ORIGINAL_ARTICLE
Ranking of units by anti-ideal DMU with common weights
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. 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.
http://ijdea.srbiau.ac.ir/article_14729_bae49bd2a77107ad0e625f27bb113636.pdf
2019-01-01T11:23:20
2020-05-27T11:23:20
1
10
Data envelopment analysis (DEA)
Common Weights Analysis (CWA)
Ranking
The bad benchmark
Masoumeh
Khanmohammadi
kh_khanmohamady@yahoo.com
true
1
Islamic Azad University, Islamshahr Branch
Islamic Azad University, Islamshahr Branch
Islamic Azad University, Islamshahr Branch
LEAD_AUTHOR
Maryam
Davaei Far
d.davaeifar@yahoo.com
true
2
Sama technical and vocational training college, Dezfoul Branch, Islamic Azad University, Dezfoul, Iran
Sama technical and vocational training college, Dezfoul Branch, Islamic Azad University, Dezfoul, Iran
Sama technical and vocational training college, Dezfoul Branch, Islamic Azad University, Dezfoul, Iran
AUTHOR
ORIGINAL_ARTICLE
Technically Efficient Targets for the Groups by Using the Centralized Scenario and Enhanced Russell Measure
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.
http://ijdea.srbiau.ac.ir/article_14730_30136f946d495ed563f239132f80f2bd.pdf
2019-01-01T11:23:20
2020-05-27T11:23:20
11
30
Data Envelopment Analysis
Aggregation
Target Setting
Enhanced Russell Measure
Reza
Fallahnejad
r.fallahnejad@gmail.com
true
1
Departeman of math,Islamic Azad university Khorram-Abad , Iran
Departeman of math,Islamic Azad university Khorram-Abad , Iran
Departeman of math,Islamic Azad university Khorram-Abad , Iran
LEAD_AUTHOR
ORIGINAL_ARTICLE
Evaluating the efficiency of bank branches with random data
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
http://ijdea.srbiau.ac.ir/article_14731_397d465d68c4d24577760d39dabc37fa.pdf
2019-01-01T11:23:20
2020-05-27T11:23:20
31
42
Data envelopment analysis (DEA)
Stock Portfolio
Model Orientation
Average Variance
Risk
Maryam
Ghashami
m.ghashami94@gmail.com
true
1
Departement of mathematics, Science and research branch, IAU, Tehran, Iran.
Departement of mathematics, Science and research branch, IAU, Tehran, Iran.
Departement of mathematics, Science and research branch, IAU, Tehran, Iran.
AUTHOR
Farhad
Hosseinzadeh lotfi
farhad@hosseinzadeh.ir
true
2
Department of Mathematics,Science and Research Branch, IAU, Tehran, Iran
Department of Mathematics,Science and Research Branch, IAU, Tehran, Iran
Department of Mathematics,Science and Research Branch, IAU, Tehran, Iran
LEAD_AUTHOR
ORIGINAL_ARTICLE
Robust Portfolio Optimization with risk measure CVAR under MGH distribution in DEA models
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.
http://ijdea.srbiau.ac.ir/article_14756_dd1e952921ffb9f413ffc06245a8b40a.pdf
2019-01-01T11:23:20
2020-05-27T11:23:20
43
56
portfolio optimization
multivariate generalized hyperbolic distribution
Efficiency
Worst Case Conditional Value at Risk
Morteza
Robatjazi
mortezarobatjazi.77@gmail.com
true
1
financial mathematics,mathematics, Allameh tabataba'i, Tehran,Iran
financial mathematics,mathematics, Allameh tabataba'i, Tehran,Iran
financial mathematics,mathematics, Allameh tabataba'i, Tehran,Iran
LEAD_AUTHOR
Shokoofeh
Banihashemi
shbanihashemi@atu.ac.ir
true
2
Allameh Tabatabai&#039;i University
Allameh Tabatabai&#039;i University
Allameh Tabatabai&#039;i University
AUTHOR
Navideh
Modarresi
namomath@aut.ac.ir
true
3
mathematics,facully of mathemaics and computer science, Allameh tabataba'i Univercity,Tehran,iran
mathematics,facully of mathemaics and computer science, Allameh tabataba'i Univercity,Tehran,iran
mathematics,facully of mathemaics and computer science, Allameh tabataba'i Univercity,Tehran,iran
AUTHOR
ORIGINAL_ARTICLE
A Novel Efficiency Ranking Approach Based on Goal Programming and Data Envelopment Analysis for the Evaluation of Iranian Banks
In the Iranian economy, banks play a key role in financing and developing the capital market.Therefore, it is important to evaluate the performance of stock banks. 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. In this paper, a novel efficiency ranking approach is proposed with two flexible mixed models derived from Goal Programming 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
http://ijdea.srbiau.ac.ir/article_14923_133e20bbce8d40be4cacdaf12e92836b.pdf
2019-01-01T11:23:20
2020-05-27T11:23:20
57
80
Bank
Data Envelopment Analysis
Efficiency
Goal Programming
Mojtaba
Nouri
true
1
Department of Industrial Engineering, University of Science and Technology, Tehran, Iran
Department of Industrial Engineering, University of Science and Technology, Tehran, Iran
Department of Industrial Engineering, University of Science and Technology, Tehran, Iran
AUTHOR
Emran
Mohammadi
true
2
Department of Industrial Engineering, University of Science and Technology, Tehran, Iran
Department of Industrial Engineering, University of Science and Technology, Tehran, Iran
Department of Industrial Engineering, University of Science and Technology, Tehran, Iran
AUTHOR
Mohammad
Rahmanipour
true
3
Department of Progress Engineering, University of Science and Technology, Tehran, Iran
Department of Progress Engineering, University of Science and Technology, Tehran, Iran
Department of Progress Engineering, University of Science and Technology, Tehran, Iran
AUTHOR
ORIGINAL_ARTICLE
Efficiency of engineering graduate programs in Brazil
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.
http://ijdea.srbiau.ac.ir/article_14998_e71b6be98e9ab141099582f1e807e7b3.pdf
2019-01-01T11:23:20
2020-05-27T11:23:20
81
110
Data Envelopment Analysis
Regression Analysis
Efficiency
Higher Education
Naijela Janaina
Silveira da Costa
naijelajanaina@gmail.com
true
1
Federal University of S&atilde;o Carlos
Federal University of S&atilde;o Carlos
Federal University of S&atilde;o Carlos
LEAD_AUTHOR
Enzo
Mariano
enzo.mariano@gmail.com
true
2
Professor at the Paulista State University Júlio de Mesquita Neto - UNESP / Bauru.
Professor at the Paulista State University Júlio de Mesquita Neto - UNESP / Bauru.
Professor at the Paulista State University Júlio de Mesquita Neto - UNESP / Bauru.
AUTHOR
Herick
Moralles
herickmoralles@dep.ufscar.br
true
3
Professor at Federal University of São Carlos - UFSCar.
Professor at Federal University of São Carlos - UFSCar.
Professor at Federal University of São Carlos - UFSCar.
AUTHOR