scholarly journals R program for estimation of group efficiency and finding its gradient. Stochastic data envelopment analysis with a perfect object approach

Data in Brief ◽  
2018 ◽  
Vol 19 ◽  
pp. 1844-1846 ◽  
Author(s):  
Alexander Vaninsky
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
R Program ◽  
Group Efficiency ◽  
Stochastic Data

An Approach in Solving Data Envelopment Analysis with Stochastic Data

2017 ◽  
pp. 154-162 ◽  
Author(s):  
Amir Hossein Nafei ◽  
Seyed Mohammad Esmaeil Pourmohammad Azizi ◽  
Rajab Ali Ghasempour
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Stochastic Data

THE SELECTION OF PROJECTS WITHIN CONSTRAINED RESOURCES USING STOCHASTIC DATA ENVELOPMENT ANALYSIS

2018 ◽  
Vol 52 (3) ◽  
pp. 171-201
Author(s):  
Maliheh Piri ◽  
Farhad Hosseinzadeh Lotfi ◽  
Mohsen Rostamy-Malkhalifeh ◽  
Mohammad Hasan Behzadi
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Stochastic Data ◽  
Selection Of

Sample median approximation on stochastic data envelopment analysis

2020 ◽  
Vol 13 (3) ◽  
pp. 279
Author(s):  
Syahril Efendi ◽  
N.A. Sutarman ◽  
Anton Abdulbasah Kamil ◽  
Marah Doly Nasution ◽  
Herman Mawengkang
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Sample Median ◽  
Stochastic Data

scholarly journals Hesitant Fuzzy-Stochastic Data Envelopment Analysis (HF-SDEA) Model for Benchmarking

2021 ◽  
Vol 5 (1) ◽  
pp. 94
Author(s):  
Dahlan Abdullah ◽  
Hartono ◽  
Cut Ita Erliana
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Fuzzy Model ◽  
Assessment Process ◽  
Data Envelopment ◽  
Dynamic Data ◽  
Decision Making Unit ◽  
Dynamic Data Envelopment Analysis ◽  
Dea Method ◽  
Stochastic Data

The Data Envelopment Analysis (DEA) method is a method commonly used in benchmarking. The Dynamic Data Envelopment Analysis (DDEA) method was proposed to improve the DEA method in the benchmarking process. The DDEA method proposed can determine the effectiveness of the Decision Making Unit (DMU). The disadvantage of the DDEA model is that it cannot handle problems that involve benchmarking for stochastic data. To improve the DDEA method, the Stochastic Data Envelopment Analysis (SDEA) method is proposed which can be used for benchmarking involving stochastic data. The SDEA method itself has weaknesses in dealing with noise and uncertainty problems that will appear in the assessment process. The purpose of the research conducted by the researcher was to use the Hesitant Fuzzy method in optimizing the SDEA method so that the Hesitant Fuzzy model - Stochastic Data Envelopment Analysis (HF-SDEA) could be carried out benchmarking process in a situation where the assessment contained many elements of uncertainty. The results of this study are benchmarking methods that can do benchmarking for stochastic data on conditions that contain elements of uncertainty.

Fuzzy stochastic Data Envelopment Analysis with application to NATO enlargement problem

2019 ◽  
Vol 53 (2) ◽  
pp. 705-721 ◽  
Author(s):  
Ali Ebrahimnejad ◽  
Seyed Hadi Nasseri ◽  
Omid Gholami
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Threshold Values ◽  
Discrimination Power ◽  
Dea Model ◽  
Nato Enlargement ◽  
Proposed Model ◽  
Stochastic Dea ◽  
Dea Models ◽  
Stochastic Data

Data Envelopment Analysis (DEA) is a widely used technique for measuring the relative efficiencies of Decision Making Units (DMUs) with multiple deterministic inputs and multiple outputs. However, in real-world problems, the observed values of the input and output data are often vague or random. Indeed, Decision Makers (DMs) may encounter a hybrid uncertain environment where fuzziness and randomness coexist in a problem. Hence, we formulate a new DEA model to deal with fuzzy stochastic DEA models. The contributions of the present study are fivefold: (1) We formulate a deterministic linear model according to the probability–possibility approach for solving input-oriented fuzzy stochastic DEA model, (2) In contrast to the existing approach, which is infeasible for some threshold values; the proposed approach is feasible for all threshold values, (3) We apply the cross-efficiency technique to increase the discrimination power of the proposed fuzzy stochastic DEA model and to rank the efficient DMUs, (4) We solve two numerical examples to illustrate the proposed approach and to describe the effects of threshold values on the efficiency results, and (5) We present a pilot study for the NATO enlargement problem to demonstrate the applicability of the proposed model.

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