Inverse Dynamic Data Envelopment Analysis for Evaluating Faculties of University with Quasi-Fixed Inputs

2019 ◽  
Vol 148 (1) ◽  
pp. 323-347
Author(s):  
Samira Foladi ◽  
Maghsud Solimanpur ◽  
Mustafa Jahangoshai Rezaee
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Inverse Dynamic ◽  
Dynamic Data ◽  
Dynamic Data Envelopment Analysis

Evaluating and ranking sustainable suppliers by robust dynamic data envelopment analysis

Measurement ◽  
2016 ◽  
Vol 83 ◽  
pp. 72-85 ◽  
Author(s):  
Saeed Yousefi ◽  
Hadi Shabanpour ◽  
Ron Fisher ◽  
Reza Farzipoor Saen
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Dynamic Data ◽  
Dynamic Data Envelopment Analysis

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.

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