This paper introduces a novel stochastic approach for the ground-delay-program planning under uncertainty, using chance-constrained optimization. The major advantage of the chance-constrained model is the ability to provide robust solutions with user-defined service level. The approach is compared with the Ball et al. (“A Stochastic Integer Program with Dual Network Structure and Its Application to the Ground-Holding Problem,” Operations Research, Vol. 51, No. 1, Feb. 2003, pp. 167–171) model for selecting planned airport acceptance rates for airports in a metroplex, which is an interdependent system in close geographic proximity. The approaches were evaluated using real flight schedules and landing-capacity data from the New York City metroplex airports. Although the Ball et al. model was found to be more efficient, the chance-constrained model shows the ability to provide a quantized way to balance the solution’s robustness and potential cost by choosing a proper service level. Moreover, the parallel-computing framework was demonstrated to be helpful in improving the computing efficiency, which suggests that deploying more computing resources would help solve a large-scale planning problem under uncertainty in the same framework.


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    Titel :

    Stochastic Ground-Delay-Program Planning in a Metroplex


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

    Erschienen in:

    Erscheinungsdatum :

    2017-08-25


    Format / Umfang :

    9 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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