Weather accounts for the majority of congestion in the National Airspace System, which highlights the importance of addressing weather uncertainty to mitigate delays. First, this paper presents a new dynamic stochastic integer programming model that studies the single-airport ground-holding problem with respect to uncertainty in the separation between flights instead of airport acceptance rate. The current model is able to provide a more accurate schedule expressed in minutes for the individual flight. Second, a converging inbound air traffic model is formulated based on the current model. This paper addresses a problem involving the merging of two inbound streams into a single airport in which uncertainty in separation from minutes-in-trail restrictions is considered. Although the “first-come/first-served” policy is still obeyed by flights on the same path, the experimentation has shown that allowing flights on different paths to switch arrival orders can help reduce the total delays. Finally, in order to tackle the computational burden posed by the disaggregate integer model, this paper introduces a dual decomposition method to reduce computation time. The original problem is decomposed scenario by scenario into several subproblems; then, a parallel computing algorithm is developed to handle these subproblems. Such a combination increases the model’s computational efficiency.


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    Titel :

    Dynamic Stochastic Model for Converging Inbound Air Traffic


    Beteiligte:
    Chen, Jun (Autor:in) / DeLaurentis, Daniel (Autor:in) / Sun, Dengfeng (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2015-09-10


    Format / Umfang :

    11 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






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