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This process is performed using the fuzzy linear programming method.
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Criterion maps were combined using the fuzzy weighted linear combination operator.
The main purpose of the paper is to solve fuzzy linear second-order differential equations (FDEs) using the fuzzy Laplace transform method, under generalized differentiability.
The parameters in both the linear and nonlinear regression models are estimated using the fuzzy evidential EM algorithm, a straightforward fuzzy version of the evidential EM algorithm.
The fuzzy linear regression (FLR) was used and compared with the multiple-linear regression (MLR) analysis.
The fuzzy approximate solution of the fuzzy linear differential equation is obtained by solving the crisp linear equations.
The fuzzy technique used the fuzzy algebraic product operator, fuzzy algebraic sum operator, and fuzzy gamma operator.
Both use the fuzzy membership and the weightings.
Use the Fuzzy Border filter (Filters > Decor > Fuzzy Border).
They used the possibility measure to quantify the demand uncertainties and solved the model using fuzzy linear programming approach.
This is followed by the second phase using a fuzzy linear programming approach implemented with an augmented max-min operator to obtain a non-dominated solution that has an equal satisfactory degree on both objectives.
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