Air traffic flow management problem with stochastic capacities

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buir.advisorİyigün, Özlem Çavuş
dc.contributor.authorSertkaya, Efe
dc.date.accessioned2021-11-02T05:14:08Z
dc.date.available2021-11-02T05:14:08Z
dc.date.copyright2021-09
dc.date.issued2021-09
dc.date.submitted2021-10-26
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (Master's): Bilkent University, Department of Industrial Engineering, İhsan Doğramacı Bilkent University, 2021.en_US
dc.descriptionIncludes bibliographical references (leaves 88-92).en_US
dc.description.abstractAir traffic systems have substantial effects on transportation, logistics, and economics in a global scope. Due to both practical significance and intellectual challenges, air traffic flow management problems have been extensively studied for many decades. The aim of air traffic flow management problems is to plan the flow throughout the air traffic network while satisfying capacity constraints. In this study, we consider the case of stochastic capacities in the air traffic network. We propose both stochastic multistage integer and stochastic two-stage integer modeling approaches for the problem. In multistage and two-stage models, we aim to resolve the demand-capacity imbalances at each element of the air traffic network. To achieve this, we decide on the take-off times and routes of each flight for a given time horizon. We propose integer L-shaped and partial Benders’ decomposition approaches to solve the two-stage model. Additionally, we analyze the effect of conditional value-at-risk constraints on delay time distributions. To incorporate conditional value-at-risk to solution methodologies, we propose a novel approximation technique. We present a detailed analysis of delay distributions, demonstrate the effect of the approximation technique on solution quality and computational performance. For computational experiments, we explicitly describe data generation procedures to obtain realistic instances. We demonstrate that the Partial Benders’ modification outperforms the commercial solver (CPLEX) in almost every instance.en_US
dc.description.provenanceSubmitted by Betül Özen (ozen@bilkent.edu.tr) on 2021-11-02T05:14:08Z No. of bitstreams: 1 10425143.pdf: 2211641 bytes, checksum: 14356a60af087fe98a0bbaa7a29241b7 (MD5)en
dc.description.provenanceMade available in DSpace on 2021-11-02T05:14:08Z (GMT). No. of bitstreams: 1 10425143.pdf: 2211641 bytes, checksum: 14356a60af087fe98a0bbaa7a29241b7 (MD5) Previous issue date: 2021-09en
dc.description.statementofresponsibilityby Efe Sertkayaen_US
dc.format.extentxii, 97 leaves : charts ; 30 cm.en_US
dc.identifier.itemidB122603
dc.identifier.urihttp://hdl.handle.net/11693/76651
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAir traffic flow managementen_US
dc.subjectTwo-stage stochastic programmingen_US
dc.subjectIn-teger L-shaped algorithmen_US
dc.subjectPartial Benders’ decompositionen_US
dc.subjectConditional value at risken_US
dc.titleAir traffic flow management problem with stochastic capacitiesen_US
dc.title.alternativeStokastik kapasitelerle hava trafiği akış problemlerien_US
dc.typeThesisen_US
thesis.degree.disciplineIndustrial Engineering
thesis.degree.grantorBilkent University
thesis.degree.levelMaster's
thesis.degree.nameMS (Master of Science)

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