Multi-objective optimization under parametric uncertainty: A Pareto ellipsoids-based algorithm

Model-based multi-objective optimization is a useful tool to compute optimal trade-offs between multiple conflicting objectives such as minimizing energy consumption while maximizing productivity. However, the computational cost of solving problems high. In addition, model approximates real process, meaning uncertainty inherently present. To avoid erroneous predictions process performance and unsafe operation conditions, this should be accounted for. Adding propagation techniques increases even further. article, novel algorithm presented that efficiently computes relevant Pareto points based on significance criterion using ellipsoids. These ellipsoids represent visualize objective functions due parametric uncertainty. This facilitates selection it allows decision-maker easily consider solution. The showcased three case studies with different levels complexity.