Centroid of a type-2 fuzzy set
Information Sciences: an International Journal
Expected value operator of random fuzzy variable and random fuzzy expected value models
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
An efficient centroid type-reduction strategy for general type-2 fuzzy logic system
Information Sciences: an International Journal
Type-2 fuzzy variables and their arithmetic
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Interval type-2 fuzzy logic systems: theory and design
IEEE Transactions on Fuzzy Systems
Expected value of fuzzy variable and fuzzy expected value models
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Standby redundancy optimization with type-2 fuzzy lifetimes
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Mean-Entropy model for portfolio selection with type-2 fuzzy returns
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Optimizing fuzzy portfolio selection problems by parametric quadratic programming
Fuzzy Optimization and Decision Making
Expert Systems with Applications: An International Journal
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Data envelopment analysis (DEA) is a methodology for measuring the relative efficiency of decision making units (DMUs) consuming the same types of inputs and producing the same types of outputs. This paper studies the DEA models with type-2 data variations. In order to deal with the existed type-2 fuzziness, we propose the mean reduction methods for type-2 fuzzy variables. Based on the mean reductions of the type-2 fuzzy inputs and outputs, we formulate a new class of fuzzy generalized expectation DEA models. When the inputs and outputs are mutually independent type-2 triangular fuzzy variables, we discuss the equivalent parametric forms for the constraints and the generalized expectation objective, where the parameters characterize the degree of uncertainty of the type-2 fuzzy coefficients so that the information cannot be lost via our reduction method. For any given parameters, the proposed model becomes nonlinear programming, which can be solved by standard optimization solvers. To illustrate the modeling idea and the efficiency of the proposed DEA model, we provide one numerical example.