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 recording, we 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 35-minute video recording consists of:
This topic is presented by 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.
You can download the materials associated with this webinar here.
Whether you are new to optimization or switching from a competing solver, we've worked hard to make getting started with Gurobi easier for you.
To help you be as productive as possible, we offer a detailed reference manual along with numerous code examples and a wide range of videos.