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.

Fuzzy C-means based data envelopment analysis for mitigating the impact of units’ heterogeneity

Kybernetes ◽  
2016 ◽  
Vol 45 (3) ◽  
pp. 536-551 ◽  
Author(s):  
Seyed Hossein Razavi Hajiagha ◽  
Shide Sadat Hashemi ◽  
Hannan Amoozad Mahdiraji
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Negative Impact ◽  
Positive Impact ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Content Type ◽  
Fuzzy C Means ◽  
The Impact ◽  
Decision Making Units

Purpose – Data envelopment analysis (DEA) is a non-parametric model that is developed for evaluating the relative efficiency of a set of homogeneous decision-making units that each unit transforms multiple inputs into multiple outputs. However, usually the decision-making units are not completely similar. The purpose of this paper is to propose an algorithm for DEA applications when considered DMUs are non-homogeneous. Design/methodology/approach – To reach this aim, an algorithm is designed to mitigate the impact of heterogeneity on efficiency evaluation. Using fuzzy C-means algorithm, a fuzzy clustering is obtained for DMUs based on their inputs and outputs. Then, the fuzzy C-means based DEA approach is used for finding the efficiency of DMUs in different clusters. Finally, the different efficiencies of each DMU are aggregated based on the membership values of DMUs in clusters. Findings – Heterogeneity causes some positive impact on some DMUs while it has negative impact on other ones. The proposed method mitigates this undesirable impact and a different distribution of efficiency score is obtained that neglects this unintended impacts. Research limitations/implications – The proposed method can be applied in DEA applications with a large number of DMUs in different situations, where some of them enjoyed the good environmental conditions, while others suffered from bad conditions. Therefore, a better assessment of real performance can be obtained. Originality/value – The paper proposed a hybrid algorithm combination of fuzzy C-means clustering method with classic DEA models for the first time.

scholarly journals Fuzzy clustering of homogeneous decision making units with common weights in data envelopment analysis

2021 ◽  
Vol 40 (1) ◽  
pp. 813-832
Author(s):  
Sajad Kazemi ◽  
Reza Kiani Mavi ◽  
Ali Emrouznejad ◽  
Neda Kiani Mavi
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Ideal Point ◽  
Weight Vector ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Complex Organizations ◽  
Measuring Efficiency ◽  
Common Set Of Weights ◽  
Decision Making Units

Data Envelopment Analysis (DEA) is the most popular mathematical approach to assess efficiency of decision-making units (DMUs). In complex organizations, DMUs face a heterogeneous condition regarding environmental factors which affect their efficiencies. When there are a large number of objects, non-homogeneity of DMUs significantly influences their efficiency scores that leads to unfair ranking of DMUs. The aim of this study is to deal with non-homogeneous DMUs by implementing a clustering technique for further efficiency analysis. This paper proposes a common set of weights (CSW) model with ideal point method to develop an identical weight vector for all DMUs. This study proposes a framework to measuring efficiency of complex organizations, such as banks, that have several operational styles or various objectives. The proposed framework helps managers and decision makers (1) to identify environmental components influencing the efficiency of DMUs, (2) to use a fuzzy equivalence relation approach proposed here to cluster the DMUs to homogenized groups, (3) to produce a common set of weights (CSWs) for all DMUs with the model developed here that considers fuzzy data within each cluster, and finally (4) to calculate the efficiency score and overall ranking of DMUs within each cluster.

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).

scholarly journals Selection and Analysis of Input-Output Variables using Data Envelopment Analysis of Decision Making Units - Indian Private Sector Banks

2021 ◽  
Vol 10 (5) ◽  
pp. 119-127
Author(s):  
B. Vittal ◽  
Raju Nellutla ◽  
M. Krishna Reddy
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Banking System ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Input Output ◽  
Key Factor ◽  
And Performance ◽  
Using Data ◽  
Decision Making Units

In banking system the evaluation of productivity and performance is the key factor among the fundamental concepts in management. For identify the potential performance of a bank efficiency is the parameter to evaluate effective banking system. To measure the efficiency of a bank selection of appropriate input-output variables is one of the most vital issues. The suitable identification of input-output variables helps to create and identify model in order to evaluate the efficiency and analysis. The Data Envelopment Analysis (DEA) is a mathematical approach used to measure the efficiency of identified Decision Making Units (DMUs). The DEA is a methodology for evaluating the relative efficiency of peer decision making units of identified input/output variables for the financial year 2018-19. In this study the basic DEA CCR, BCC models used for measure the efficiency of DMUs. In addition to these models for minimize the input excess and output shortfall Slack Based Measure (SBM) efficiency used. The SBM is a scalar measure which directly deals with slacks of input, output variables which help in obtain improved efficiency score compare with previous model. The result from the analysis is

A ranking system based on inverse data envelopment analysis

2019 ◽  
Vol 31 (3) ◽  
pp. 367-385 ◽  
Author(s):  
Khosro Soleimani-Chamkhorami ◽  
Farhad Hosseinzadeh Lotfi ◽  
Gholamreza Jahanshahloo ◽  
Mohsen Rostamy-Malkhalifeh
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Commercial Banks ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Input Output ◽  
Data Set ◽  
Ranking System ◽  
Numerical Example ◽  
Decision Making Units

Abstract Inverse (DEA) is an approach to estimate the expected input/output variation levels when the efficiency score reminds unchanged. Essentially, finding most efficient decision-making units (DMUs) or ranking units is an important problem in DEA. A new ranking system for ordering extreme efficient units based on inverse DEA is introduced in this article. In the adopted method, here the amount of required increment of inputs by increasing the outputs of the unit under evaluation is obtained through the proposed models. By obtaining these variations, this proposed methodology enables the researcher to rank the efficient DMUs in an appropriate manner. Through the analytical theorem, it is proved that suggested models here are feasible. These newly introduced models are validated through a data set of commercial banks and a numerical example.

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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