Solving Simple Stochastic Optimization Problems with Gurobi

The importance of incorporating uncertainty into optimization problems has always been known; however, both the theory and software were not up to the challenge to provide meaningful models that could be solved within a reasonable run time.

Over the last 15 years, the continuous improvements made to the theoretical as well as the algorithmic area of stochastic and mixed integer linear optimization have changed this situation dramatically.

In this webinar, we will focus on stochastic optimization models and easy-to-understand algorithms, amenable to being easily solved with Gurobi. The intended audience for this webinar includes those with a background in optimization and knowledge on basic probability and statistics.

This webinar will consist of:

  • A quick introduction to stochastic optimization
  • Types of stochastic optimization problems
  • Types of models that can be solved easily: two-stage stochastic problems with expected value and coherent risk measures
  • Overview of the main algorithms: sample average approximation
  • Examples of common problems with Gurobi

There will be time for questions at the end of the webinar. Presenting this webinar is Dr. Daniel Espinoza, Senior Developer at Gurobi Optimization.

Dr. Espinoza holds a Ph.D. in Operations Research from Georgia Institute of Technology. He has published numerous papers in the fields of mathematical programming, computer optimization and operations research. Prior to joining Gurobi, he was Associate Professor in the Department of Industrial Engineering at the Universidad de Chile.

There are two sessions, presented in English, to choose from:

Tuesday, December 4th at 8:00am PT (GMT -8)

Wednesday, December 5th at 3pm CET (GMT +1)

Click on the "Show in My Time Zone" link at the top of the registration page for each webinar to see the start time in your local timezone.