scholarly journals An Interpretation Of The Technical Efficiency As The "Best Possible Deviation" From The Conditions Defined By The Weak Axiom Of Profit Maximization

2011 ◽  
Vol 6 (2) ◽  
Author(s):  
Said Gattoufi ◽  
Yuntong Wang ◽  
Arnold Reisman ◽  
Muhittin Oral
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Profit Maximization ◽  
Data Envelopment ◽  
Efficiency Measures ◽  
Weak Axiom ◽  
Ccr Model ◽  
Decision Making Units

This paper provides a characterization of the classical Charnes, Cooper and Rhodes (CCR) model in Data Envelopment Analysis (DEA). The characterization is based on the Weak Axiom of Profit Maximization (WAPM) in Firm Theory. Efficiency measures for Decision Making Units (DMUs) provided by the classical CCR-DEA model are derived as measurements of deviations from the conditions prescribed by the Weak Axiom of Profit Maximization (WAPM).

Technical Efficiency of State Road Transport Undertakings in India - Data Envelopment Analysis

2012 ◽  
Vol 1 (2) ◽  
pp. 31-42 ◽  
Author(s):  
V. Prakash ◽  
J. Rajesh ◽  
M. Thilagam
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Relative Efficiency ◽  
Fuel Efficiency ◽  
Returns To Scale ◽  
Road Transport ◽  
Data Envelopment ◽  
Variable Returns To Scale ◽  
Decision Making Units

Data envelopment analysis (DEA) is a method of analyzing the relative efficiency of similar types of organizations known as decision making units (DMU’s). In this paper, DEA model is applied to evaluate the relative technical efficiency of state road transport undertakings (SRTU’s) in India during the period 2011-2012. The authors have considered thirty-four SRTU’s functioning in India. The variables chosen to characteristic production units are the number of fleet held, staff strength and fuel efficiency as inputs and Passengers carried as output. The BCC model is input- oriented allowing for variable returns to scale (VRS), units are ranked and the projection analyses are given.

scholarly journals Data envelopment analysis of randomized ranks

2002 ◽  
Vol 22 (2) ◽  
pp. 203-215 ◽  
Author(s):  
Annibal P. Sant'Anna
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Efficiency Frontier ◽  
Preliminary Information ◽  
Efficiency Measures ◽  
Location Parameters ◽  
Random Disturbances ◽  
Decision Making Units

Probabilities and odds, derived from vectors of ranks, are here compared as measures of efficiency of decision-making units (DMUs). These measures are computed with the goal of providing preliminary information before starting a Data Envelopment Analysis (DEA) or the application of any other evaluation or composition of preferences methodology. Preferences, quality and productivity evaluations are usually measured with errors or subject to influence of other random disturbances. Reducing evaluations to ranks and treating the ranks as estimates of location parameters of random variables, we are able to compute the probability of each DMU being classified as the best according to the consumption of each input and the production of each output. Employing the probabilities of being the best as efficiency measures, we stretch distances between the most efficient units. We combine these partial probabilities in a global efficiency score determined in terms of proximity to the efficiency frontier.

scholarly journals A Slacks-based Measure in the Presence of Ratio Variable with Convexity Consideration

2020 ◽  
pp. 53-62
Author(s):  
Farzaneh Ghaffari ◽  
Morteza Haghiri
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Data Envelopment ◽  
Analysis Method ◽  
Input Output ◽  
Input And Output ◽  
Ratio Data ◽  
Measuring Efficiency ◽  
Decision Making Units

The nature of input-output relationships in general and ratio data in particular has important consequences for practitioners when the data envelopment analysis method is used to  measure technical efficiency of decision making units or production units. Since the data envelopment analysis approach was introduced several studies tried to develop the model from different aspects including when the model deals with ratio data. To date, none of these studies was able to address the aforementioned problem properly and as a result most of them suffered from a lack of clarity in the presence of input-and-output ratios. This study proposes a slacks-based measure of efficiency in the presence of ratio variable. Our approach deals directly with the input excess and the output shortfalls of the decision making units’ concerns, and as a result, improved measuring efficiency scores.

Decision-Making Support in Evaluating Gaps and Efficiencies of the Railway Industry Performance

2020 ◽  
Vol 12 (4) ◽  
pp. 65-79
Author(s):  
Osman Ghanem ◽  
Li Xuemei
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Performance Assessment ◽  
Technical Efficiency ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Analysis Method ◽  
Industry Performance ◽  
Railway Industry ◽  
Decision Making Units

An efficiency evaluation is one of the most significant tools of transportation performance assessment and is of particular importance to decision making units to consider efficiency issues. The experience of Turkey can be used to compare and improve the efficiency of rail performance. The study employs both of radial and non-radial of data envelopment analysis method, where efficiency scores and technical efficiency of rail performance were ranked and compared over period 1977–2017. The study was fulfilled that Turkey rail is more capable in terms of exploiting its transport indicators into useful outputs. The outcomes indicated that the rail performance was operating most effectively, and the most efficient years were 1977, 1978, 1979, 1984, 1985, 1988, 1989, 1990, 1993, 2008, 2010, 2011, 2014, 2015, 2016, and 2017, whereas it exhibited relative inefficiency throughout 2001–2002, in which the efficiency scores decreased in relation to other years.

Relative Efficiency of Decision Making Units Producing Both Desirable and Undesirable Outputs: A Case of Textile Processing Units in Pakistan.

2011 ◽  
Vol 50 (4II) ◽  
pp. 685-698
Author(s):  
Samina Khalil
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Relative Efficiency ◽  
Value Added ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Textile Processing ◽  
Decision Making Unit ◽  
Desirable And Undesirable Outputs ◽  
Decision Making Units

This paper aims at measuring the relative efficiency of the most polluting industry in terms of water pollution in Pakistan. The textile processing is country‘s leading sub sector in textile manufacturing with regard to value added production, export, employment, and foreign exchange earnings. The data envelopment analysis technique is employed to estimate the relative efficiency of decision making units that uses several inputs to produce desirable and undesirable outputs. The efficiency scores of all manufacturing units exhibit the environmental consciousness of few producers is which may be due to state regulations to control pollution but overall the situation is far from satisfactory. Effective measures and instruments are still needed to check the rising pollution levels in water resources discharged by textile processing industry of the country. JEL classification: L67, Q53 Keywords: Data Envelopment Analysis (DEA), Decision Making Unit (DMU), Relative Efficiency, Undesirable Output

scholarly journals Measuring the Productivity of Energy Consumption of Major Industries in China: A DEA-Based Method

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xishuang Han ◽  
Xiaolong Xue ◽  
Jiaoju Ge ◽  
Hengqin Wu ◽  
Chang Su
Keyword(s):  
Decision Making ◽  
Energy Consumption ◽  
Data Envelopment Analysis ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Productivity Change ◽  
Data Envelopment ◽  
Policy Makers ◽  
Time Periods ◽  
Decision Making Units

Data envelopment analysis can be applied to measure the productivity of multiple input and output decision-making units. In addition, the data envelopment analysis-based Malmquist productivity index can be used as a tool for measuring the productivity change during different time periods. In this paper, we use an input-oriented model to measure the energy consumption productivity change from 1999 to 2008 of fourteen industry sectors in China as decision-making units. The results show that there are only four sectors that experienced effective energy consumption throughout the whole reference period. It also shows that these sectors always lie on the efficiency frontier of energy consumption as benchmarks. The other ten sectors experienced inefficiency in some two-year time periods and the productivity changes were not steady. The data envelopment analysis-based Malmquist productivity index provides a good way to measure the energy consumption and can give China's policy makers the information to promote their strategy of sustainable development.

Forecast of the efficiency confidence interval of decision-making units in data envelopment analysis

2020 ◽  
Vol 33 (02) ◽  
pp. 431-445
Author(s):  
Azarnoosh Kafi ◽  
Behrouz Daneshian ◽  
Mohsen Rostamy-Malkhalifeh ◽  
Mohsen Rostamy-Malkhalifeh
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Confidence Interval ◽  
Data Envelopment ◽  
Data Set ◽  
Interval Model ◽  
Time Period ◽  
Proposed Model ◽  
The Future ◽  
Decision Making Units

Data Envelopment Analysis (DEA) is a well-known method for calculating the efficiency of Decision-Making Units (DMUs) based on their inputs and outputs. When the data is known and in the form of an interval in a given time period, this method can calculate the efficiency interval. Unfortunately, DEA is not capable of forecasting and estimating the efficiency confidence interval of the units in the future. This article, proposes a efficiency forecasting algorithm along with 95% confidence interval to generate interval data set for the next time period. What’s more, the manager’s opinion inserts and plays its role in the proposed forecasting model. Equipped with forecasted data set and with respect to data set from previous periods, the efficiency for the future period can be forecasted. This is done by proposing a proposed model and solving it by the confidence interval method. The proposed method is then implemented on the data of an automotive industry and, it is compared with the Monte Carlo simulation methods and the interval model. Using the results, it is shown that the proposed method works better to forecast the efficiency confidence interval. Finally, the efficiency and confidence interval of 95% is calculated for the upcoming period using the proposed model.

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