scholarly journals A Cross-Efficiency Evaluation Method Based on Evaluation Criteria Balanced on Interval Weights

Symmetry ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1503 ◽  
Author(s):  
Hailiu Shi ◽  
Yingming Wang ◽  
Xiaoming Zhang
Keyword(s):  
Evaluation Method ◽  
Evaluation Criteria ◽  
Evaluation Criterion ◽  
Peer Evaluation ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Common Set Of Weights ◽  
The Cross ◽  
The Difference ◽  
Evaluation Approaches

Cross-efficiency evaluation approaches and common set of weights (CSW) approaches have long been suggested as two of the more important and effective methods for the ranking of decision making units (DMUs) in data envelopment analysis (DEA). The former emphasizes the flexibility of evaluation and its weights are asymmetric, while the latter focuses on the standardization of evaluation and its weights are symmetrical. As a compromise between these two approaches, this paper proposes a cross-efficiency evaluation method that is based on two types of flexible evaluation criteria balanced on interval weights. The evaluation criteria can be regarded as macro policy—or means of regulation—according to the industry’s current situation. Unlike current cross-efficiency evaluation methods, which tend to choose the set of weights for peer evaluation based on certain preferences, the cross-efficiency evaluation method based on evaluation criterion determines one set of input and output weights for each DMU. This is done by minimizing the difference between the weights of the DMU and the evaluation criteria, thus ensuring that the cross-evaluation of all DMUs for evaluating peers is as consistent as possible. This method also eliminates prejudice and arbitrariness from peer evaluations. As a result, the proposed cross-efficiency evaluation method not only looks for non-zero weights, but also ranks efficient DMUs completely. The proposed DEA model can be further extended to seek a common set of weights for all DMUs. Numerical examples are provided to illustrate the applications of the cross-efficiency evaluation method based on evaluation criterion in DEA ranking.

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 An Entropy-Based Cross-Efficiency under Variable Returns to Scale

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1205
Author(s):  
Chun-Hsiung Su ◽  
Tim Lu
Keyword(s):  
Evaluation Method ◽  
Returns To Scale ◽  
Peer Evaluation ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Dea Models ◽  
Constant Returns To Scale ◽  
Numerical Index ◽  
Variable Returns To Scale ◽  
Decision Making Units

Cross-efficiency evaluation is an effective methodology for discriminating among a set of decision-making units (DMUs) through both self- and peer-evaluation methods. This evaluation technique is usually used for data envelopment analysis (DEA) models with constant returns to scale due to the fact that negative efficiencies never happen in this case. For cases of variable returns to scale (VRSs), the evaluation may generate negative cross-efficiencies. However, when the production technology is known to be VRS, a VRS model must be used. In this case, negative efficiencies may occur. Negative efficiencies are unreasonable and cause difficulties in calculating the final cross-efficiency. In this paper, we propose a cross-efficiency evaluation method, with the technology of VRS. The cross-efficiency intervals of DMUs were derived from the associated aggressive and benevolent formulations. More importantly, the proposed approach does not produce negative efficiencies. For comparison of DMUs with their cross-efficiency intervals, a numerical index is required. Since the concept of entropy is an effective tool to measure the uncertainty, this concept was employed to build an index for ranking DMUs with cross efficiency intervals. A real-case example was used to illustrate the approach proposed in this paper.

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.

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 Game Performance Versus Competitive Performance in the World Championship of Handball 2011

2013 ◽  
Vol 36 (1) ◽  
pp. 137-147 ◽  
Author(s):  
Óscar Gutiérrez ◽  
José L. Ruiz
Keyword(s):  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Competitive Performance ◽  
World Championship ◽  
The World ◽  
Overall Performance ◽  
The Cross ◽  
Using Data ◽  
World Champion ◽  
Game Performance

This article assesses the game performance of the teams participating in the Men’s World Championship of Handball of 2011 by using Data Envelopment Analysis (DEA) and the cross-efficiency evaluation. DEA uses Linear Programming to yield a measure of the overall performance of the game of particular teams, and allows us to identify relative strengths and weaknesses by means of benchmarking analysis. The cross-efficiency evaluation provides a peerappraisal of the teams with different patterns of game, and makes it possible to rank them. Comparisons between this ranking and the final classification in the championship provide an insight into the game performance of the teams versus their competitive performance. We highlight the fact that France, which is the world champion, is also identified as an “all-round” performer in our game performance assessment.

scholarly journals Selecting the Best of Portfolio Using OWA Operator Weights in Cross Efficiency-Evaluation

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Masoud Sanei ◽  
Shokoofeh Banihashemi
Keyword(s):  
Evaluation Process ◽  
Efficiency Score ◽  
Weighted Averaging ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Owa Operator ◽  
Public And Private ◽  
The Cross ◽  
Asset Selection ◽  
Public And Private Sector

The present study is an attempt toward evaluating the performance of portfolios and asset selection using cross-efficiency evaluation. Cross-efficiency evaluation is an effective way of ranking decision making units (DMUs) in data envelopment analysis (DEA). The most widely used approach is to evaluate the efficiencies in each row or column in the cross-efficiency matrix with equal weights into an average cross-efficiency score for each DMU and consider it as the overall performance measurement of the DMU. This paper focuses on the evaluation process of the efficiencies in the cross-efficiency matrix and proposes the use of ordered weighted averaging (OWA) operator weights for cross-efficiency evaluation. The OWA operator weights are generated by the minimax disparity approach and allow the decision maker (DM) or investor to select the best assets that are characterized by an orness degree. The problem consists of choosing an optimal set of assets in order to minimize the risk and maximize return. This method is illustrated by application in mutual funds and weights are obtained via OWA operator for making the best portfolio. The finding could be used for constructing the best portfolio in stock companies, in various finance organization, and public and private sector companies.

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 Common set of weights and efficiency improvement on the basis of separation vector in two-stage network data envelopment analysis

2019 ◽  
Vol 14 (1) ◽  
pp. 53-65
Author(s):  
Hamid Kiaei ◽  
Reza Kazemi Matin
Keyword(s):  
Network Data ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Efficiency Improvement ◽  
Ranking Methods ◽  
Two Stage ◽  
Black Boxes ◽  
Dea Models ◽  
Common Set Of Weights ◽  
Single Objective

AbstractCommon set of weights (CSWs) method is one of the popular ranking methods in DEA which can rank efficient and inefficient units. Based on an identical criterion, the method selects the most favorable weight set for all units. An important issue is that in most common DEA models, the internal structure of the production units is ignored and the units are often considered as black boxes. In this paper, in order to evaluate the units and subunits in the two-stage NDEA based on an identical criterion, it is suggested to use CSWs method on the basis of separation vector. Our research contribution in this paper includes: (1) CSWs method is formulated in two-stage NDEA as a multiple objective fractional programming (MOFP) problem. (2) A method is suggested based on separation vector to change MOFP problem into single objective linear programming (SOLP) problem in two-stage NDEA. In the theorem, it is shown that the obtained solutions from MOFP and SOLP in two-stage NDEA are identical. (3) In the framework of the new models of two-stage NDEA, a process is introduced to improve efficiency evaluation by CSWs on the basis of separation vector which is based on the radial improvement of inputs and final outputs. Finally, an enlightening application is presented.

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