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<record>
 <datafield tag="088" ind1="" ind2="">
  <subfield code="a">ARP_21_06</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>
</datafield>
 <datafield tag="700" ind1="" ind2="">
  <subfield code="a">Eggenberg, Niklaus</subfield> 
  </datafield>
 <datafield tag="700" ind1="" ind2="">
  <subfield code="a">Bierlaire, Michel</subfield> 
  </datafield>
 <datafield tag="700" ind1="" ind2="">
  <subfield code="a">Salani, Matteo</subfield> 
  </datafield>
<datafield tag="245" ind1="" ind2="">
<subfield code="a">
Airline Disruptions: Aircraft Recovery with Maintenance Constraints</subfield>
</datafield>
<datafield tag="260" ind1="" ind2="">
<subfield code="c">2007</subfield>
</datafield>
<datafield tag="711" ind1="2" ind2="">
<subfield code="a">
OR Seminar June 2007</subfield>
<subfield code="c">
EPFL, MA 31</subfield>
<subfield code="d">June 21, 2007</subfield>
</datafield>
<datafield tag="520" ind1="" ind2="">
<subfield code="a">
Airline schedules are rarely performed as planned because of irregularities such as delays, unpredicted maintenances or bad weather conditions. Recover from a disrupted schedule as quickly as possible is a hard and high priority problem for practitioners: given a disrupted situation they have to take decisions in order to minimize the number of canceled flights and the average delay respecting all technical requirements. We present a column generation based algorithm to solve the airplane recovery problem including maintenance constraints and show, through computational results, the added value of including the maintenance scheduling in the recovery problem.
</subfield>
</datafield>
  </record>



  </collection>
