عنوان مقاله [English]
Data Envelopment Analysis (DEA) is a method based on linear programming to measure efficiency of Decision Making Units (DMU). In classic models of DEA, the whole system had been usually considered as a Decision Making Units to evaluate respective efficiency and it is also ignored the separate processes inside the system. Whereas, the internal relations of various sectors of a Decision Making Unit can have had diverse structures which cause complexity in evaluating its efficiency, because, the type of structures and the performance of these components would have different effects on efficiency of the system. Network standpoint is one of the appropriate ways for the internal relations of units’ modeling and the relation among sub-units in a DMU may be communicated in series, parallel or mixed way. In this paper, a new convert called Star Structure was introduced as a comprehensive one. The one that every structure existing between a Decision Making Units’ sub-units can easily be converted to such structure so that can accurately evaluate a Decision Making Units’ efficiency and also using star structure, we evaluated the performance of regional electronic companies in Iran.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
Charnes, A., Cooper, W. W., & Rhodes, E. (1981). Evaluating program and managerial efficiency: an application of data envelopment analysis to program follow through. Management science, 27(6), 668-697.
Chen, Y., Liang, L., Yang, F., & Zhu, J. (2006). Evaluation of information technology investment: a data envelopment analysis approach. Computers & operations research, 33(5), 1368-1379.
Färe, R., & Grosskopf, S. (1997). Intertemporal production frontiers: with dynamic DEA. Journal of the operational research society, 48(6), 656-656.
Fӓre, R. (1997). Efficiency and productivity in Rich and poor countries. In B. S. Jensen & K. Wong, (Eds.), Dynamics, economic growth, and international trade (pp. 43-63). University of Michigan press.
Fӓre, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic planning sciences, 34, 35-49.
Kao, C. (2009a). Efficiency measurement for parallel production systems. European journal of operational research, 196(3), 1107-1112.
Kao, C. (2009b). Efficiency decomposition in network data envelopment analysis: A relational model. European journal of operational research, 192(3), 949-962.
Kao, C. (2014). Efficiency decomposition for general multi-stage systems in data envelopment analysis. European journal of operational research, 232(1), 117-124.
Kao, C., & Hwang, S. N. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European journal of operational research, 185(1), 418-429.
Kao, C., & Hwang, S. N. (2010). Efficiency measurement for network systems: IT impact on firm performance. Decision support systems, 48(3), 437-446.
Keh, H. T., Chu, S., & Xu, J. (2006). Efficiency, effectiveness and productivity of marketing in services. European journal of operational research, 170(1), 265-276.
Lewis, H. F., & Sexton, T. R. (2004). Network DEA: efficiency analysis of organizations with complex internal structure. Computers & operations research, 31(9), 1365-1410.
Liang, L., Cook, W. D., & Zhu, J. (2008). DEA models for two‐stage processes: Game approach and efficiency decomposition. Naval research logistics (NRL), 55(7), 643-653.
Liu, J. S., & Lu, W. M. (2012). Network-based method for ranking of efficient units in two-stage DEA models. Journal of the operational research society, 63(8), 1153-1164.
Liu, J. S., & Lu, W. M. (2010). DEA and ranking with the network-based approach: a case of R&D performance. Omega, 38(6), 453-464.
Lovell, C. A. K., Walters, L. C., & Wood, L. L. (1994). Stratified models of education production using modified DEA and regression analysis. In A. Charnes W. W. Copper A. Y. Lewin & L. M. Seiford (Eds.), Data envelopment analysis: theory, methodology and applications (pp. 329-351). London: Kluwer Academic Publishers.
Lozano, S. (2015). Alternative SBM model for network DEA. Computers & industrial engineering, 82, 33-40.
Prieto, A. M., & Zofío, J. L. (2007). Network DEA efficiency in input–output models: with an application to OECD countries. European journal of operational research, 178(1), 292-304.
Bernstein, M. A., Feldman, S. L., & Schinnar, A. P. (1990). Impact of pollution controls on the productivity of coal-fired power plants. Energy economics, 12(1), 11-17.
Seiford, L. M., & Zhu, J. (1999). Profitability and marketability of the top 55 US commercial banks. Management science, 45(9), 1270-1288.
Sexton, T. R., & Lewis, H. F. (2003). Two-stage DEA: An application to major league baseball. Journal of productivity analysis, 19(2-3), 227-249.
Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European journal of operational research, 197(1), 243-252.
Yaghobi, A. (2016). A detailed report on Iran’s electricity power industry, particularly management. Retrieved from Tehran: Ministry of Power of the Islamic Republic of Iran, Tavanir, Department of Human Resources and Research.