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.
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