The Ground-Holding Policy Problem (GHPP) has become a matter of
great interest in recent years because of the high cost incurred by
aircraft suffering from delays. Ground-holding keeps a flight on
the ground at the departure airport if it is known it will be
unable to land at the arrival airport. The GBPP is determining how
many flights should be held on the ground before take-off and for
how long, in order to minimize the cost of delays. When the
uncertainty associated with airport landing capacity is considered,
the GHPP becomes complicated. A decision support system that
incorporates this uncertainty, solves the GHPP quickly, and gives
good results would be of great help to air traffic management. The
purpose of this thesis is to modify and analyze a probabilistic
ground-holding algorithm by applying it to two common cases of
capacity reduction. A graphical user interface was developed and
sensitivity analysis was done on the algorithm, in order to see how
it may be implemented in practice. The sensitivity analysis showed
the algorithm was very sensitive to the number of probabilistic
capacity scenarios used and to the cost ratio of air delay to
ground delay. The algorithm was not particularly sensitive to the
number of periods that the time horizon was divided into. In terms
of cost savings, a ground-holding policy was the most beneficial
when demand greatly exceeded airport capacity. When compared to
other air traffic flow strategies, the ground-holding algorithm
performed the best and was the most consistent under various
situations. The algorithm can solve large problems quickly and
efficiently on a personal computer.
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