Multi-scenario multi-objective robust optimization under deep uncertainty: A posteriori approach

This paper proposes a novel optimization approach for multi-scenario multi-objective robust decision making, as well an alternative way scenario discovery and identifying vulnerable scenarios even before any solution generation. To demonstrate test the approach, we use classic shallow lake problem. We compare results obtained with to those previously used approaches. show that guarantees feasibility efficiency of produced solutions under all selected scenarios, while decreasing computation cost, addresses scenario-dependency issues, enables decision-makers explore trade-off between optimality/feasibility in robustness across broader range scenarios. also find problem is ill-suited reflecting trade-offs performance over set Pareto optimality specific scenario, highlighting need benchmark problems properly evaluate