OR 750 - Stochastic Optimization

Welcome to the OR 750 webpage!

This course provides an introduction to stochastic optimization, more specifically, stochastic programming. The objectives are (i) to provide students with the ability to model and solve optimization problems under uncertainty, and (ii) to make students familiar with the state-of-the-art of stochastic programming. The course will be offered in a lecture format, and homeworks will be used to reinforce and supplement information in each section. Throughout the semester we will be reading research papers to supplement the material in the text book. Papers and other course material will be provided on Blackboard. Students should be proficient with one programming language (e.g., MATLAB, Python, Java, C) and should be able to become familiar with a math programming solver (e.g., Cplex, Gurobi.)

Textbook and Software

  • John Birge and François Louveaux, Introduction to Stochastic Programming, Springer, New York, 2011. Available online through the university library website.

  • Alexander Shapiro, Darinka Dentcheva, and Andrzej Ruszczy'nski, Lectures on Stochastic Programming. Modeling and Theory, MPS/SIAM Series on Optimization, SIAM, Philadelphia, PA, 2009. Available online here.