Print Email Facebook Twitter Multi-period adaptive fleet planning problem Title Multi-period adaptive fleet planning problem: with Approximate Dynamic Programming Author Requeno García, Laura (TU Delft Aerospace Engineering) Contributor Santos, Bruno F. (mentor) Curran, R. (mentor) van Kampen, E. (mentor) Degree granting institution Delft University of Technology Programme Aerospace Engineering Date 2017-07-17 Abstract This MSc thesis presents a stochastic modelling approach to the multi-period airline fleet planning problem. Approximate Dynamic Programming (ADP) is used to model the impact of demand uncertainty on fleet decisions. The proposed ADP algorithm applies local value function approximations resulting from Gaussian kernel regressions to estimate future airline operating profits. A case study at a reference airline is used to show the effectiveness and practicability of the proposed approach. Optimal solutions are achieved for the deterministic case of the problem, while obtained policies excel the most-likely deterministic solution on stochastic versions. Subject Fleet planningAirlineApproximate Dynamic ProgrammingMachine LearningNon-parametric modelsKernel regressionsScenario treeMultistage stochastic modellingMulti-periodAdaptive policyValue function iterationDynamic Programming To reference this document use: http://resolver.tudelft.nl/uuid:228626b4-129f-492c-b1d4-8eb859941bbc Embargo date 2021-07-17 Part of collection Student theses Document type master thesis Rights © 2017 Laura Requeno García Files PDF MScThesis_4508831.pdf 67.35 MB Close viewer /islandora/object/uuid:228626b4-129f-492c-b1d4-8eb859941bbc/datastream/OBJ/view