Uncertainty in air traffic arrival demand creates difficulties for the Air Traffic Control (ATC) specialists in effectively planning Ground Delay Programs(GDPs). An inefficiently planned GDP leads to excessive flight delays and under-utilization of the GDP airport. GDP optimization models that exist today may not generate the best strategies for planning GDPs as they consider demand as deterministic, when in reality, it is highly stochastic.

In this thesis, we identify Flight Cancellations, Pop-up Flight Arrivals, and Flight Drift as the common sources of demand uncertainties. Two models -- an optimization model and a simulation model -- that generate effective planningstrategies for a stochastic demand and deterministic capacity scenario, are developed. These models incorporate uncertainty in demand by associating probabilities to the stochastic demand elements during GDPs.

The results from both the models suggest that setting Planned Airport Arrival Rates (PAARs) -- the number of flights that are ordered to arrive in a time period at a GDP airport -- that exhibit taircasepattern can effectively mitigate the detrimental effects of demand uncertainties during GDPs. This is a significant finding as it opposes the current policy of setting latPAAR patterns by the ATC specialists.