In this article, we discuss an alternative method for deriving conservative approximation models for two-stage robust optimization problems. The method mainly relies on a linearization scheme employed in bilinear optimization problems, therefore we will say that it gives rise to the linearized robust counterpart models. We identify a close relation between this linearized robust counterpart model and the popular affinely adjustable robust counterpart model. We also describe methods of modifying both types of models to make these approximations less conservative. These methods are heavily inspired by the use of valid linear and conic inequality in the linearization process for bilinear models. We finally demonstrate how to employ this new scheme in a set of four operations management problems to improve the performance and guarantees of robust optimization.
Published September 2016 , 26 pages
This cahier was revised in November 2017