Traditionally, robust optimization has solved problems based on static decisions which are predetermined by the decision makers. Once the decisions were made, the problem was solved and whenever a new uncertainty was realized, the uncertainty was incorporated to the original problem and the entire problem was solved again to account for the uncertainty.[1] In order to address deal with the issues of uncertainties, several attempts were made in the field of optimization. While numerous technique exists, one of the widely studied way of dealing with uncertainty has been utilizing Stochastic Optimization method. In Stochastic Approach, the uncertainty is handled by assigning probability distribution to the uncertainty. Stochastic Optimization has proven its usefulness in certain areas, but this approach has couple drawbacks. First, while one can randomly assign probability distribution to make the model work, in a real life application, it is difficult to come up with an accurate probability distribution. Second, the Stochastic approach does not emphasize heavily on minimizing the cost of the worst case scenario, for the people who are making investment or company decisions need an Optimization technique that will yield conservative result and account for the uncertainties. Adaptive Robust Optimization, currently led by Aharon Ben-Tal and dimitris Bertsimas, is an improved version of the traditional static robust optimization. Instead of assigning probability distribution to handle uncertainty, Adaptive Robust Optimization handles uncertainty by treating it as a function of ellipsoid, polyhedron, or any other ways that might best serve a specific case of interest. Furthermore, it utilizes the decisions made in the first stage to come up with a solution, which is used to arrive at the final answer even under uncertainties. Even though Adaptive Robust Optimization is a relatively new field, its capability as a way of solving a frequently asked questions in business and other real life application has proven the method useful. This wiki-page was created to introduce the topic of Adaptive Robust Optimization to fellow students with the hope of enriching ChemE 345 experience beyond the scope of what was covered in class.

Last modified

• 11/30/2018

Creator

• Woo Soo Choe

DOI

• https://doi.org/10.21985/N2WF36

Keyword

• Adaptive Robust Optimization
• Optimization

Rights statement

• In Copyright