Modeling fuzzy data envelopment analysis under robust input and output data

2018 ◽  
Vol 52 (2) ◽  
pp. 619-643 ◽  
Author(s):  
Xuejie Bai ◽  
Feng Zhang ◽  
Yankui Liu
Keyword(s):  
Data Envelopment Analysis ◽  
Domain Decomposition ◽  
Decomposition Method ◽  
Domain Decomposition Method ◽  
Fuzzy Variable ◽  
Data Envelopment ◽  
Dea Model ◽  
Fuzzy Variables ◽  
Interval Valued ◽  
Inputs And Outputs

This paper offers a fuzzy optimization framework for data envelopment analysis (DEA) to evaluate the relative efficiency of decision making units (DMUs) with parametric interval-valued fuzzy variable-based inputs and outputs. The parametric interval-valued fuzzy variable-based inputs and outputs is employed to capture the uncertainty of data on the basis of professional judgements or empirical estimations. The DEA problem is formulated as fuzzy expectation model with credibility constraints. When the inputs and outputs are mutually independent parametric interval-valued triangular fuzzy variables, we investigate the parametric equivalent representations of expectation objective function and chance constraints. In order to find the optimal solution of our DEA model, a domain decomposition method is proposed. Finally, the numerical example on the sustainable supplier evaluation and selection problem is provided to demonstrate the efficiency of the proposed DEA model and domain decomposition method.

scholarly journals The Application of Data Envelopment Analysis in Healthcare Performance Evaluation of Rehabilitation Departments in Hungary

2017 ◽  
Vol 21 (3) ◽  
pp. 127 ◽  
Author(s):  
Rita Veronika Dénes ◽  
Judit Kecskés ◽  
Tamás Koltai ◽  
Zoltán Dénes
Keyword(s):  
Performance Evaluation ◽  
Data Envelopment Analysis ◽  
Healthcare System ◽  
Economies Of Scale ◽  
Musculoskeletal Diseases ◽  
Evaluation Criteria ◽  
Data Envelopment ◽  
Dea Model ◽  
Exact Amount ◽  
Inputs And Outputs

<p><strong>Purpose:</strong> Performance evaluation is a general problem both in production and service systems. Generally, operation performance is determined based on input resource utilization and on outputs related data. Performance evaluation is especially complicated when both financial and nonfinancial indicators must be considered in the evaluation of the efficiency of healthcare system. The purpose of this paper is to apply data envelopment analysis (DEA) in order to measure the efficiency of rehabilitation departments curing musculoskeletal diseases.</p><p><strong>Methodology/Approach:</strong> The evaluation of the efficiency of rehabilitation departments includes several parameters. Performance evaluation becomes complicated when several evaluation criteria must be taken into consideration at the same time. In these cases, scoring methods are generally used, which transform performance data into a common scale and an aggregate score is calculated with subjective weights. Using DEA the subjective element of evaluation is eliminated when the weights of inputs and outputs are determined.</p><p><strong>Findings:</strong> The applied DEA model evaluates the performance of rehabilitation departments. The presented analysis highlights the differences between the efficiency of the studied departments, and explores inefficiencies related to economies of scale. The slack values directly show the operational shortcomings in specific areas, and indicate the exact amount of the required changes.</p><p><strong>Research Limitation/implication:</strong> The applied DEA model evaluates the performance of rehabilitation departments. The presented analysis highlights the differences between the efficiency of the studied departments, and explores inefficiencies related to economies of scale. The slack values directly show the operational shortcomings in specific areas, and indicate the exact amount of the required changes.</p><strong>Originality/Value of paper:</strong> The originality of the paper lies on the identification of inputs and outputs for the applied DEA model as only nonfinancial indicators were taken into consideration. The analysis involves all rehabilitation departments of the Hungarian healthcare system; consequently, conclusions related to the general state of this area can be drawn.

scholarly journals Data Envelopment Analysis with Uncertain Inputs and Outputs

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Meilin Wen ◽  
Linhan Guo ◽  
Rui Kang ◽  
Yi Yang
Keyword(s):  
Data Envelopment Analysis ◽  
Deterministic Model ◽  
The Other ◽  
Data Envelopment ◽  
Decision Tool ◽  
Uncertain Measure ◽  
Dea Model ◽  
Dea Models ◽  
The One ◽  
Inputs And Outputs

Data envelopment analysis (DEA), as a useful management and decision tool, has been widely used since it was first invented by Charnes et al. in 1978. On the one hand, the DEA models need accurate inputs and outputs data. On the other hand, in many situations, inputs and outputs are volatile and complex so that they are difficult to measure in an accurate way. The conflict leads to the researches of uncertain DEA models. This paper will consider DEA in uncertain environment, thus producing a new model based on uncertain measure. Due to the complexity of the new uncertain DEA model, an equivalent deterministic model is presented. Finally, a numerical example is presented to illustrate the effectiveness of the uncertain DEA model.

Energy management in crop production using a novel fuzzy data envelopment analysis model

2018 ◽  
Vol 52 (2) ◽  
pp. 595-617 ◽  
Author(s):  
Mohammad Izadikhah ◽  
Alireza Khoshroo
Keyword(s):  
Data Envelopment Analysis ◽  
Crop Production ◽  
Fuzzy Variable ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Analysis Model ◽  
Dea Model ◽  
Model Based ◽  
Smoke Pollution ◽  
Fuzzy Dea

Data envelopment analysis is a relatively “data oriented” approach to measure the efficiency of a set of decision making units which transform multiple inputs into multiple outputs. However, some production processes may generate undesirable outputs like smoke pollution or waste. On the other hand, in many situations, such as a manufacturing system, a production process or a service system, inputs and outputs can be considered as a fuzzy variable. Thus, this paper has presented a new non-radial DEA model based on a modification of Enhanced Russell Model (ERM model) in the presence of an undesirable output in a fuzzy environment. Hereafter, a method for solving the proposed fuzzy DEA model based on the concept of alpha cut and possibility approach is presented. A useful stochastic closeness coefficient is also proposed to present a complete ranking. The proposed methodology is applied to evaluate the efficiencies of barley production farms in 22 provinces in Iran.

scholarly journals A non-overlapping Schwarz domain decomposition method with high-order finite elements for flow acoustics

2020 ◽  
Vol 369 ◽  
pp. 113223
Author(s):  
Alice Lieu ◽  
Philippe Marchner ◽  
Gwénaël Gabard ◽  
Hadrien Bériot ◽  
Xavier Antoine ◽  
...  
Keyword(s):  
Finite Elements ◽  
Domain Decomposition ◽  
Decomposition Method ◽  
Domain Decomposition Method ◽  
High Order ◽  
Overlapping Schwarz
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