مقایسـه کارایـی شـرکتهای انتقـال نیـرو در صنعـت برق ایـران با اسـتفاده از روشهای مرسـوم و تحلیل پوششـی دادههای شـبکه ای

Subject Areas : Data Envelopment Analysis

کامبیز  شاهرودی ¹ (Associate Professor of Business Administration Administration, Islamic Azad University of Rasht, Iran)
محمد رضا  خسروی ² (PhD student of Business Administration, Islamic Azad University of Rasht, Iran)

Received: 2017-04-19 Accepted : 2017-07-09 Published : 2017-12-01

Keywords: ارزیابی عملکرد کارایی تحلیل پوششی داده های شبکه ای صنعت برق ایران شرکت برق منطقه ای,

Abstract :

ایـن مطالعـه یـک مطالعـه کاربردی اسـت که بـه منظور بررسـی و مقایسـه کارایی شـرکتهای برق منطقهای ایران با اسـتفاده از روشهای تحلیل پوششـی دادههای مرسـوم و شـبکیه انجام شـده اسـت. شـرکتهای بـرق منطقـهای ایـران از فرایند دو مرحلـهای بـرای انتقـال نیـرو اسـتفاده میکنند. با اسـتفاده از رویکـرد کاربردی، عملکـرد و کارایـی ایـن شـرکتها بـا روشهـای شـبکهای و مرسـوم اندازهگیری شـد و با یکدیگر مقایسـه شـد. (BCC ورودی-محور). مشـخص شـد که مدلهای شـبکه در مقایسـه بـا روشهـای دیگـر از آنجـا کـه تصویـر واضـح از کارایـی شـرکتهای بـرق منطقـهای را فراهـم میکنـد ،گسـتردهتر هسـتند. نتایـج آزمـون ویلکاکسـون نشـان میدهـد کـه بیـن نمـرات کارآیی شـرکتهای بـرق منطقهای ایـران بـا اسـتفاده ازروش BCC و روشهای شـبکه تفـاوت معناداری وجـود دارد و بررسـی کیفیـت تفـاوت نمـرات نیـز نشـان میدهد کـه کارایـی شـرکتها در مدل شـبکه پایینتـر از نمـرات کارایـی مدل BCC اسـت. به طور کلی، مدلهای شـبکه دارای کاربـرد بالاتـری نسـبت بـه ارائـه یـک تصویر واضـح از کارایی شـرکتهای بـرق منطقـهای و مقایسـه دقیقتر آنها هسـتند. 

References:

 Ahmadi Movaghar, S. (2008). Performance Evaluation and Optimization of Transmission Sector in Regional Electricity Company Through Combination of Multiple and Intelligence Analysis Techniques (Data Envelopment Analysis and Genetic Algorithm); MA thesis; Technical Faculty of Tehran University (in persian)

Banker. R,D. Charnes, A., & Cooper,W,W. (1984). Some Models for Estimating  Technical and Scale Efficiencies in Data Envelopment Analysis. Management Science, 30: 1078-92.

 Castelli, L., Pesenti, R. & Ukovich, W. (2004). DEA-like models for the efficiency evaluation of hierarchically structured units. European Journal of Operational Research, 154: 465–76.

 Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision- making units. European Journal of Operational Research, 2: 429–44.

Chen, C. & Yan, H. (2011). Network DEA model for supply chain performance evaluation. European Journal of Operational Research, 213: 147–55.

 Cook, W.D., Zhu, J., Bi, G. & Yang, F. (2010). Network DEA: additive efficiency decomposition. European Journal of Operational Research, 207: 1122–9.

 Emami Meybodi, A., Afghah, M., & Rahmani Sefati, M. H. (2009). Measuring the Technical Efficiency and Productivity in Steam, Gas and Combined Sycle Power Stations. The Quarterly of Quantitative Economy, (3) 6, 79-103.

 Fare, R., & Grosskopf, S. (2000). Network DEA. socio-economic plannig science, 34: 35-49.

 Farell, M. (1957). The measurement of productive efficiency. Journal of the Royal Statistics Society, Series A, 120 (3): 253-281.

Fukuyama, H. & Weber, W.L. (2010). A slacks-based inefficiency measure for a two-stage system with bad outputs. Omega, 38: 398–409.

 Iran Monneko Consulting Engineers Company. (2013). Restructuring in Power Industry. Tehran: Shiveh Publications.

 Kao, C. & Hwang, S.N. (2008). Efficiency de composition in two-stage data envelopment analysis: an application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185: 418–29.

 Kao, C. (2009). Efficiency decomposition in network data envelopment analysis: A relational model. European Journal of Operational Research, 192: 949–62.

 Khosravi, M.R. & Shahroudi, K. (2014). Applying Network Data Envelopment Analysis Model to Measure Efficiency of Iran Electric Power Transmission Industry Sector. Industrial Management, 6 (2), 263- 282. (in persian)

 Kwon, H.B., MarveJ, H.& Roh, J.J. (2016). Three-stage performance modeling using DEA-BPNN for better practice benchmarking, Expert Systems with Applications, 71 (1). pp. 429–441.

 Li, Y., Chen, Y., Liang, L. & Xie, J. (2012). DEA models for extended two-stage network structures. Omega, 40: 611–18.

 Liang, L., Yang, F., Cook, W.D. & Zhu, J. (2006). DEA models for supply chain efficiency evaluation. Annals of Operations Research, 145: 35–49.

 Ma, J., Qi, L. & Deng, L.(2017). Efficiency measurement and decomposition in hybrid two-stage DEA with additional inputs, Expert Systems with Applications, 79 (15). pp.348–357.

 Mehregan, M. R. (2004). Quantitative Models for Performance Evaluations of Organizations. Teharn: Teharn University Management College Publications.

 Mo'meni. M. & Shahkhah, N. (2011). The Use of Communication Two-Phase Data Envelopment Analysis Model in Efficiency Evaluation. Daneshvar Raftar, (47-1), 333-344.

 Salehzadeh, S. J., Hejazi, S. R., Arkan, A. V., & Hoseini, S. M. (2011). Presenting a Synthetic Method of Efficiency Measurement of Network Structures Including Allocated Revolution and Links. Journal of Production and Operation, (1), 47-60.

 Shahriari, S., Razavi, S. M., & Asgharizadeh, A. (2013). Fuzzy Data Envelopment Analysis and the New Approach FIEP/AHP to Complete Ranking of Decision Making Units. (Case Study: School of Humanities of Tehran University). Industrial Management, 5 (1), 21-42.

 Tone K. & Tsutsui M. (2009). Network DEA: a slacks-based measure approach. European Journal of Operational Research, 197: 243–52.

 Wang, Q. Hang, Y., Sun, L. & Zhao, Z. (2016). Two-stage innovation efficiency of new energy enterprises in China: A non-radial DEA approach, Technological Forecasting & Social Change, 112 (1). pp.254–261.