Interactive multi-scenario multi-objective robust optimization for decision-making under deep uncertainty

This study proposes a novel approach for integrating interactive multi-objective optimization into Many Objective Robust Decision Making (MORDM) to involve decision-makers during the solution process. Unlike the a posteriori methods, this involvement provides an intuitive learning phase for the decision-maker with complete control to search and uncover the problem characteristics, the feasibility of their preferences, how uncertainty may affect the outcomes of a decision, and explore various parts of the Pareto fronts, one at a time, significantly reducing cognitive load and computation resources. We further introduce a hypothetical water management problem as a new benchmark problem for robust decision-making with multiple objectives under deep uncertainty, which is most suited for properly showcasing the robustness optimality trade-offs. Utilizing this example, we illustrate the stages and interactions of the proposed approach as a proof of concept.