International Journal of Data Envelopment Analysis
2345-458X
7
1
2019
01
01
Ranking of units by anti-ideal DMU with common weights
1
10
EN
Masoumeh
Khanmohammadi
Islamic Azad University, Islamshahr Branch
kh_khanmohamady@yahoo.com
Maryam
Davaei Far
Sama technical and vocational training college, Dezfoul Branch, Islamic Azad University, Dezfoul, Iran
d.davaeifar@yahoo.com
Data envelopment analysis (DEA) is a powerful technique for performance evaluation of decision making units (DMUs). One of the main objectives that is followed in performance evaluation is discriminating among efficient DMUs to provide a complete ranking of DMUs. DEA successfully divides them into two categories: efficient DMUs and inefficient DMUs. The DMUs in the efficient category have identical efficiency score. But the question that raises here is in evaluation. Where several DMUs have the equal efficiency, which unit performs better and how can we rank these efficient units, Different methods have been presented for ranking the efficient units. <br /> In this paper, we propose a method for calculating an efficiency of DMUs by comparing with the bad benchmark line. Our approach obtain common set of weights to create the best efficiency score, such that the amount of DMUs that are efficient is less than that of other models. If we have more than one efficient DMU, we can rank them by the same model and it isn't necessary to use another ranking method.
Data envelopment analysis (DEA),Common Weights Analysis (CWA),Ranking,The bad benchmark
http://ijdea.srbiau.ac.ir/article_14729.html
http://ijdea.srbiau.ac.ir/article_14729_bae49bd2a77107ad0e625f27bb113636.pdf
International Journal of Data Envelopment Analysis
2345-458X
7
1
2019
01
01
Technically Efficient Targets for the Groups by Using the Centralized Scenario and Enhanced Russell Measure
11
30
EN
Reza
Fallahnejad
Departeman of math,Islamic Azad university Khorram-Abad , Iran
r.fallahnejad@gmail.com
Cooper et al. [Efficiency aggregation with enhanced Russell measures in data envelopment analysis, Socio-Economic Planning Sciences, 41 (2007) 1–21] presented a method for measuring aggregate efficiency, using the enhanced Russell measure. In that paper, they posed questions and opened the way for new opportunities for studying and extending the proposed method and some other related fields. One of these issues is the extension of the proposed method in the case where there is the possibility of reallocation among the units in order to improve group efficiency. In this paper, we propose a model for evaluating the group efficiency, and employ the centralized scenario to set targets for each unit as well as for the group.
Data Envelopment Analysis,Aggregation,Target Setting,Enhanced Russell Measure
http://ijdea.srbiau.ac.ir/article_14730.html
http://ijdea.srbiau.ac.ir/article_14730_30136f946d495ed563f239132f80f2bd.pdf
International Journal of Data Envelopment Analysis
2345-458X
7
1
2019
01
01
Evaluating the efficiency of bank branches with random data
31
42
EN
Maryam
Ghashami
Departement of mathematics, Science and research branch, IAU, Tehran, Iran.
m.ghashami94@gmail.com
Farhad
Hosseinzadeh lotfi
Department of Mathematics,Science and Research Branch, IAU, Tehran, Iran
farhad@hosseinzadeh.ir
Data Envelopment Analysis (DEA) is a mathematic technique to evaluate the relative efficiency of a group of homogeneous decision making units (DMUs) with multiple inputs and outputs. The efficiency of each unit is measured based on its distance to the production possibility set (PPS). In this paper, the BCC model is used in output-oriented. The average return on profit as output and the covariance of profit (risk) are considered as inputs. In the continuation, the median and the mod earned investment as two factors of output to the model presented to provide a better analysis of the types of investment, and finally, let us mention a true example
Data envelopment analysis (DEA),Stock Portfolio,Model Orientation,Average Variance,Risk
http://ijdea.srbiau.ac.ir/article_14731.html
http://ijdea.srbiau.ac.ir/article_14731_397d465d68c4d24577760d39dabc37fa.pdf
International Journal of Data Envelopment Analysis
2345-458X
7
1
2019
01
01
Robust Portfolio Optimization with risk measure CVAR under MGH distribution in DEA models
43
56
EN
Morteza
Robatjazi
financial mathematics,mathematics, Allameh tabataba'i, Tehran,Iran
mortezarobatjazi.77@gmail.com
Shokoofeh
Banihashemi
Allameh Tabatabai&#039;i University
shbanihashemi@atu.ac.ir
Navideh
Modarresi
mathematics,facully of mathemaics and computer science, Allameh tabataba'i Univercity,Tehran,iran
namomath@aut.ac.ir
Financial returns exhibit stylized facts such as leptokurtosis, skewness and heavy-tailness. Regarding this behavior, in this paper, we apply multivariate generalized hyperbolic (mGH) distribution for portfolio modeling and performance evaluation, using conditional value at risk (CVaR) as a risk measure and allocating best weights for portfolio selection. Moreover, a robust portfolio optimization and performance evaluation modeling in mGH framework are developed, using worst case CVaR (WCVaR) as a risk measure. Due to the fact that expected returns can take negative values, the introduced model is inspired by Range Directional Measure model. Finally, real data in Iran stock market are given to illustrate the effectiveness of the model.
portfolio optimization,multivariate generalized hyperbolic distribution,Efficiency,Worst Case Conditional Value at Risk
http://ijdea.srbiau.ac.ir/article_14756.html
http://ijdea.srbiau.ac.ir/article_14756_dd1e952921ffb9f413ffc06245a8b40a.pdf
International Journal of Data Envelopment Analysis
2345-458X
7
1
2019
01
01
A Novel Efficiency Ranking Approach Based on Goal Programming and Data Envelopment Analysis for the Evaluation of Iranian Banks
57
80
EN
Mojtaba
Nouri
Department of Industrial Engineering, University of Science and Technology, Tehran, Iran
Emran
Mohammadi
Department of Industrial Engineering, University of Science and Technology, Tehran, Iran
Mohammad
Rahmanipour
Department of Progress Engineering, University of Science and Technology, Tehran, Iran
<span lang="EN">In the Iranian economy, banks play a key role in financing and developing the capital market.</span><span lang="EN">Therefore, it is important to evaluate the performance of stock banks.</span><span> Data Envelopment Analysis (DEA) is a wide range of mathematical models used to measure the relative efficiency for a set of homogeneous decision-making units with similar inputs and outputs.</span><span lang="EN"> In this paper, a novel efficiency ranking approach is proposed with two flexible mixed </span><span>models derived </span><span lang="EN">from Goal Programming </span><span>Data Envelopment Analysis (GPDEA) models.To solve this mismatch, we use Gantt chart to show DMUs’ floating ranking and use another model to appoint the ranks exactly. In this paper, we analyze 18 stocks efficiency from bank industry of Tehran Exchange in 2016 using the GPDEA approach. Results demonstrate that the novel efficiency ranking approach has higher ability than the basic models in efficiency ranking</span>
Bank,Data Envelopment Analysis,Efficiency,Goal Programming
http://ijdea.srbiau.ac.ir/article_14923.html
http://ijdea.srbiau.ac.ir/article_14923_133e20bbce8d40be4cacdaf12e92836b.pdf
International Journal of Data Envelopment Analysis
2345-458X
7
1
2019
01
01
Efficiency of engineering graduate programs in Brazil
81
110
EN
Naijela Janaina
Silveira da Costa
Federal University of S&atilde;o Carlos
naijelajanaina@gmail.com
Enzo
Barberio
Mariano
Professor at the Paulista State University Júlio de Mesquita Neto - UNESP / Bauru.
enzo.mariano@gmail.com
Herick
Fernando
Moralles
Professor at Federal University of São Carlos - UFSCar.
herickmoralles@dep.ufscar.br
The efficiency of graduate programs is directly linked to a country's capacity for innovation, which entails the need to diagnose the causes of low academic performance, as well as the development of techniques and methods to evaluate and measure the performance of educational units. Thus, the aim of this research is to analyze the efficiency of Brazilian graduate programs using Multiple Regression and Data Envelopment Analysis tools from 2014. In order to do this, a specific area was selected, called Engineering III, which includes production, mechanical, industrial and aerospace engineering programs. The results of this research can contribute to a better understanding of the dynamics and determining factors of the national academic production in order to generate knowledge concerning graduate programs, especially courses that did not meet the technical production efficiency standards required by the Coordination for the Improvement of Higher Education Personnel (CAPES), an organ responsible for graduate studies in Brazil. By analysis, there is a need to reposition the CAPES evaluation process in terms of the variables to be considered, as well as the criteria applied for this.
Data Envelopment Analysis,Regression Analysis,Efficiency,Higher Education
http://ijdea.srbiau.ac.ir/article_14998.html
http://ijdea.srbiau.ac.ir/article_14998_e71b6be98e9ab141099582f1e807e7b3.pdf