Uncertainty Feature Optimization: an implicit paradigm for problems with noisy data

Eggenberg, NiklausSalani, MatteoBierlaire, Michel2010-09-302010-09-302010-09-30201110.1002/net.20428https://infoscience.epfl.ch/handle/20.500.14299/54470WOS:000290359800009Optimization problems with noisy data solved using stochastic programming or robust optimization approaches require the explicit characterization of an uncertainty set U that models the nature of the noise. Such approaches depend on the modeling of the uncertainty set and suffer from an erroneous estimation of the noise.optimization under uncertaintyrobustnessrecoverabilityDisruption ManagementAirlineConstraintsModelUncertainty Feature Optimization: an implicit paradigm for problems with noisy datatext::journal::journal article::research article