A new data envelopment analysis method for ranking decision making units: an application in industrial parks

2015 ◽  
Vol 32 (5) ◽  
pp. 596-608 ◽  
Author(s):  
Mohammad Izadikhah ◽  
Reza Farzipoor Saen
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Analysis Method ◽  
Industrial Parks ◽  
Decision Making Units

A Robust Data Envelopment Analysis Method with Bounded Data for Ranking Operations Strategies

2015 ◽  
Vol 6 (3) ◽  
pp. 49-64 ◽  
Author(s):  
Mohamad Amin Kaviani ◽  
Mehdi Abbasi
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Cement Industry ◽  
Data Envelopment ◽  
Analysis Method ◽  
Interval Numbers ◽  
Complete Ranking ◽  
Bounded Data ◽  
Decision Making Units

This paper introduces a new robust data envelopment analysis (RDEA) approach for analyzing and ranking the organizations' operations strategies. In the proposed RDEA method, pessimistic and optimistic efficiencies of decision making units (DMUs) obtained from the robust counterpart of the envelopment form and the optimistic counterpart of the multiplier form of DEA are introduced. The inputs and outputs data are assumed to be bounded data (interval numbers) in the proposed models. A case study in the cement industry is presented to demonstrate the applicability of the proposed RDEA approach. The results obtained from the authors' proposed RDEA approach is more robust and their method provides a more complete ranking of the DMUs compared to conventional Likert-based DEA model.

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.

Measuring Performance of Dynamic and Network Structures by SBM Model

2015 ◽  
pp. 527-558
Author(s):  
N. Aghayi ◽  
Z. Ghelej Beigi ◽  
K. Gholami ◽  
F. Hosseinzadeh Lotfi
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Dynamic Network ◽  
Black Box ◽  
Time Dependent ◽  
Data Envelopment ◽  
Network Efficiency ◽  
Sbm Model ◽  
Radial Model ◽  
Decision Making Units

The conventional Data Envelopment Analysis (DEA) model considers Decision Making Units (DMUs) as a black box, meaning that these models do not consider the connection and the inner structures of DMUs. Moreover, these models consider that the activities of DMUs in each time are independent of other times, but in the real world, the inner structures of DMUs are complicated, and the activities of DMUs are dependent on other times. Therefore, in this chapter, the authors consider DMUs with network structure and the activity of each DMU in each time dependent to activity of other times, so they call this structure a dynamic network. To this end, in this chapter, models are suggested to evaluate the dynamic network efficiency based on the SBM model, which is a non-radial model of three types with respect to orientation: input-oriented, output-oriented, and non-oriented.

A Common Set of Weights for Ranking Decision-Making Units with Undesirable Outputs: A Double Frontiers Data Envelopment Analysis Approach

2018 ◽  
Vol 35 (06) ◽  
pp. 1850039 ◽  
Author(s):  
Lei Chen ◽  
Fei-Mei Wu ◽  
Feng Feng ◽  
Fujun Lai ◽  
Ying-Ming Wang
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Optimal Solution ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Discrimination Power ◽  
Efficiency Optimization ◽  
Common Set Of Weights ◽  
Desirable And Undesirable Outputs ◽  
Decision Making Units

Major drawbacks of the traditional data envelopment analysis (DEA) method include selecting optimal weights in a flexible manner, lacking adequate discrimination power for efficient decision-making units, and considering only desirable outputs. By introducing the concept of global efficiency optimization, this study proposed a double frontiers DEA approach with undesirable outputs to generate a common set of weights for evaluating all decision-making units from both the optimistic and pessimistic perspectives. For a unique optimal solution, compromise models for individual efficiency optimization were developed as a secondary goal. Finally, as an illustration, the models were applied to evaluate the energy efficiency of the Chinese regional economy. The results showed that the proposed approach could improve discrimination power and obtain a fair result in a case where both desirable and undesirable outputs exist.

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