An optimistic-pessimistic DEA model based on game cross efficiency approach

2020 ◽  
Vol 54 (4) ◽  
pp. 1215-1230
Author(s):  
Mediha Örkcü ◽  
Volkan Soner Özsoy ◽  
H. Hasan Örkcü
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Equilibrium Solution ◽  
Data Envelopment ◽  
Optimal Weights ◽  
Discriminating Power ◽  
Proposed Model ◽  
Essential Problem ◽  
Decision Making Units ◽  
Selection Of

The ranking of the decision making units (DMUs) is an essential problem in data envelopment analysis (DEA). Numerous approaches have been proposed for fully ranking of units. Majority of these methods consider DMUs with optimistic approach, whereas their weaknesses are ignored. In this study, for fully ranking of the units, a modified optimistic–pessimistic approach, which is based on game cross efficiency idea is proposed. The proposed game like iterative optimistic-pessimistic DEA procedure calculates the efficiency scores according to weaknesses and strengths of units and is based on non-cooperative game. This study extends the optimistic-pessimistic DEA approach to obtain robust rank values for DMUs. The proposed approach yields Nash equilibrium solution, thus overcomes the problem of non-uniqueness of the DEA optimal weights that can possibly reduce the usefulness of cross efficiency. Finally, in order to verify the validity of the proposed model and to show the practicability of algorithm, we apply a real-world example for selection of industrial R&D projects. The proposed model can increase the discriminating power of DMUs and can fully rank the DMUs.

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.

Use Shapley value for increasing power distinguish of data envelopment analysis model: An application for estimating environmental efficiency of industrial producers in Iran

2019 ◽  
Vol 31 (4) ◽  
pp. 656-675
Author(s):  
Hashem Omrani ◽  
Mohaddeseh Amini ◽  
Mahdieh Babaei ◽  
Khatereh Shafaat
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Shapley Value ◽  
Environmental Efficiency ◽  
Data Envelopment Analysis Model ◽  
Game Model ◽  
Data Envelopment ◽  
Analysis Model ◽  
Proposed Model ◽  
Decision Making Units

Data envelopment analysis is a linear programming model for estimating the efficiency of decision making units (DMUs). Data envelopment analysis model has two major advantages: it does not need the explicit form of production function for estimating the efficiency scores of decision making units and also, it allows decision making units to choose the weights of inputs and outputs to reach the estimated efficient frontier. In several cases, the distinguish power of data envelopment analysis model is weak and it is unable to rank decision making units, entirely. The goal of this study is to provide a better methodology to fully rank all the decision making units. First, the efficiency scores of all decision making units are generated using the cross-efficiency data envelopment analysis model and then, the cooperative game theory approach is applied to produce a fully fair ranking of decision making units. The DEA-Game model calculates the Shapley value for each coalition of decision making units and the final ranking is relied on common weights. These fair common weights are found using the Shapley value to rank decision making units, completely. To illustrate the capability of the proposed model, the industrial producers in the provinces of Iran are evaluated. First, the suitable indicators are defined and then, the actual environmental data for year 2013 is gathered. Finally, the proposed model is applied to fully rank the industrial producers in provinces of Iran from environmental perspective. The results show that the DEA-Game model can rank provinces, entirely. Based on the results, the industrial producers in big provinces such as Tehran, Fars and Yazd have undesirable performance in environmental efficiency.

scholarly journals Ranking of units by corrected cross-efficiency method using optimal weights in the smallest interval

2020 ◽  
Vol 33 (02) ◽  
pp. 446-453
Author(s):  
Fatemeh Gholami Golsefid ◽  
Behrouz Daneshian ◽  
Mohsen Rostamy-Malkhalifeh
Keyword(s):  
Decision Making ◽  
Emergency Department ◽  
The Other ◽  
Data Envelopment ◽  
Improve Performance ◽  
Important Method ◽  
Optimal Weights ◽  
Proposed Model ◽  
Objective Model ◽  
Decision Making Units

An important method for ranking of decision making units (DMUs) in data envelopment analysis (DEA) is cross-efficiency method. This study proposes a secondary multi-objective model for calculating optimal weights with least dispersion. Firstly, these weights are placed in the smallest interval. Secondly, the cross-efficiency of each of the other units has the least deviation from the CCR efficiency of the same unit. Therefore, optimal weights are obtained which have the least dispersion. As result, the zero optimal weights which lead to the triviality of the relevant index, are avoided as far as possible. Hence, using the average cross-efficiency, the results of the ranking would be more reasonable. Using the proposed model for ranking of six nursing homes, the results show that this model is more accurate. Finally, in order to improve performance of the emergency department of a hospital, the proposed model is used to rank 11 defined scenarios.

scholarly journals A Dominance-Based Network Method for Ranking Efficient Decision-Making Units in Data Envelopment Analysis

2019 ◽  
Vol 11 (7) ◽  
pp. 2059 ◽  
Author(s):  
Jiyoung Lee ◽  
Gyunghyun Choi
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Hybrid Electric Vehicles ◽  
Ranking Method ◽  
Data Envelopment ◽  
Discriminating Power ◽  
Network Method ◽  
Representation Technique ◽  
Using Data ◽  
Decision Making Units

Ranking of efficient decision-making units (DMUs) using data envelopment analysis (DEA) results is very important for various purposes. We propose a new comprehensive ranking method using network analysis for efficient DMUs to improve the discriminating power of DEA. This ranking method uses a measure, namely dominance value, which is a network centrality-based indicator. Thus far, existing methods exploiting DMU’s positional features use either the superiority, which considers the efficient DMUs’ relative position on the frontier compared to other DMUs, or the influence, which captures the importance of the DMUs’ role as benchmarking targets for inefficient DMUs. However, in this research, the dominance value is the compounded measure of both core positional features of DMUs. Moreover, a network representation technique has been used to ensure the performance of the dominance value compared to the superiority and influence. To demonstrate the proposed ranking method, we present two examples, research and development (R&D) efficiency of small and medium-sized enterprises (SMEs) and technical efficiency of plug-in hybrid electric vehicles (HEVs). Through these two examples, we can see how the known weaknesses and the unobserved points in the existing method differ in this new method. Hence, it is expected that the proposed method provides another new meaningful ranking result that can show different implications.

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.

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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