Fuzzy set theory, observer bias and probability theory
Fuzzy Sets and Systems
Programming Microsoft Office 2000 Web Components with Cdrom
Programming Microsoft Office 2000 Web Components with Cdrom
Uncertain Programming
Data Envelopment Analysis: A Comprehensive Text with Models, Applications References, and DEA-Solver Software with Cdrom
Redefining chance-constrained programming in fuzzy environment
Fuzzy Sets and Systems - Theme: Decision and optimization
A fuzzy DEA/AR approach to the selection of flexible manufacturing systems
Computers and Industrial Engineering
Selecting the best statistical distribution using multiple criteria
Computers and Industrial Engineering
Computers and Industrial Engineering
A one-model approach based on relaxed combinations of inputs for evaluating input congestion in DEA
Journal of Computational and Applied Mathematics
Linear programming under randomness and fuzziness
Fuzzy Sets and Systems
Computers and Industrial Engineering
A robust optimization approach for imprecise data envelopment analysis
Computers and Industrial Engineering
Ranking efficient decision-making units in data envelopment analysis using fuzzy concept
Computers and Industrial Engineering
Recognizing strong and weak congestion slack based in data envelopment analysis
Computers and Industrial Engineering
Right and left returns to scales in data envelopment analysis: Determining type and measuring value
Computers and Industrial Engineering
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Varieties of data envelopment analysis (DEA) models have been formulated to assess performance of decision making units (DMUs) in various fields with different data such as: deterministic, interval, fuzzy, etc. Classic DEA requires that values of all inputs and outputs are known exactly. However, this assumption may not be true, since in practice, data can not be precisely measured. Furthermore, a realistic situation is no longer realistic when imprecise and uncertain information are neglected to analyze efficiency of DMUs and measurement errors and data entry errors, etc. For these reasons, in this present investigation, we deal with a realistic decision problem that contains fuzzy constraints and uncertain information (stochastic data) that most productive scale size (MPSS) is estimated in imprecise-chance constrained DEA model. Moreover, intention of this research is to develop and solve a chance-constrained input-output orientation DEA model in which even the chance factors associated with the constraints are not specified precisely. Fuzziness and probability concepts allow the data errors and provide probabilistic results. Hence, if the data is quite imprecise, and also an irregular estimate is needed, the imprecise chance constrained model might be fancied. It is worth stressing that, in practice, data is imprecise. However, uncertainty does n't only relate to stochastic data. Hence, fuzziness and randomness are required to be considering in a real situation, simultaneously. Other advantage of our research is to impose managers' ideas, by considering the tolerances allowed by decision makers. In this current study, a methodology is taken for conversion of fuzzy probabilistic constraints into the deterministic equivalent form. It is worth stressing that, the process of conversion deals first with randomness and then with fuzziness. One it can first deal with fuzziness and then randomness. However, the results will be the same. This is because of the concepts that the involvement of randomness and fuzziness are independent in the model. At last, an empirical example highlights the application of the model then some conclusions are drawn and directions for future research are suggested.