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research article

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

Eggenberg, Niklaus
•
Salani, Matteo  
•
Bierlaire, Michel  
2011
Networks

Optimization 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.

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Type
research article
DOI
10.1002/net.20428
Web of Science ID

WOS:000290359800009

Author(s)
Eggenberg, Niklaus
Salani, Matteo  
Bierlaire, Michel  
Date Issued

2011

Published in
Networks
Volume

57

Issue

3

Start page

270

End page

284

Subjects

optimization under uncertainty

•

robustness

•

recoverability

•

Disruption Management

•

Airline

•

Constraints

•

Model

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
TRANSP-OR  
Available on Infoscience
September 30, 2010
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/54470
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