OPER791 Optimization under Uncertainty

Instructor:Yongjia Song
Time: Monday, Wednesday, 2:00-3:15 PM
Location: 4155 Harris Hall
Office hours: 4140 Harris Hall, Tuesday, Thursday 3:00-4:00 PM, and by appointment.

Description: This course will address basic models and algorithms for stochas- tic programming (optimization). Stochastic programming is a popular optimization tool that integrates statistics and operations research. Students are expected to have certain math background: basic calculus, basic linear algebra, and some mathematical analysis ability. Basic knowledge in optimization: linear pro- gramming, mathematical modeling. Basic knowledge in statistics: basic probability, statistical tests. Basic knowledge in a general purpose programming language is preferred, but not required. After the course, students are expected to understand and apply stochastic optimization tools:

• How to create a stochastic programming model using the given information (data, probability distri- bution, decision makers’ risk tolerance, etc).
• How to solve the stochastic programming model using certain optimization solvers.
• How to perform statistical analysis on the solutions of stochastic programs.

Course Syllabus: Link