joint work with Anita Schöbel
Previously developed concepts for robust multi-objective optimization such as set-based and point-based minmax robust efficiency determine efficient solutions that are good in the worst case. However, finding robust efficient solutions for a given uncertain multi-objective problem remains difficult. We adopt a row generating approach incrementally increasing the set of considered scenarios and investigate what selection rule for the scenario to add is most beneficiary to achieve fast convergence.
joint work with Ralf Werner
Consider a multiobjective decision problem with uncertainty given as a set of scenarios. In the single criteria case, robust optimization methodology helps to identify solutions which remain feasible and of good quality for all possible scenarios. Here one method is to compare the possible decisions under uncertainty against the optimal decision with the benefit of hindsight, i.e. to minimize the regret of not having chosen the optimal decision. In this talk I will extend this regret to the multiobjective setting to introduce a robust strategy for multiobjective optimization under uncertainty.