A frontier-based neural network for assessing the efficiency of activity units

• Autorzy: Curram S. P. , Athanassopoulos A. D. , Shale E. A.
• Języki publikacji: EN

Abstrakty

EN

This paper uses neural networks to assess the relative efficiency of activity units. A new formulation is proposed whereby the neural network fit is obtained by minimising one-sided errors which are attributed to technical inefficiency, effectively modelling a frontier. Efficiency ratings are given to rank activity units on a (0, 100] scale. Experiments on artificial data are employed to test the proposed method for its accuracy. These results are also compared with the corresponding efficiency ratings obtained from data envelopment analysis. Factors relevant to methodology selection for implementation are discussed. The empirical results of the paper support the potential role of neural networks as a reliable method of frontier efficiency assessment

Słowa kluczowe

EN

network; data envelopment analysis; performance assessment

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2009
• Tom: Vol. 34, No. 1
• Strony: 23--39
• Opis fizyczny: Bibliogr. 18 poz.

Twórcy

autor

Curram S. P.

autor

Athanassopoulos A. D.

autor

Shale E. A.

• HVR Consulting Services Limited, Selborne House, Mill Lane, Alton GU34 2QJ, UK,

Bibliografia

• [1]Aigner D., Lovell C.A.K., Schmidt P., Formulation and estimation of stochastic frontier production function models, Journal of Econometrics, 6, 1977 21-37.
• [2]Athanassopoulos A.D., Curram S.P., A comparison of data envelopment analysis and artificial neural networks as tools for assessing the efficiency of decision making units, Journal of the Operational Research Society, 47, 1996, 1000-1016.
• [3]Banker R., Charnes A., Cooper W., Maindiratta A., A comparison of data envelopment analysis and translog estimates of production frontiers using simulated observations from a known technology, in:A Dogramaci, R Fare (eds.), Applications of Modern Production Theory: Efficiency and Productivity, Kluwer Academic Publishers, Boston, MA, 1987, 33-55.
• [4]Banker R., Gadh V., Gorr W., A Monte Carlo comparison of two production frontier estimation methods: Corrected ordinary least squares and data envelopment analysis, European Journal of Operational Research, 67, 1993, 332-343.
• [5]Berger A.N., Humphrey D.B., The dominance of inefficiencies over scale and product mix economies in banking, Journal of Monetary Economics, 28, 1991, 117-148.
• [6]Bowlin W., Charnes A., Cooper W., Sherman. H.,. Data envelopment analysis and regression approaches to efficiency estimation and evaluation. Annals of Operations Research 2, 1985, 113-138.
• [7]Charnes A., Cooper W.W., Rhodes E.,. Measuring the efficiency of decision making units, European Journal of Operational Research, 2, 1978, 429-444.
• [8]Charnes A., Cooper W., Lewin A.Y., Seiford L.M., Data Envelopment Analysis:Theory, Methodology and Applications, Kluwer Academic Publishers, Massachusetts, 1994
• [9]Cooper W.W., Seiford L.M., Tone K., Data Envelopment Analysis: a comprehensive text with models, applications, references and DEA-Solver software, Kluwer Academic Publishers, Massachusetts, 2000.
• [10]Fried H., Lovell K., Schmidt S (eds.), The Measurement of Productive Efficiency:Techniques and Applications, Oxford University Press, New York, 1993.
• [11]Gong B.H., Sickles R., Finite sample evidence on the performance of stochastic frontiers and data envelopment analysis using panel data, Journal of Econometrics, 51 1992, 259-284.
• [12]Hoptroff R.G., The principles and practice of time series forecasting and business modelling using neural nets, Neural Computing & Applications, 1, 1993, 59-66.
• [13]Rumelhart D.E., Hinton G.E., Williams R.J., Learning internal representations by error propagation, Rumelhart, D.E., McClelland, J.L. and the PDP Research Group, Parallel Distributed Processing Vol 1, MIT Press, Cambridge, MA, 1986, 318-362.
• [14]Smith M., 1993. Neural Networks for Statistical Modeling, Van Nostrand Reinhold, New York.
• [15]Smith P., Model misspecification in data envelopment analysis, Annals of Operations Research, 73, 1997, 233-252.
• [16]Swingler K., Applying Neural Networks: A Practical Guide, Academic Press, London, 1996.
• [17]Thanassoulis E., A comparison of regression analysis and data envelopment analysis as alternative methods for assessing performance, Journal of the Operational Research Society, 44, 1993, 1129-1145.
• [18]Wang S., Adaptive non-parametric efficiency analysis: a neural-network-based approach, Computers and Operations Research, 30, 2003, 279-295.