This book provides ways for air traffic managers to better address
airport capacity uncertainty in the airspace system. In particular, it
discusses decision-making algorithms under uncertainty in ground delay
programs (GDPs) for a single destination airport. The book proposes
methods to model stochasticity in GDP operations and mechanisms to
respond to conditions dynamically such that the overall system
performance is optimized. The single airport ground holding problem with
capacity uncertainty is modeled using two approaches: multi-stage
stochastic integer programs with probabilistic capacity scenario trees
and sequential decision dynamic programs with Markov capacity evolution
processes. The stochastic programs require scenarios that depict
capacity evolutions. Methodologies are introduced for generating and
using scenario trees from empirical data. The challenge for the dynamic
programs lies in the computational load for solving large-scale problems
due to the curse of dimensionality. We present computational strategies
to manage the complexity. In this book, we also discuss the mathematical
relationship between the models and analyze their performance in a
real-world setting.