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        <identifier>oai:drops-oai.dagstuhl.de:1183</identifier>
        <datestamp>2024-03-06T10:28:27Z</datestamp>
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          <dc:title>03. Disruption Management in PassengerTransportation - from Air to Tracks</dc:title>
          <dc:creator>Clausen, Jens</dc:creator>
          <dc:description>Over the last 10 years there has been a tremendous growth&#13;
in air transportation of passengers. Both airports and airspace are close&#13;
to saturation with respect to capacity, leading to delays caused by disruptions.&#13;
At the same time the amount of vehicular traffic around and&#13;
in all larger cities of the world has show a dramatic increase as well.&#13;
Public transportation by e.g. rail has come into focus, and hence also&#13;
the service level provided by suppliers ad public transportation. These&#13;
transportation systems are likewise very vulnerable to disruptions.&#13;
In the airline industry there is a long tradition for using advanced mathematical&#13;
models as the basis for planning of resources as aircraft and crew.&#13;
These methods are now also coming to use in the process of handling&#13;
disruptions, and robustness of plans has received much interest. Commercial&#13;
IT-systems supplying decision support for recovery of disrupted&#13;
operations are becoming available. The use of advanced planning and&#13;
recovery methods in the railway industry currently gains momentum.&#13;
The current paper gives a short overview over the methods used for planning&#13;
and disruption management in the airline industry. The situation&#13;
regarding railway optimization is then described and discussed. The issue&#13;
of robustness of timetables and plans for rolling stock and crew is&#13;
also addressed.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Jens Clausen</dc:contributor>
          <dc:date>2007</dc:date>
          <dc:relation>Is Part Of OASIcs, Volume 7, 7th Workshop on Algorithmic Approaches for Transportation Modeling, Optimization, and Systems (ATMOS'07) (2007)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
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          <dc:format>application/pdf</dc:format>
          <dc:identifier>doi:10.4230/OASIcs.ATMOS.2007.1183</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-11836</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ATMOS.2007.1183</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode</dc:rights>
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