Evaluating the efficiency of power companies using data envelopment analysis based on SBM models: a case study in power industry of Iran

Document Type: Research Paper

Authors

1 Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran.

3 Department of Industrial Engineering, Industrial Management Institute (IMI), Tehran, Iran.

Abstract

Data Envelopment Analysis (DEA) is one of the most powerful and useful instrument to evaluate the efficiency of DMU’s. To measure the relative efficiency of multi-sector DMU’s, it is essential to focus on the activities which relate these sectors together.  The electrical firms or companies usually include different parts or sectors and focusing on the efficiency of each part enables us to evaluate the efficiency of the complex in a better way. In this paper, using the network DEA based on SBM approach we have measured the efficiency of electrical firms or companies in Iran.

Keywords


[1]     Alavidoost, M. H. (2017). Assembly line balancing problems in uncertain environment: a novel interactive fuzzy approach for solving multi-objective fuzzy assembly line balancing problems. LAP LAMBERT Academic Publishing.

[2]    Alavidoost, M. H., Tarimoradi, M., & Zarandi, M. F. (2015). Fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problems. Applied soft computing34, 655-677.

[3]    Tarimoradi, M., Alavidoost, M. H., & Zarandi, M. F. (2015). Comparative corrigendum note on papers “Fuzzy adaptive GA for multi-objective assembly line balancing” continued “Modified GA for different types of assembly line balancing with fuzzy processing times”: differences and similarities. Applied soft computing35, 786-788.

[4]    Alavidoost, M. H., Babazadeh, H., & Sayyari, S. T. (2016). An interactive fuzzy programming approach for bi-objective straight and U-shaped assembly line balancing problem. Applied soft computing40, 221-235.

[5]    Alavidoost, M. H., Zarandi, M. F., Tarimoradi, M., & Nemati, Y. (2017). Modified genetic algorithm for simple straight and U-shaped assembly line balancing with fuzzy processing times. Journal of intelligent manufacturing28(2), 313-336.

[6]    Alavidoost, M. H., & Nayeri, M. A. (2014). Proposition of a hybrid NSGA-II algorithm with fuzzy objectives for bi-objective assembly line balancing problem. Tenth international industrial engineering conference.

[7]    Alavidoost, M. H., Tarimoradi, M., & Zarandi, M. F. (2018). Bi-objective mixed-integer nonlinear programming for multi-commodity tri-echelon supply chain networks. Journal of intelligent manufacturing29(4), 809-826.

[8]    Babazadeh, H., Alavidoost, M. H., Zarandi, M. F., & Sayyari, S. T. (2018). An enhanced NSGA-II algorithm for fuzzy bi-objective assembly line balancing problems. Computers & industrial engineering, 123, 189-208.

[9]    Zarandi, M. F., Tarimoradi, M., Alavidoost, M. H., & Shakeri, B. (2015, August). Fuzzy approximate reasoning toward Multi-objective optimization policy: deployment for supply chain programming. 2015 annual conference of the north american fuzzy information processing society (nafips) held jointly with 2015 5th world conference on soft computing (WConSC)(pp. 1-6). Redmond, WA, USA: IEEE.

[10] Zarandi, M. F., Tarimoradi, M., Alavidoost, M. H., & Shirazi, M. A. (2015, August). Fuzzy comparison dashboard for multi-objective evolutionary applications: an implementation in supply chain planning. 2015 annual conference of the north american fuzzy information processing society (nafips) held jointly with 2015 5th world conference on soft computing (WConSC)(pp. 1-6). Redmond, WA, USA: IEEE.

[11] Nemati, Y., & Alavidoost, M. H. (2018). A fuzzy bi-objective MILP approach to integrate sales, production, distribution and procurement planning in a FMCG supply chain. Soft computing, 1-20.

[12] Nemati, Y., Madhoushi, M., & Alavidoost, M. H. (2017). Modeling of S&OP in a large scale dairy supply chain. Scholar's Press.

[13] Kao, C. (2014). Network data envelopment analysis: a review. European journal of operational research239(1), 1-16.

[14] Shahrasbi, A., Shamizanjani, M., Alavidoost, M. H., & Akhgar, B. (2017). An aggregated fuzzy model for the selection of a managed security service provider. International journal of information technology & decision making16(03), 625-684.

[15] Mahmoudabadi, M. Z., Azar, A., & Emrouznejad, A. (2018). A novel multilevel network slacks-based measure with an application in electric utility companies. Energy, 158, 1120-1129.

[16] Shermeh, H. E., Najafi, S. E., & Alavidoost, M. H. (2016). A novel fuzzy network SBM model for data envelopment analysis: a case study in Iran regional power companies. Energy112, 686-697.

[17] Emrouznejad, A., & Yang, G. L. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic planning sciences61, 4-8.

[18] Olatubi, W. O., & Dismukes, D. E. (2000). A data envelopment analysis of the levels and determinants of coal-fired electric power generation performance. Utilities policy9(2), 47-59.

[19] Lam, P. L., & Shiu, A. (2001). A data envelopment analysis of the efficiency of China’s thermal power generation. Utilities policy10(2), 75-83.

[20] Nag, B. (2006). Estimation of carbon baselines for power generation in India: the supply side approach. Energy policy34(12), 1399-1410.

[21] Sueyoshi, T., & Goto, M. (2012). Data envelopment analysis for environmental assessment: comparison between public and private ownership in petroleum industry. European journal of operational research216(3), 668-678.

[22] Sueyoshi, T., Goto, M., & Ueno, T. (2010). Performance analysis of US coal-fired power plants by measuring three DEA efficiencies. Energy policy38(4), 1675-1688.

[23] Pahwa, A., Feng, X., & Lubkeman, D. (2003). Performance evaluation of electric distribution utilities based on data envelopment analysis. IEEE transactions on power systems18(1), 400-405.

[24] Sanhueza, R., Rudnick, H., & Lagunas, H. (2004). DEA efficiency for the determination of the electric power distribution added value. IEEE transactions on power systems19(2), 919-925.

[25] Azadeh, A., Amalnick, M. S., Ghaderi, S. F., & Asadzadeh, S. M. (2007). An integrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy intensive manufacturing sectors. Energy policy35(7), 3792-3806.

[26] Agrell, P. J., & Bogetoft, P. (2005). Economic and environmental efficiency of district heating plants. Energy policy33(10), 1351-1362.

[27] Hjalmarsson, L., & Veiderpass, A. (1992). Productivity in Swedish electricity retail distribution. The Scandinavian journal of economics, S193-S205.

[28] 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 economics18(1-2), 1-23.

[29] Førsund, F. R., & Kittelsen, S. A. (1998). Productivity development of Norwegian electricity distribution utilities. Resource and energy economics20(3), 207-224.

[30] Filippini, M., Wild, J., & Kuenzle, M. (2001). Scale and cost efficiency in the Swiss electricity distribution industry: evidence from a frontier cost approach. CEPE working paper8.

Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European journal of operational research197(1), 243-252.