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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    Title :

    Stochastic Ground-Delay-Program Planning in a Metroplex


    Contributors:


    Publication date :

    2017




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English



    Classification :

    BKL:    55.54 Flugführung
    Local classification TIB:    770/7040



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