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 <datafield tag="088" ind1="" ind2="">
  <subfield code="a">EggSTRC08</subfield> 
  </datafield>
<datafield tag="909" ind1="C" ind2="0">
<subfield code="p">TRANSP-OR</subfield>
</datafield>
<datafield tag="980" ind1="" ind2="">
<subfield code="a">TALK</subfield>
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 <datafield tag="700" ind1="" ind2="">
  <subfield code="a">Eggenberg, Niklaus</subfield> 
  </datafield>
 <datafield tag="700" ind1="" ind2="">
  <subfield code="a">Salani, Matteo</subfield> 
  </datafield>
 <datafield tag="700" ind1="" ind2="">
  <subfield code="a">Bierlaire, Michel</subfield> 
  </datafield>
<datafield tag="245" ind1="" ind2="">
<subfield code="a">
Optimization of Uncertainty Features for Transportation Problems </subfield>
</datafield>
<datafield tag="260" ind1="" ind2="">
<subfield code="c">2008</subfield>
</datafield>
<datafield tag="711" ind1="2" ind2="">
<subfield code="a">
Swiss Transportation Research Conference 2008</subfield>
<subfield code="c">
Monte Verità, Switzerland</subfield>
<subfield code="d">October 17, 2008</subfield>
</datafield>
<datafield tag="520" ind1="" ind2="">
<subfield code="a">
In this work we present the concept of Uncertainty Feature Optimization (UFO), an optimization framework to handle problems due to noisy data. We show that UFO is an extension of standard methods as robust optimization and stochastic optimization and we show that the method can be used when no information of the data uncertainty sets is available. We present a proof of concept for the multiple knapsack problem and we show applications to some routing problems: vehicle routing with stochastic demands and airline scheduling.</subfield>
</datafield>
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