Comprehensive Cross-Efficiency Methods with Common Weight Restrictions in Data Envelopment Analysis

Considering both self-evaluation and peer evaluation, the traditional data envelopment analysis (DEA) cross-efficiency method has been widely used to evaluate efficiency scores. However, it has several defects such as excessive weight flexibility, unstable evaluation, and aggregation irrationality. This paper proposes a novel comprehensive DEA cross-efficiency method where two novel weight restriction methods are used to enhance the stability and feasibility of evaluation. Then, final efficiency scores were calculated through the geometric mean aggregation method. Finally, an empirical example is used to demonstrate that the proposed methods are more reasonable and scientific.

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Weight Restrictions and Free Production in Data Envelopment Analysis

Operations Research ◽

10.1287/opre.1120.1122 ◽

2013 ◽

Vol 61 (2) ◽

pp. 426-437 ◽

Cited By ~ 33

Author(s):

Victor V. Podinovski ◽

Tatiana Bouzdine-Chameeva

Keyword(s):

Data Envelopment Analysis ◽

Data Envelopment ◽

Weight Restrictions

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Weight restrictions in Data Envelopment Analysis: A comprehensive Genetic Algorithm based approach for incorporating value judgments

Expert Systems with Applications ◽

10.1016/j.eswa.2014.09.034 ◽

2015 ◽

Vol 42 (3) ◽

pp. 1503-1512 ◽

Cited By ~ 18

Author(s):

Vipul Jain ◽

Amit Kumar ◽

Sameer Kumar ◽

Charu Chandra

Keyword(s):

Genetic Algorithm ◽

Data Envelopment Analysis ◽

Data Envelopment ◽

Weight Restrictions ◽

Value Judgments

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A New Look at Selecting Third-Party Reverse Logistics Providers

Information Technologies, Methods, and Techniques of Supply Chain Management ◽

10.4018/978-1-4666-0918-1.ch014 ◽

2012 ◽

pp. 246-254

Author(s):

Reza Farzipoor Saen

Keyword(s):

Decision Making ◽

Data Envelopment Analysis ◽

Reverse Logistics ◽

Third Party ◽

Data Envelopment ◽

Weight Restrictions ◽

Use Of Data ◽

Available Information ◽

Nondiscretionary Factors ◽

Decision Making Criteria

The use of Data Envelopment Analysis (DEA) in many fields is based on total flexibility of the weights. However, the problem of allowing total flexibility of the weights is that the values of the weights obtained by solving the unrestricted DEA program are often in contradiction to prior views or additional available information. Also, many applications of DEA assume complete discretionary of decision making criteria. However, they do not assume the conditions that some factors are nondiscretionary. To select the most efficient third-party reverse logistics (3PL) provider in the conditions that both weight restrictions and nondiscretionary factors are present, a methodology is introduced. A numerical example demonstrates the application of the proposed method.

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Production trade-offs and weight restrictions in two-stage network data envelopment analysis

International Journal of Applied Decision Sciences ◽

10.1504/ijads.2020.104304 ◽

2020 ◽

Vol 13 (1) ◽

pp. 21

Author(s):

Hamid Kiaei ◽

Reza Kazemi Matin ◽

Seyed Hadi Nasseri

Keyword(s):

Data Envelopment Analysis ◽

Network Data ◽

Data Envelopment ◽

Weight Restrictions ◽

Two Stage ◽

Network Data Envelopment Analysis ◽

Trade Offs

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Using Data Envelopment Analysis and Multi-Criteria Decision-Making Methods to Evaluate Teacher Performance in Higher Education

Symmetry ◽

10.3390/sym12040563 ◽

2020 ◽

Vol 12 (4) ◽

pp. 563 ◽

Cited By ~ 4

Author(s):

Milena Popović ◽

Gordana Savić ◽

Marija Kuzmanović ◽

Milan Martić

Keyword(s):

Higher Education ◽

Decision Making ◽

Data Envelopment Analysis ◽

Conjoint Analysis ◽

Data Envelopment ◽

Efficiency Evaluation ◽

Multi Criteria Decision Making ◽

Weight Restrictions ◽

Analytic Hierarchy ◽

Discrimination Power

This paper proposes an approach that combines data envelopment analysis (DEA) with the analytic hierarchy process (AHP) and conjoint analysis, as multi-criteria decision-making methods to evaluate teachers’ performance in higher education. This process of evaluation is complex as it involves consideration of both objective and subjective efficiency assessments. The efficiency evaluation in the presence of multiple different criteria is done by DEA and results heavily depend on their selection, values, and the weights assigned to them. Objective efficiency evaluation is data-driven, while the subjective efficiency relies on values of subjective criteria usually captured throughout the survey. The conjoint analysis helps with the selection and determining the relative importance of such criteria, based on stakeholder preferences, obtained as an evaluation of experimentally designed hypothetical profiles. An efficient experimental design can be either symmetric or asymmetric depending on the structure of criteria covered by the study. Obtained importance might be a guideline for selecting adequate input and output criteria in the DEA model when assessing teachers’ subjective efficiency. Another reason to use conjoint preferences is to set a basis for weight restrictions in DEA and consequently to increase its discrimination power. Finally, the overall teacher’s efficiency is an AHP aggregation of subjective and objective teaching and research efficiency scores. Given the growing competition in the field of education, a higher level of responsibility and commitment is expected, and it is therefore helpful to identify weaknesses so that they can be addressed. Therefore, the evaluation of teachers’ efficiency at the University of Belgrade, Faculty of Organizational Sciences illustrates the usage of the proposed approach. As results, relatively efficient and inefficient teachers were identified, the reasons and aspects of their inefficiency were discovered, and rankings were made.

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