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<dc:title>A novel approach to the tail assignment problem in airline planning</dc:title>
<dc:creator>Fuentes, Manuel</dc:creator>
<dc:creator>Cadarso, Luis</dc:creator>
<dc:creator>Vaze, Vikrant</dc:creator>
<dc:creator>Barnhart, Cynthia</dc:creator>
<dc:subject>Planificación del transporte</dc:subject>
<dc:subject>Industria aérea</dc:subject>
<dc:subject>Planning of transport</dc:subject>
<dc:subject>Airline industry</dc:subject>
<dc:description>Trabajo presentado en: R-Evolucionando el transporte, XIV Congreso de Ingeniería del Transporte (CIT 2021), realizado en modalidad online los días 6, 7 y 8 de julio de 2021, organizado por la Universidad de Burgos</dc:description>
<dc:description>Combinatorial optimization problems abound in the field of airline planning. Aircraft and&#xd;
passengers fly on networks made up of flights and airports. To schedule aircraft, assignments&#xd;
of fleet types to flights and of aircraft to routes must be determined. The former is known as&#xd;
the fleet assignment problem while the latter is known as the aircraft routing problem in the&#xd;
literature. Aircraft routing is typically addressed as a feasibility problem, the solution to&#xd;
which is required for the construction of crew schedules. All these issues are typically&#xd;
resolved 4 to 6 months before the day of operations. As a result, there is little information&#xd;
available about each aircraft's operational status when making such decisions. The tail&#xd;
assignment problem, which has received little attention in the literature, is solved when&#xd;
additional information about operational conditions is revealed, with the goal of determining&#xd;
each aircraft's route for the day of operations while accounting for the originally planned&#xd;
aircraft routes and crew schedules. As a result, it is a problem that must be resolved closer&#xd;
to the day of operations. We propose a mathematical programming approach based on&#xd;
sequencing that captures all operational constraints and maintenance requirements while&#xd;
minimizing operational costs and schedule changes relative to original plans. The&#xd;
computational experiments are based on realistic cases drawn from a Spanish airline with&#xd;
over 1000 flights and over 100 aircraft.</dc:description>
<dc:date>2022-09-21T10:10:11Z</dc:date>
<dc:date>2022-09-21T10:10:11Z</dc:date>
<dc:date>2021-07</dc:date>
<dc:type>info:eu-repo/semantics/conferenceObject</dc:type>
<dc:identifier>978-84-18465-12-3</dc:identifier>
<dc:identifier>http://hdl.handle.net/10259/6977</dc:identifier>
<dc:identifier>10.36443/10259/6977</dc:identifier>
<dc:language>eng</dc:language>
<dc:relation>R-Evolucionando el transporte</dc:relation>
<dc:relation>http://hdl.handle.net/10259/6490</dc:relation>
<dc:relation>https://doi.org/10.36443/9788418465123</dc:relation>
<dc:relation>info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TRA2016-76914-C3-3-P/ES/ROBUSTEZ, EFICIENCIA Y RECUPERACION DE SISTEMAS DE TRANSPORTE PUBLICO</dc:relation>
<dc:relation>info:eu-repo/grantAgreement/MICIU/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/CAS19%2F00036</dc:relation>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:publisher>Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional</dc:publisher>
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