Decision Optimization for Complex systems

Catalog description

This course builds a strong foundation in mathematical modeling and optimization techniques for data-driven decision-optimization in complex systems. Students are introduced to cutting-edge modeling and analytical techniques for linear, nonlinear, mixed-integer, multi-criteria, and black-box optimization, with hands-on work using state-of-the-art industrial solvers and generative AI tools. Modern techniques for optimizing dynamic systems under uncertainty that integrate prescriptive modeling with predictive and simulation modeling are also introduced. Pragmatic approaches for evaluating and selecting solutions are emphasized. Throughout the course, students work on data-driven optimization projects and study real-world cases of large-scale implementations of these techniques across finance, healthcare, energy, transportation, manufacturing, retail and supply chain. Though no specific prerequisites are needed, some background in Python coding and data manipulation is preferred. For those who have not used Python for a while, a crash tutorial will be provided.