جستجوی مقالات مرتبط با کلیدواژه « تحلیل پنجره » در نشریات گروه « مدیریت »

Input and output selection in Data Envelopment Analysis (DEA) has many important. In this research, inputs and outputs of reginal power companies are selected with artifitial neural network. The application of neural network in the selection of inputs and outputs of reginal power companies is not a precedent in the literature and it is considered the main advantage of the proposed method. In order to train two layers MLP neural network, after presenting of error resilience, learning method was used. After neural network training, neural network performance is examined by using the test set. RMSE value for 15 test set equals 0/0269 which reflects the high accuracy of training network. The Sensitivity Analysis of the studied parameters which are the same inputs and outputs of Data Envelopment Analysis, with ten percent increase of parameter, compared to the prior one was carried out and output relative error average for neural network parameters was calculated. Based on the output relative error average, inputs and outputs were determined. By comparing the efficiency scores of regional electricity companies before and after reducing the number of variables, it is noticed that the number of efficient companies during the above four periods decreased from 50 percent to 11 percent. Finally, the neural network application in inputs and outputs selection of the regional electricity companies was unprecedented in the literature and this is the main advantage of this method.

The aim of this study is to evaluate efficiency of the power industry using an integrated approach of Network DEA with a variety of outputs and Windows Analysis. This network system includes undesirable, final desirable and intermediate output in time interval of 2012 to 2014. Input and output variables of first and second stages were respectively considered as domestic consumption of power stations, fuel consumption, thermal output, efficiency, nominal power of power station and operational power of power station, maximum production, especial production and gross production, capacity of power transmission substations, and finally delivery energy and energy losses. The results indicate that due to the possibility of integration over three times, in terms of total efficiency and efficiency of production stage, there is highest number of efficient units. In 2013 However, compared to efficient frontier of three periods, electricity production stage had the greatest number of inefficient units and increasing the number of efficient units in the total efficiency stage is due to high efficiency of regional electricity companies.

Window analysis basically assumes that no technical change has taken place in the periods under review. This underlying assumption has been neglected in most research works. In the current study, using non-parametric statistical Wilcoxon test and Friedman test between two time periods and over two time periods, the shift frontier between the periods under review has been investigated and determined. Accordingly, the length of the windows was selected in the way that the basic assumption of window analysis was observed. In this research, the data of 7 distribution-channels of Pars Khazar Company was picked up in time periods of one year (2005-2011). After running the statistical tests, it was revealed that shift frontiers had occurred within the periods (2007 and 2008), (2008 and 2009) and (2010 and 2010). So four analyses with the lengths of 3, 1, 2, and 1 were performed in which the average performance of the units was obtained as 0.832, 0.987, 0.906 and 0.953, respectively.

Window Analysis is a known technique to evaluate performance during the time periods based on data envelopment analysis. In application of Window Analysis is assumed that efficient frontier have not changed during the periods in the study. Unlike the Window Analysis، Malmquist index tries to identify difference between efficient frontiers and separated them. But the means of different frontier in the Window Analysis are the their general changes that can be studied with statistical methods. While from this standpoint of Malmquist، limited points of frontiers is considered that using mathematical methods is indivisible. In this study، Considering conformity of Production Possibility Set in Malmquist index with window analysis with width is equal to or greater، a framework is proposed that enables the user by determining appropriate length of the window that prevents violating basic assumption، and by using appropriate kind of Malmquist index، calculate the differences point in frontiers in evaluation of performance.

In this paper with the aims to examine the changes in productivity, two well-known techniques of evaluation over time studied that window Analysis and Malmquist productivity index and by selecting the appropriate length for the windows, the window Analysis and a newer version of Malmquist productivity index used within compilation framework implemented so that performance compared to the same period in consecutive time, and adding new information to does not lead the necessity of repeating the previous calculations periods. In this framework, we combine the two techniques in an attempt to compensate for the weaknesses of each to help with the implementation of a simple statistical test; the overall situation information is also frontiers of technology. Shifted of techniques that have steadily organized manner at any point of use with minimal calculations relatively comprehensive level of information provided. Proposed Framework for Supervision Bureau of the Sepah Bank South of Tehran in 9 Periods of has been applied to the detection of four cases of shift frontier, the performance of the proposed framework is shown to be the case.