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<record>
 <datafield tag="088" ind1="" ind2="">
  <subfield code="a">NEg_STRC_2007</subfield> 
  </datafield>
<datafield tag="980" ind1="" ind2="">
<subfield code="a">PROC</subfield>
</datafield>
<datafield tag="909" ind1="C" ind2="0">
<subfield code="p">TRANSP-OR</subfield>
</datafield>
 <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">
Robust Optimization with Recovery: Application to Shortest Paths and Airline Scheduling</subfield>
</datafield>
<datafield tag="260" ind1="" ind2="">
  <subfield code="c">2007</subfield> 
</datafield>
<datafield tag="711" ind1="2" ind2="">
<subfield code="a">Swiss Transport Research Conference</subfield>
<subfield code="c">Monte Verità, Switzerland</subfield>
<subfield code="d">September 12-14</subfield>
</datafield>
<datafield tag="520" ind1="" ind2="">
<subfield code="a">
In this exploratory paper we consider a robust approach to decisional problems subject to uncertain data in which we have an additional knowledge on the strategy (algorithm) used to react to an unforeseen event or recover from a disruption. This is a typical situation in scheduling problems where the decision maker has no a priori knowledge on the probabilistic distribution of such events but he only knows rough information on the event, such as its impact on the schedule. We discuss a general framework to address this situation and its links with other existing methods, we present an illustrative example on the Shortest Path Problem with Interval Data (SPPID) and we discuss a more general application to airline scheduling with recovery.</subfield>
</datafield>
  </record>



  </collection>
