سال انتشار: ۱۳۸۵

محل انتشار: اولین کنفرانس بین المللی مدیریت و برنامه ریزی انرژی

تعداد صفحات: ۱۰

نویسنده(ها):

M. A. Azadeh – Research Institute of Energy Management and Planning and Department of Industrial Engineering, Faculty of Engineering, University of Tehran Iran
S. F. Ghaderi – Research Institute of Energy Management and Planning and Department of Industrial Engineering, Faculty of Engineering, University of Tehran Iran
M. Anvari – Research Institute of Energy Management and Planning and Department of Industrial Engineering, Faculty of Engineering, University of Tehran Iran
M. Saberi – Department of Industrial Engineering, Faculty of Engineering, University of Bu Ali Sina, Hamedan, Iran and Research Institute of Energy Management and Planning

چکیده:

efficiency frontier analysis has been an important approach of evaluating firms’ performance in private and public sectors. There have been many efficiency frontier analysis methods reported in the literature. However, the assumptions made for each of these methods are restrictive. Each of these methodologies has its strength as well as major limitations. This study proposes two nonparametric efficiency frontier analysis method based on the adaptive neural network technique for measuring efficiency as a complementary tool for the common techniques of the
efficiency studies in the previous studies. The proposed computational methods are able to find a stochastic frontier based on a set of input–output observational data and do not require explicit assumptions about the function structure of the stochastic frontier. in first algorithm, for calculating the efficiency scores, a similar approach to econometric methods has been used and the effect of the scale of decision making
unit (DMU) on its efficiency is included and the unit used for the correction is selected by notice of its scale. But for increasinghomogeneousness, second algorithm is proposed that use Fuzzy C-means method to cluster DMUs. An example using real data is presented for illustrative purposes. In the application to the power generation sector of Iran, we find that the neural network provide more robust results to rank decision making units than the conventional methods.