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