Uncertainty feature optimization: an implicit paradigm for problems with noisy data

Abstract

We introduce uncertainty feature optimization, an implicit paradigm for handling problems with noisy or uncertain data. The approach models uncertainty at the feature level and optimizes over feature uncertainty sets, providing robust solutions applicable to transportation and scheduling problems.

Robust OptimizationUncertaintyTransportation

BibTeX

@article{eggenberg2011uncertainty,
  title={Uncertainty feature optimization: an implicit paradigm for problems with noisy data},
  author={Eggenberg, Niklaus and Salani, Matteo and Bierlaire, Michel},
  journal={Networks},
  volume={57},
  number={3},
  pages={270--284},
  year={2011},
  publisher={Wiley Subscription Services, Inc., A Wiley Company Hoboken}
}