Designing a New Combined Model Based on Data Envelopment Analysis, Artificial Neural Network, Genetic Algorithm and Particle Swarm Optimization to Evaluate Efficiency and Benchmarking of Efficient and Inefficient Units

Document Type : Original Article


1 Ph.D. Candidate, Central Tehran Branch, Islamic Azad University.

2 Associate Prof., Central Tehran Branch, Islamic Azad University.

3 Prof., Islamic Azad University, Tehran, Iran.


The main purpose of the present study is to design a new hybrid model based on data envelopment analysis, artificial neural network, genetic algorithm and particle swarm optimization to evaluate the efficiency and benchmarking of efficient and inefficient units. A two stage process has been used to evaluate the relative efficiency of 16 Tavanir regional power companies, using the combined model of data envelopment analysis with the neural network optimized by genetic algorithm and using a hybrid model of particle swarm optimization with genetic algorithm Benchmarking for efficient and inefficient units has been addressed. The average efficiency of regional power companies during the years 1391 to 1396 has increased from 0.8934 to 0.91477, while the regional power companies of Azerbaijan, Isfahan, Tehran, Khorasan, Semnan, Kerman, Gilan and Yazd have always had the highest average efficiency, And West and Fars regional electric companies with average values of 0.70747 and 0.60525 have the lowest efficiency during the years 1391 to 1396.


  1. Abd-El-Wahed, W., Mousa, A., & El-Shorbagy, M. (2011). Integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems. Journal of Computational and Applied Mathematics, 235(5), 1446-1453.
  2. Ajaly, Mehdi and Safari, Hossein. (2011). Performance Evaluation of Decision Making Units Using Hybrid Model of Predictive Neural Networks and Data Envelopment Analysis (Case Study: National Iranian Gas Company). Journal of Industrial Engineering, 45 (1), 13-29 (in Persian).
  3. Alborzi, Mahmoud. (2014). Genetic Algorithm. Sharif University of Technology Scientific Publishing Institute: Tehran (in Persian).
  4. Alborzi, Mahmoud. (2014). Neural Networks - Translated by R. Bill and T. Jackson. Sharif University of Technology Publications Institute: Tehran (in Persian).
  5. Angeline, P. J. (1998). Using selection to improve particle swarm optimization. Paper presented at the Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on.
  6. Athanassopoulos, A. D., & Curram, S. P. (1996). 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 Society47(8), 1000-1016.
  7. Azar, A., Daneshvar, M., Khodad Hosseini, S. H., & Azizi, Sh. (2012). Designing a Multilevel Performance Assessment Model: A Data Envelopment Analysis Approach. Organizational Resource Management Research, Volume 2 (3), 1-22 (in Persian).
  8. Bagdadioglu, N., Price, C. M. W., & Weyman-Jones, T. G. (1996). Efficiency and ownership in electricity distribution: a non-parametric model of the Turkish experience. Energy Economics, 18(1-2), 1-23.
  9. Bongo, M. F., Ocampo, L. A., Magallano, Y. A. D., Manaban, G. A., & Ramos, E. K. F. (2018). Input–output performance efficiency measurement of an electricity distribution utility using super-efficiency data envelopment analysis. Soft Computing. doi:10.1007/s00500-018-3007-2
  10. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.
  11. Charnes, A., Cooper, W., Lewin, A., & Seiford, L. (1995). Data Envelopment Analysis: Theory, Methodology and Applications, Kluwer Publications.
  12. Cook WD and Green RH. (2005). Evaluating power plant efficiency: a hierarchical model. Computers & Operations Research32, 813-823.
  13. Costa, Á., & Markellos, R. N. (1997). Evaluating public transport efficiency with neural network models. Transportation Research Part C: Emerging Technologies, 5(5), 301-312.
  14. Cullmann, A., & von Hirschhausen, C. (2008). Efficiency analysis of East European electricity distribution in transition: legacy of the past? Journal of Productivity Analysis, 29(2), 155.
  15. De Jong, K. A. (1975). Analysis of the behavior of a class of genetic adaptive systems.
  16. Debreu, G. (1951). The Coefficient of Resource Utilization, Econometric, 19, Economics: Principles and Applications: Zaria: AGTAB Publishers Ltd.
  17. Dreyfus, G. (2005). Neural networks: methodology and applications: Springer Science & Business Media.
  18. Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. Paper presented at the Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on.
  19. Emrouznejad, A., & Shale, E. (2009). A combined neural network and DEA for measuring efficiency of large scale datasets. Computers & Industrial Engineering, 56(1), 249-254.
  20. Fallahi, M., & Ahmadi, V. (2005). Cost efficiency analysis of electricity distribution companies in Iran. Journal of Economic Researches, 71, 297-320.
  21. Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253-290.
  22. Garg, H. (2016). A hybrid PSO-GA algorithm for constrained optimization problems. Applied Mathematics and Computation, 274, 292-305.
  23. Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine learning, 3(2), 95-99.
  24. Goto, M., & Tsutsui, M. (2008). Technical efficiency and impacts of deregulation: An analysis of three functions in US electric power utilities during the period from 1992 through 2000. Energy Economics, 30(1), 15-38.
  25. Hattori, T., Jamasb, T., & Pollitt, M. G. (2003). A comparison of UK and Japanese electricity distribution performance 1985-1998: lessons for incentive regulation.
  26. Hess, B., & Cullmann, A. (2007). Efficiency analysis of East and West German electricity distribution companies–Do the “Ossis” really beat the “Wessis”? Utilities Policy, 15(3), 206-214.
  27. Hjalmarsson, L., & Veiderpass, A. (1992). Efficiency and ownership in Swedish electricity retail distribution International Applications of Productivity and Efficiency Analysis (pp. 3-19): Springer.
  28. Holland, J. (1975). Adaptation in natural and artificial systems: an introductory analysis with application to biology. Control and artificial intelligence.
  29. Hosseini, Mirza Hassan (2012). Measuring Productivity Changes Using Data Envelopment Analysis and Malmquist Index in Power Generation Management Companies .Industrial Management Perspective, 2 (6), 150-129 (in Persian).
  30. Kazemi, Mostafa.,Fayezy Rad, Mohammad Ali (1979). Predicting Performance by Nonlinear Influence of Time Delay in Data Envelopment Analysis with Artificial Neural Networks. Journal of Industrial Management, 10 (1) 34-17 (in Persian).
  31. Koopmans, T. C. (1951). Efficient allocation of resources. Econometrica: Journal of the Econometric Society, 455-465.
  32. Li, J., Li, J., & Zheng, F. (2014). Unified efficiency measurement of electric power supply companies in China. Sustainability, 6(2), 779-793.
  33. Mehregan, Mohammad Reza. (2012). Quantitative Models in Organizational Performance Evaluation (Data Envelopment Analysis). Tehran: Tehran University School of Management Publications (in Persian).
  34. Meibodi, A. E. (1998). Efficiency considerations in the electricity supply industry: The case of Iran: university of Surrey.
  35. Menhaj, Mohammad Bagher. (2014). Fundamentals of Neural Networks (Computational Intelligence) Amirkabir University of Technology (Tehran Polytechnic) (in Persian).
  36. Mostafa, M. M. (2009). Modeling the efficiency of top Arab banks: A DEA–neural network approach. Expert systems with applications, 36(1), 309-320.
  37. Munakata, T. (1998). Fundamentals of the new artificial intelligence (Vol. 2): Springer.
  38. Russell, R. R. (1985). Measures of technical efficiency. Journal of Economic Theory, 35(1), 109-126.
  39. Sadjadi, S., & Omrani, H. (2008). Data envelopment analysis with uncertain data: An application for Iranian electricity distribution companies. Energy Policy, 36(11), 4247-4254.
  40. 40.             Salimi, Mehrdad. Karamati, Mohammad Ali. (2015). Evaluating and Analyzing the Technical Efficiency of Regional Electricity Companies of Iran with Three-Stage Data Envelopment Analysis Approach. Quality and Productivity of Iranian Power Industry, Fourth Year (8), 37-48 (in Persian).
  41. Samoilenko, S., & Osei-Bryson, K.-M. (2010). Determining sources of relative inefficiency in heterogeneous samples: Methodology using Cluster Analysis, DEA and Neural Networks. European Journal of Operational Research, 206(2), 479-487.
  42. Shokrollahpour, E., Lotfi, F. H., & Zandieh, M. (2016). An integrated data envelopment analysis–artificial neural network approach for benchmarking of bank branches. Journal of Industrial Engineering International, 12(2), 137-143.
  43. Soleimaniadena, R., Momeni, M., Mostafaei, A., and Rostami-M Khalifa, M. (2017). Development of a dynamic network data envelopment analysis model to evaluate the performance of banks. Industrial Management Perspectives, 25 (7), 67-89 (in Persian).          
  44. Toloei Eshlaghi, Abbas., Afshar Kazemi, Mohammad Ali. And Abbasi, Fatima. (2013) Evaluation of Insurance Companies' Performance Based on Integrated Balanced Scorecard Approach and Data Envelopment Analysis Technique and Providing Development Pathway for Inefficient Companies. Journal of Business Management (17), 65-82 (in Persian).
  45. Toloie-Eshlaghy, A., Alborzi, M., & Ghafari, B. (2012). Assessment of the personnel’s efficiency with Neuro/DEA combined model.
  46. Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498-509.
  47. Wu, D. D., Yang, Z., & Liang, L. (2006). Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank. Expert systems with applications, 31(1), 108-115.