scholarly journals Cross-Efficiency Evaluation Method with Compete-Cooperate Matrix

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Qiang Hou ◽  
Xue Zhou
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Evaluation Method ◽  
Peer Assessment ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Self Assessment ◽  
Dea Method ◽  
Decision Making Units

Cross-efficiency evaluation method is an effective and widespread adopted data envelopment analysis (DEA) method with self-assessment and peer-assessment to evaluate and rank decision making units (DMUs). Extant aggressive, benevolent, and neutral cross-efficiency methods are used to evaluate DMUs with competitive, cooperative, and nontendentious relationships, respectively. In this paper, a symmetric (nonsymmetric) compete-cooperate matrix is introduced into aggressive and benevolent cross-efficiency methods and compete-cooperate cross-efficiency method is proposed to evaluate DMUs with diverse (relative) relationships. Deviation maximization method is applied to determine the final weights of cross-evaluation to enhance the differentiation ability of cross-efficiency evaluation method. Numerical demonstration is provided to illustrate the reasonability and practicability of the proposed method.

scholarly journals Gap Minimization for Peer-Evaluation in DEA Cross-Efficiency

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Anrong Yang ◽  
Zigang Zhang ◽  
Yishi Zhang ◽  
Dunliang Chen
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Peer Evaluation ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Self Evaluation ◽  
Criterion A ◽  
The Cross ◽  
Similarity Distance ◽  
Decision Making Units

Cross-efficiency evaluation is an effective and widely used method for ranking decision making units (DMUs) in data envelopment analysis (DEA). Gap minimization criterion is introduced in aggressive and benevolent cross-efficiency methods to avoid possible extreme efficiency from peer-evaluation and to get equitable results. On the basis of this criterion, a weighted cross-efficiency method with similarity distance that, respectively, considers the aggressive and the benevolent formulations is proposed to determine cross-efficiency. The weights of the cross-evaluation determined by this method are positively influenced by self-evaluation and thus are propitious to resolving conflict. Numerical demonstration reveals the feasibility of the proposed method.

scholarly journals Identification and Prioritization of Hazardous Road Locations by Segmentation and Data Envelopment Analysis Approach

1970 ◽  
Vol 25 (2) ◽  
pp. 127-136 ◽  
Author(s):  
Aliasghar Sadeghi ◽  
Esmaeel Ayati ◽  
Mohammadali Pirayesh Neghab
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Regression Models ◽  
Data Envelopment ◽  
The Road ◽  
Black Spots ◽  
Main Factors ◽  
Dea Method ◽  
Decision Making Units

The aim of the present study is the representation of a method to identify and prioritize accident-prone sections (APSs) based upon efficiency concept to emphasize accidents with regard to traffic, geometric and environmental circumstances of road which can consider the interaction of accidents as well as their casual factors. This study incorporates the segmentation procedure into data envelopment analysis (DEA) technique which has no requirement of distribution function and special assumptions, unlike the regression models. A case study has been done on 144.4km length of Iran roads to describe the approach. Eleven accident-prone sections were identified among 154 sections obtained from the segmentation process and their prioritization was made based on the inefficiency values coming from DEA method. The comparisons demonstrated that the frequency and severity of accidents would not be only considered as the main factors for black-spots identification but proper rating can be possible by obtaining inefficiency values from this method for the road sections. This approach could applicably offer decision-making units for identifying accident-prone sections and their prioritizations. Also, it can be used to prioritize intersections, roundabouts or the total roads of the safety organization domain.

scholarly journals The basic solution ellipsoid method approach for the efficiency measurement problems

2016 ◽  
Vol 57 ◽  
Author(s):  
Eligijus Laurinavičius ◽  
Daiva Rimkuvienė ◽  
Aurelija Sakalauskaitė
Keyword(s):  
Decision Making ◽  
Linear Programming ◽  
Data Envelopment Analysis ◽  
Basic Solution ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Parametric Approach ◽  
Ellipsoid Method ◽  
Method Approach ◽  
Decision Making Units

The efficiency is a measure of a performance of a decision making units (DMUs can be a firm, a person, an organization). The data envelopment analysis (DEA) is a datadriven non-parametric approach for measuring the efficiency of a set of DMUs. The DEA is a linear programming (LP) based technique which deals with the basic models (CCR, BCC, SBM, additive) of the efficiency evaluation. This paper presents basic solution ellipsoid method approach associated with some problems of initial basic solution and the steps of it.

scholarly journals A multiobjective approach for non-discretionary variables in data envelopment analysis

DYNA ◽  
2016 ◽  
Vol 83 (195) ◽  
pp. 9-15 ◽  
Author(s):  
Lidia Angulo Meza ◽  
João Carlos Soares de Mello ◽  
Silvio Gomes Junior
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Decision Maker ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Data Set ◽  
Numerical Example ◽  
Multiobjective Approach ◽  
Ratio Optimization ◽  
Decision Making Units

Data Envelopment Analysis is a non-parametrical approach for efficiency evaluation of so-called DMUs (Decision Making Units) and takes into account multiple inputs and outputs. For each inefficient DMU, a target is provided which is constituted by the inputs or outputs levels that are to be attained for the inefficient DMU to become efficient. However, multiobjective models, known as MORO (Multiobjective Model for Ratio Optimization) provide a set of targets for inefficient DMU, which provides alternatives among which the decision-maker can choose. In this paper, we proposed an extension of the MORO models to take into account non-discretionary variables, i.e., variables that cannot be controlled. We present a numerical example to illustrate the proposed multiobjective model. We also discuss the characteristics of this model, as well as the advantages of offering a set of targets for the inefficient DMUs when there are non-discretionary variables in the data set.

A DATA ENVELOPMENT ANALYSIS (DEA) EVALUATION METHOD BASED ON SAMPLE DECISION MAKING UNITS

2010 ◽  
Vol 09 (04) ◽  
pp. 601-624 ◽  
Author(s):  
QUANLING WEI ◽  
HONG YAN
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Evaluation Method ◽  
Evaluation Procedure ◽  
Data Envelopment ◽  
Training Set ◽  
Production Possibility ◽  
Original Definition ◽  
Production Possibility Set ◽  
Decision Making Units

Most of evaluation methods on large number of candidates are based a single criterion. To bring the multiple attribute evaluation method Data Envelopment Analysis (DEA) into evaluating large number of elements, it needs to set up the performance standards and an evaluation procedure by the DEA model. In this paper, we first determine a set of "standard" candidates, called in decision making units (DMUs) in the DEA terminology. This standard set is called "training set". We then establish the evaluation procedure based on this "training set" for measuring a large number of DMUs. We first investigate the efficiency evaluation of a new DMU along with the original definition based on the sum formed production possibility set which is formed by the n DMUs in the training set and the new DMU. We then identify the intersection form of the production possibility set formed only by the n DMUs from the training set. And show that the new DMU evaluation is simply to check if the DMU satisfies a set of linear inequalities. The intersection formed production possibility set formed by the n DMUs from the training set is fixed for evaluating any new DMU. Therefore, it provides an efficient and effective method for dealing with a large amount of data. The method can be regarded as a complementary approach for data mining.

Gangless cross-evaluation in DEA: an application to stock selection

2019 ◽  
Vol 53 (2) ◽  
pp. 645-655 ◽  
Author(s):  
Gholam R. Amin ◽  
Amar Oukil
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Stock Market ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Stock Selection ◽  
Evaluation Approach ◽  
The Cross ◽  
The Impact ◽  
Decision Making Units

This paper discusses the impact of ganging decision making units (DMUs) on the cross-efficiency evaluation in data envelopment analysis (DEA). A group of DMUs are said to be ganging-together if the minimum and the maximum cross-efficiency scores they give to all other DMUs are identical. This study demonstrates that the ganging phenomenon can significantly influence the cross-efficiency evaluation in favour of some DMUs. To overcome this shortcoming, we propose a gangless cross-efficiency evaluation approach. The suggested method reduces the effect of ganging and generates a more diversified list of top performing units. An application to the Tehran stock market is used to show the benefits of gangless cross-evaluation.

scholarly journals Hybrid cross-efficiency approach based on Ideal and Anti-Ideal points and the CRITIC method for ranking decision-making units: A case study on ranking the methods of rice weevil disinfestation

2021 ◽  
Vol 10 (3) ◽  
pp. 375-392
Author(s):  
Pariwat Nasawat ◽  
Sukangkana Talangkun ◽  
Sirawadee Arunyanart ◽  
Narong Wichapa
Keyword(s):  
Decision Making ◽  
Evaluation Method ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
New Approach ◽  
Comprehensive Method ◽  
Ideal Points ◽  
The Cross ◽  
Decision Making Units

A new approach is applied in the process of measuring the efficiency of decision-making units (DMUs) through the cross-efficiency evaluation method. Ideal and Anti-Ideal models are generated to form a comprehensive method based on the cross-efficiency evaluation method. The two models are formulated and combined to the Data Envelopment Analysis using the CRITIC method. In a comparative analysis based on three numerical examples, the proposed approach can lead to achieving a more reliable result than one based on an individual method.

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

Sign in / Sign up

Export Citation Format

Share Document