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 35-minute video recording, 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 recording consists 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
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.
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[@bonzai_303]

Huge thanks... very thorough and clear explanation.

[@humyunfuadrahman624]

Can you share the source code and the mathematical model for the problem. Without that it is difficult to implement it in GUROBI.

[@ehdo-tool]

Good explanation, thanks for sharing.

[@kpjxX]

Where can I find the promised Jupyter Notebook?

[@ludwigb6334]

Good question

[@minglee5164]

Really helpful, thanks

[@GurobiVideos]

Glad to hear!