This grant provides funding a) for the development of robust and adaptive optimization methods for multi-period optimization problems under uncertainty, b) for applications of these methods to air traffic control in the United States in the presence of adverse weather, and c) applications in revenue management, pricing and transportation network design. The approach will utilize a broad array of methodologies including all aspects of mathematical optimization, simulation, control theory, dynamical systems and game theory. From an educational standpoint, the results of this grant will serve as components in teaching modules on stochastic optimization and also be included in a book on robust optimization that aspires to present a unified and modern introduction to this area.
Multi-period optimization under uncertainty is a central problem in many engineering and management problems, such as engineering design, supply chain optimization, financial planning, air traffic control and revenue management. If successful, the results of this research will enable the development of new tractable and scalable approaches to solving optimization under uncertainty using data directly and through the publication of the book mentioned above will also affect the way optimization under uncertainty is taught in both undergraduate and graduate curricula. As a test case, the results of this research will be applied to controlling air traffic in the United States in the presence of adverse weather.
