An integrated artificial neural network and fuzzy clustering algorithm for performance assessment of decision making units

Applied Mathematics and Computation

Volumn 187, Issue 2, 2007, Pages 584-599

  Azadeh, A ^a   Ghaderi, S F ^a   Anvari, M ^a   Saberi, M ^b   Izadbakhsh, H ^a  

^a Department of Engineering Optimization Research ^*  (Iran)

^b BU ALI SINA UNIVERSITY   (Iran)

Author keywords

Fuzzy clustering; Neural networks; PCA; Performance assessment; Steam power plants

Indexed keywords

ALGORITHMS; FUZZY CLUSTERING; INDUSTRIAL ENGINEERING; NEURAL NETWORKS; PARAMETER ESTIMATION; PRINCIPAL COMPONENT ANALYSIS; STEAM POWER PLANTS;

EFFICIENCY SCORES; FRONTIER ANALYSIS; FUZZY CLUSTERING ALGORITHMS; PERFORMANCE ASSESSMENT;

DECISION MAKING;

EID: 34247465971     PISSN: 00963003     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.amc.2006.08.092     Document Type: Article

References (45)

1. Golany B., Yaakov R., and Rybak D. Measuring efficiency of power plants in Israel by envelopment analysis. IEEE Transactions on Engineering Management 41 3 (1994) 291-301
2. Jamasb T., and Pollitt M. Benchmarking and Regulation of Electricity Distribution and Transmission Utilities: Lessons from International Experience, in DAE Working Paper (2001), Department of Applied Economics, Univ. Cambridge, Cambridge, UK
3. Sanhueza R., Rudnick H., and Lagunas H. DEA efficiency for the determination of the electric power distribution added value. IEEE Transactions on Power Systems 19 2 (2004)
4. Pollit M.G. Ownership and Performance in Electric Utilities (1995), Oxford Univ. Press, Oxford, UK
5. Lam P., and Shiu A. A data envelopment analysis of the efficiency of China's thermal power generation. Utilities Policy 10 (2001) 75-83
6. Olatfubi W.O., and Dismukes D.E. A data envelopment analysis of the level and determinants of coal-fired electric power generation performance. Utilities Policy 9 (2000) 47-59
7. Park S., and Lesourd J. The efficiency of conventional fuel power plants in South Korea: a comparison of parametric and non-parametric approaches. International Journal of production economics 63 (2000) 59-67
8. A. Azadeh, S.F. Ghaderi, M. Anvari, S. Dehghan, H. Izadbakhsh, Performance assessment and optimization of thermal power plants by DEA-BCC and multivariate analysis, in: Proceeding of the 11th International Energy Conference and Exhibition (Energex 2006), Stavanger-Norway, 2006.
9. Coelli T.J. Recent developments in frontier modeling and efficiency measurement. Australian Journal of Agricultural Economics 39 3 (1995) 219-245
10. Knittel C.R. Alternative regulatory methods and firm efficiency: stochastic frontier evidence from the US electricity industry. Review of Economics and Statistics 84 3 (2002) 530-540
11. Sueyoshi T., and Goto M. Slack-adjusted DEA for time series analysis: performance measurement of Japanese electric power generation industry in 1984-1993. European Journal of Operation Research 133 (2001) 232-259
12. Goto M., and Tsutsui M. Comparison of Productive and Cost Efficiencies among Japanese and US Electric Utilities (1997), Central Research Institute of Electric Power Industry, Tokyo, Japan
13. Cook W.D., and Green R.H. Evaluating power plant efficiency: a hierarchical model. Computers and Operations Research 32 4 (2005) 813-823
14. Yunos J.M., and Hawdon D. The efficiency of the national electricity board in Malaysia: an inter county comparison using DEA. Energy Economics 19 (1997) 255-269
15. Geman S., Bienenstock E., and Doursat R. Neural networks and the bias/variance dilemma. Neural Computation 4 (1992) 1-58
16. Nielsen ai F. Neural Networks - Algorisms and Application (2001), Niles Brock Business College
17. A. Azadeh, S. F. Ghaderi, M. Anvari, M. Saberi, Measuring performance electric power generations using artificial neural networks and fuzzy clustering, in: Proceeding of the 2006 IEEE International Conference on Industrial Electronics - IECON'06 (Paris), 2006.
18. Bauer P.W. Recent developments in the econometric estimation of frontiers. Journal of Econometrics 46 1-2 (1990) 39-56
19. Wang S. Adaptive non-parametric efficiency frontier analysis: a neural-network-based model. Computers and Operations Research 30 (2003) 279-295
20. Athanassopoulos A.D., and Curram S.P. A comparison of data envelopment analysis and artificial neural networks as tool for assessing the efficiency of decision making units. Journal of the Operational Research Society 47 8 (1996) 1000-1016
21. Costa A., and Markellos R.N. Evaluating public transport efficiency with neural network models. Transportation Research C 5 5 (1997) 301-312
22. D. Santi{dotless}n, A. Valin, Artificial Neural Networks for Measuring Technical Efficiency in Schools with a Two-Level Model: an Alternative Approach, II Oviedo Workshop on Efficiency and Productivity, Oviedo, 2000.
23. Fleissig A.R., Kastens T., and Terrell D. Evaluating the semi-nonparametric Fourier, AIM, and neural networks cost functions. Economics Letters 68 3 (2000) 235-244
24. Pendharkar P.C., and Rodger J.A. Technical efficiency-based selection of learning cases to improve forecasting accuracy of neural networks under monotonicity assumption. Decision Support Systems 36 1 (2003) 117-136
25. Santin D., Delgado F.J., and Valino A. The measurement of technical efficiency: a neural network approach. Applied Economics 36 (2004) 627-635
26. Wu D., Yang Z., and Liang L. Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank. Expert Systems with Applications 31 (2006) 108-115
27. Cheng B., and Titterington D.M. Neural networks: a review from a statistical perspective. Statistical Science 9 1 (1994) 2-54
28. Hornik K., Stinchcombe M., and White H. Multilayer feed forward networks are universal approximators. Neural Networks 2 (1989) 359-366
29. White H. Some asymptotic results for learning in single hidden-layer feed forward network models. Journal of the American Statistical Association 84 408 (1989) 1003-1013
30. P.I. Werbos, Beyond Regression: New Tools for Prediction and Analysis in the Behavior Sciences, Ph.D. Thesis, Harvard University, Cambridge, MA, 1974.
31. In: Rumelhart D.E., and McClelland J.L. (Eds). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Foundations (1986), MIT Press, Cambridge, MA
32. W. Schiffmann, M. Joost, R. Werner, Optimization of The Back Propagation Algorithm for Training Multilayer Perceptrons, Technical Report 16/92, Institute of Physics, University of Koblenz, 1992.
33. Dunn J.C. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics 3 (1973) 32-57
34. Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithms (1981), Plenum Press, New York
35. Windham M.P. Cluster validity for fuzzy clustering algorithms. Fuzzy Sets and Systems 5 (1981) 177-185
36. Murat Y.S., and Ceylan H. Use of artificial neural networks for transport energy demand modeling. Energy Policy 34 17 (2006) 3165-3172
37. Delgado F.J. Measuring efficiency with neural networks: an application to the public sector. Economics Bulletin 3 15 (2005) 1-10
38. A. Emami Meibodi, Efficiency Considerations in the Electricity Supply Industry: The Case of Iran, Ph.D. thesis, University of Surrey, 1998.
39. Hawdon D. Improving The Performance of Electricity Industries in Developing Countries: Is World Bank Policy on Deregulation The Way Forward. Economics Departmental Research Seminar (1997), University of Surrey
40. Fare R., Grosskopf S., and Lovell C.A.K. The Measurement of Efficiency of Production (1983), Kluwer, Boston
41. TAVANIR management organization, Electric Power Industry in Iran 1997-2004, TAVANIR management organization, Iran, 1997-2004.
42. Azadeh A., and Ebrahimipour V. An integrated approach for assessment and ranking of manufacturing systems based on machine performance. International Journal of Industrial Engineering 11 4 (2004)
43. A. Azadeh, V. Ebrahimipour, G.H. Ataei, A Total Machine Productivity Model for Assessment and Improvement of Electrical Manufacturing Systems. In the Proceeding of the 33rd International Conference on Computers and Industrial Engineering, Ireland (2003).
44. A. Azadeh, V. Ebrahimipour, An integrated approach for assessment of manufacturing sectors based on machine performance: the cases of automotive and food and beverages industries, In Proceeding of the Second International Conference on Manufacturing on Complexity University of Cambridge, UK (2002).
45. Azadeh A., and Jalal S. Identifying the economic importance of industrial sectors by multivariate analysis. Journal of Faculty of Engineering (University of Tehran, Iran) 35 3 (2001) 437-449