zere, E. A., Barry, S. P., Kirigia, D. G., Kamara, C., et al. (2005). Technical efficiency of peripheral health units in Pujehun district of Sierra Leone: a DEA application. BMC Health Services Research. doi:10.1186/1472-6963-5-7.
Sarkis, J. (2000). A comparative analysis of DEA as a discrete alternative multiple criteria decision tool. European Journal of Operational Research, 123, 543–557.
Seiford, L. M., & Zhu, J. (2003). Context-dependent data envelopment analysis—measuring attractiveness and progress. Omega, 31, 397–408.
Sueyoshi, T., & Goto, M. (2009). Methodological comparison between DEA (data envelopment analysis) and DEA–DA (discriminate analysis) from the perspective of bankruptcy assessment. European Journal of Operational Research, 199, 561–575.
Suzuki, S., Nijkamp, P., Rietveld, P., & Pels, E. (2010). A distance friction minimization approach in data envelopment analysis: A comparative study on airport efficiency. European Journal of Operational Research, 207, 1107–1115.
Toloo, M., Masoumzadeh, A., & Barat, M. (2014). Finding an initial basic feasible solution for DEA models with an application on bank industry. Computational Economics. doi:10.1007/s10614-014-9423-1.
Toloo, M., Sohrabi, B., & Nalchigar, S. (2009). A new method for ranking discovered rules from data mining by DEA. Expert Systems with Applications, 36, 8503–8508.
Toloo, M., & Nalchigar, S. (2009). A new integrated DEA model for finding most BCC-efficient DMU. Applied Mathematical Modelling, 33, 597–604.
Toloo, M. (2009). On classifying inputs and outputs in DEA: A revised model. European Journal of Operational Research, 198, 358–360.
Toloo, M. (2012). Alternative solutions for classifying inputs and outputs in data envelopment analysis. Computers and Mathematics with Applications, 63, 1104–1110.
Toloo, M. (2013). The most efficient unit without explicit inputs: An extended MILP-DEA model. Measurement, 46, 3628–3634.
Toloo, M. (2014). An epsilon-free approach for finding the most efficient unit in DEA. Applied Mathematical Modelling, 38(13), 3182–3192.
Toloo, M., & Ertay, T. (2014). The most cost efficient automotive vendor with price uncertainty: A new DEA approach. Measurement, 52, 135–144.
Toloo, M., & Kraska, A. (2014). Finding the best asset financing alternative: A DEA-WEO approach. Measurement, 55, 288–294.
Ulucan, A., & Bar, K. (2010). Efficiency evaluations with context-dependent and measure-specific data envelopment: An application in a World Bank supported project. Omega, 38, 68–83.
Wang, Y. M., & Chin, K. S. (2010). Some alternative models for DEA cross-efficiency evaluation. International Journal of Production Economics, 128, 332–338.
Wu, J., Liang, L., & Chen, Y. (2009). DEA game cross-efficiency approach to Olympic rankings. Omega, 37, 909–918.
Yang, C. C. (2011). An enhanced DEA model for decomposition of technical efficiency in banking. Annals of Operations Research. doi:10.1007/s10479-011-0926-z.
Zhu, J. (2003). Imprecise data envelopment analysis (IDEA): A review and improvement with an application. European Journal of Operational Research, 144, 513–529.
Acknowledgments
The research was supported by the Czech Science Foundation (GACR project 14-31593S) and through European Social Fund within the project CZ.1.07/2.3.00/20.0296.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Toloo, M., Barat, M. & Masoumzadeh, A. Selective measures in data envelopment analysis. Ann Oper Res 226, 623–642 (2015). https://doi.org/10.1007/s10479-014-1714-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10479-014-1714-3