000152326 001__ 152326
000152326 005__ 20181203022017.0
000152326 0247_ $$2doi$$a10.1002/net.20428
000152326 022__ $$a0028-3045
000152326 02470 $$2ISI$$a000290359800009
000152326 037__ $$aARTICLE
000152326 245__ $$aUncertainty Feature Optimization: an implicit paradigm for problems with noisy data
000152326 260__ $$c2011
000152326 269__ $$a2011
000152326 336__ $$aJournal Articles
000152326 520__ $$aOptimization problems with noisy data solved using stochastic programming or robust optimization approaches require the explicit characterization of an uncertainty set U that models the nature of the noise. Such approaches depend on the modeling of the uncertainty set and suffer from an erroneous estimation of the noise.
000152326 6531_ $$aoptimization under uncertainty
000152326 6531_ $$arobustness
000152326 6531_ $$arecoverability
000152326 6531_ $$aDisruption Management
000152326 6531_ $$aAirline
000152326 6531_ $$aConstraints
000152326 6531_ $$aModel
000152326 700__ $$aEggenberg, Niklaus
000152326 700__ $$0244507$$g172916$$aSalani, Matteo
000152326 700__ $$aBierlaire, Michel$$g118332$$0240563
000152326 773__ $$j57$$tNetworks$$k3$$q270-284
000152326 909C0 $$xU11418$$0252123$$pTRANSP-OR
000152326 909CO $$particle$$pENAC$$ooai:infoscience.tind.io:152326
000152326 937__ $$aEPFL-ARTICLE-152326
000152326 970__ $$aIJ-EggeSalaBier10/TRANSP-OR
000152326 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000152326 980__ $$aARTICLE