Abstract
Using data envelopment analysis as a mathematical performance evaluation tool is much more serious for researchers and practitioners. Different data envelopment analysis models are now introduced in different fields. In addition to the classic performance evaluation models in data envelopment analysis, developed models such as super-efficiency, returns to scale, progress and regress models, and so on have been introduced in this technique that help different aspects of analytics and decision making units in performance evaluation. In this chapter, such developed DEA models are formulated, and then the corresponding R codes for these models are provided.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anderson, P., Peterson, N.C.: A procedure for ranking efficient units in data envelopment analysis. Manag. Sci. 39(10), 1261–1264 (1993)
Mehrabian, S., Alirezaee, M.R., Jahanshahloo, G.R.: A complete efficiency ranking of decision making units in data envelopment analysis. Comput. Optim. Appl. 14, 261–266 (1999)
Jahanshahloo, G.R., Hosseinzadeh Lotfi, F., Shoja, N., Tohidi, G., Razavyan, S.: Ranking using L1-norm in data envelopment analysis. Appl. Math. Comput. 153, 215–224 (2004)
Banker, R.D., Cooper, W.W., Seiford, L.M., Thrall, R.M., Zhu, J.: Returns to scale in different DEA models. Eur. J. Oper. Res. 154, 345–362 (2004)
Camanho, A.S., Dyson, R.G.: Cost efficiency, production and value-added models in the analysis of bank branch performance. JORS 56(5), 483–494 (2005)
Wang, C.G., Fare, R., Seavert, C.F.: Revenue capacity efficiency of pear trees and its decomposition. J. Am. Soc. Hortic. Sci. 131(1), 32–40 (2006)
Caves, D.W., Christensen, L.R., Diewert, W.E.: The economic theory of index numbers and the measurement of input, output and productivity. Econometrica 50, 1393–1414 (1982)
Tone, K.: A slacks-based measure of efficiency in data envelopment analysis. Eur. J. Oper. Res. 130, 498–509 (2001)
Färe, R., Grosskopf, S.: Network DEA. Socio-Econ. Plan. Sci. 34, 35–49 (2000)
Färe, R., Grosskopf, S.: Profit efficiency, Farrell decompositions and the Mahler inequality. Econ. Lett. 57, 283–287 (1997)
Koltai, T., Uzonyi-Kecskés, J.: The comparison of data envelopment analysis (DEA) and financial analysis results in a production simulation game. Acta Polytech. Hung. 14(4), 167–185 (2017)
Cooper, W.W., Thompson, R.G., Thrall, R.M.: Introduction: extensions and new developments in DEA. Ann. Oper. Res. 66, 3–45 (1996)
Hosseinzadeh Lotfi, F., Jahanshahloo, G.R., Memariani, A.: A method for finding common set of weights by multiple objective programming in data envelopment analysis. S. W. J. Pure Appl. Math. 1, 44–54 (2000)
Chambers, R.G., Chung, Y., Färe, R.: Benefit and distance functions. J. Econ. Theory 70, 407–419 (1996)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Hosseinzadeh Lotfi, F., Ebrahimnejad, A., Vaez-Ghasemi, M., Moghaddas, Z. (2020). Advanced DEA Models with R Codes. In: Data Envelopment Analysis with R. Studies in Fuzziness and Soft Computing, vol 386. Springer, Cham. https://doi.org/10.1007/978-3-030-24277-0_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-24277-0_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24276-3
Online ISBN: 978-3-030-24277-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)