This course will provide an overview of the theory, solution algorithms, and applications of models for optimal decision-making under uncertainty. The course will emphasize models and methods that apply to discrete-time, high-dimensional decisions in a variety of domains including energy, finance, logistics, manufacturing, transportation, and services. Continuous-time models will also be presented for comparison. Topics will include characterization of optimality, stability, sensitivity, and robustness, approximation, statistical, and convergence properties, asymptotic and extremal distributions, and computational complexity.
Students will develop skills to represent complex decision problems in a tractable form, to solve large-scale problems, and to describe resulting solution properties. Students will be prepared to read, understand, and interpret recent literature in the field.
Fundamental knowledge of linear programming, probability, and stochastic processes. Some familiarity with nonlinear optimization and convex analysis.
Birge and Louveaux, Introduction to Stochastic Programming, Second edition, Springer-Verlag, 2011; additional papers to accompany specific topics
Weekly problems, project, midterm, and final examination. Grading (class participation (5%), homework (20%), midterm (30%), and final (45%)).
Description and/or course criteria last updated: November 20 2024
