Event Details

This is the first of a two part seminar session for Management Science

Abstract: We consider robust optimization problems with implementation uncertainty. In this setting, the solution that a decision maker wants to implement is affected by uncertainty. The aim is to find a solution that optimizes the worst possible performance in this setting.

Previously, only few generic search methods have been developed for this setting. We introduce a new approach for a global search, based on placing a largest empty hypersphere. We do not assume any knowledge on the structure of the original objective function, making this approach also viable for simulation-optimization settings. In computational experiments we demonstrate a strong performance of our approach, which makes it possible to solve even high-dimensional problems.