Book excerpt: The global airline industry is a multi-stakeholder stochastic system whose performance is the outcome of complex interactions between its multiple decisions-makers under a high degree of uncertainty. Inadequate understanding of uncertainty and stakeholder preferences leads to adverse effects including airline losses, delays and disruptions. This thesis studies a set of topics in airline scheduling and air traffic control to mitigate some of these issues. The first part of the thesis focuses on building aircraft schedules that are robust against delays. We develop a robust optimization approach for building aircraft routes. The goal is to mitigate propagated delays, which are defined as the delays caused by the late arrival of aircraft from earlier flights and are the top cause of flight delays in the United States air transportation system. The key feature of our model is that it allows us to handle correlation in flight delays explicitly that existing approaches cannot handle efficiently. We propose an efficient decomposition algorithm to solve the robust model and present the results of numerical experiments, based on data from a major U.S. airline, to demonstrate its effectiveness compared to existing approaches. The second part of the thesis focuses on improving the planning of air traffic flow management (ATFM) programs by incorporating airline preferences into the decision-making process. We develop a voting mechanism to gather airline preferences of candidate ATFM designs. A unique feature of this mechanism is that the candidates are drawn from a domain with infinite cardinality described by polyhedral sets. We conduct a detailed case study based on actual schedule data at San Francisco International Airport to assess its benefits in planning of ground delay programs. Finally, we study an integrated airline network planning model which incorporates passenger choice behavior. We model passenger demand using a multinomial logit choice model and integrate it into a fleet assignment and schedule design model. To tackle the formidable computational challenge associated with solving this model, we develop a reformulation, decomposition and approximation scheme. Using data from a major U.S. airline, we prove that the proposed approach brings significant profit improvements over existing methods.