Qualitative and Quantitative Data Envelopment Analysis with Interval Data

2010 ◽  
pp. 163-174 ◽  
Author(s):  
Masahiro Inuiguchi ◽  
Fumiki Mizoshita
Keyword(s):  
Data Envelopment Analysis ◽  
Quantitative Data ◽  
Interval Data ◽  
Data Envelopment ◽  
Qualitative And Quantitative

Selecting Six Sigma projects with interval data envelopment analysis

2013 ◽  
Vol 2 (2) ◽  
pp. 138 ◽  
Author(s):  
Alireza Alinezhad ◽  
Nushin Samadi Bahrami ◽  
Abolfazl Kazemi ◽  
Keyvan Sarrafha
Keyword(s):  
Data Envelopment Analysis ◽  
Six Sigma ◽  
Interval Data ◽  
Data Envelopment

scholarly journals A Distribution-Free Approach to Stochastic Efficiency Measurement with Inclusion of Expert Knowledge

2013 ◽  
Vol 2013 ◽  
pp. 1-21 ◽  
Author(s):  
Kerry Khoo-Fazari ◽  
Zijiang Yang ◽  
Joseph C. Paradi
Keyword(s):  
Data Envelopment Analysis ◽  
Quantitative Data ◽  
Expert Knowledge ◽  
Simulated Dataset ◽  
Data Envelopment ◽  
Nonparametric Modeling ◽  
Distribution Free ◽  
New Frontier ◽  
Distribution Free Approach ◽  
Computationally Intensive

This paper proposes a new efficiency benchmarking methodology that is capable of incorporating probability while still preserving the advantages of a distribution-free and nonparametric modeling technique. This new technique developed in this paper will be known as the DEA-Chebyshev model. The foundation of DEA-Chebyshev model is based on the model pioneered by Charnes, Cooper, and Rhodes in 1978 known as Data Envelopment Analysis (DEA). The combination of normal DEA with DEA-Chebyshev frontier (DCF) can successfully provide a good framework for evaluation based on quantitative data and qualitative intellectual management knowledge. The simulated dataset was tested on DEA-Chebyshev model. It has been statistically shown that this model is effective in predicting a new frontier, whereby DEA efficient units can be further differentiated and ranked. It is an improvement over other methods, as it is easily applied, practical, not computationally intensive, and easy to implement.

scholarly journals Modified Fuzzy Data Envelopment Analysis Models

2020 ◽  
Vol 3 (3b) ◽  
pp. 208-221
Author(s):  
IJ DIKE
Keyword(s):  
Data Envelopment Analysis ◽  
Lower Bound ◽  
Uncertain Data ◽  
Interval Data ◽  
Data Envelopment ◽  
Or Efficiency ◽  
Input And Output ◽  
Dea Models ◽  
Relative Productivity ◽  
Fuzzy Dea

This paper examines the use of data envelopment analysis (DEA) in the conduct of efficiency measurement involving fuzzy (interval) input-output values. Data envelopment analysis is a linear programming method for comparing the relative productivity (or efficiency) of multiple service units. Standard DEA models assume crisp data for both the input and output values. In practice however, input and output values may be uncertain, vague, imprecise or incomplete. A new pair of fuzzy DEA models is presented which differs from existing fuzzy DEA models handling uncertain data. In this approach, upper bound interval data are used exclusively to obtain the upper frontier values while lower bound interval data are used exclusively to obtain the lower frontier values. The outcome, when compared with the outcome of existing approach, based on the same set of data, shows a swap in the upper and lower frontier values with exactly the same number of efficient decision making units (DMUs). This new approach therefore clears the ambiguity occasioned by the mixture of upper and lower bound values in the determination of the upper and lower frontier efficiency scores respectively.

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