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.
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