Evaluation efficiency of large-scale data set with negative data: an artificial neural network approach
- 265 Downloads
Data envelopment analysis (DEA) is the most widely used methods for measuring the efficiency and productivity of decision-making units (DMUs). The need for huge computer resources in terms of memory and CPU time in DEA is inevitable for a large-scale data set, especially with negative measures. In recent years, wide ranges of studies have been conducted in the area of artificial neural network and DEA combined methods. In this study, a supervised feed-forward neural network is proposed to evaluate the efficiency and productivity of large-scale data sets with negative values in contrast to the corresponding DEA method. Results indicate that the proposed network has some computational advantages over the corresponding DEA models; therefore, it can be considered as a useful tool for measuring the efficiency of DMUs with (large-scale) negative data.
KeywordsArtificial neural network Data envelopment analysis (DEA) SORM-DEA Negative data Levenberg–Marquardt (LM) LM-DEA
The constructive comments and suggestions of the referees and Editor-in-Chief Professor Hamid R. Arabnia are highly appreciated. The research was supported by the Czech Science Foundation (GACR project 14-31593S), through European Social Fund within the project CZ.1.07/2.3.00/20.0296 and SP2014/111, an SGS project of Faculty of Economics, VŠB-Technical University of Ostrava.
- 2.Athanassopoulos AD, Curram SP (1996) A comparison of data envelopment analysis and artificial neural networks as tools for assessing. JORS 47(8):100Google Scholar
- 6.Cooper WW, Seiford LM, Tone K (2008) Introduction to data envelopment analysis and its uses, 3rd edn. Springer, BerlinGoogle Scholar
- 11.Flammini F, Setola R, Franceschetti G (2013) Effective surveillance for homeland security balancing technology and social issues. Chapman and Hall/CRC, Boca RatonGoogle Scholar
- 12.Freeman JA, Skapura DM (1992) Neural network algorithms, applications, and programming techniques. Addison-Wesley, New YorkGoogle Scholar
- 21.Pastor JT (1994) How to discount environmental effects in DEA: an application to bank branches. Working paper no. 011/94, Depto. De Estadistica e Investigacion Operativa, Universidad de Alicante, SpainGoogle Scholar
- 24.Press WH, Flannery BP, Teukolsky SA, Vetterling WT (1990) Numerical Recipes, Cambridge University Press, New YorkGoogle Scholar
- 25.Principe JC, Euliano NR (2000) Neural and adaptive systems: fundamentals through simulations. Wiley, New YorkGoogle Scholar
- 28.The MathWorks, MATLAB [Online]. http://www.mathworks.com