Uncertainty makes decision-making challenging. The robust decision-making (RDM) framework has been successfully applied in various real-world applications involving deep uncertainty. It has been extended to consider simultaneously multiple objective functions and scenarios. However, the practical applications of these extensions are mostly limited to academic case studies. The main reasons are:\ (i) substantial cognitive load in tracking all the trade-offs across scenarios and the interplay between uncertainties and trade-offs, (ii) lack of decision-makers' involvement in solution generation and confidence. To address these problems, in this study, we propose a novel interactive framework involving decision-makers in searching for the most preferred robust solutions utilizing interactive multi-objective optimization methods. The proposed interactive framework provides a learning phase for decision-makers to discover the problem characteristics and the inter-dependencies between objective functions and scenarios, the feasibility of their preferences, and how uncertainty may affect the outcomes of a decision. Furthermore, decision-makers can interactively study the trade-offs between objective functions in various scenarios. This involvement and learning give them additional insight into the problem and allow them to control and direct the multiobjective search during the solution generation process, boosting their confidence and assurance in implementing the identified robust solutions in practice.

Reproducibility artifacts for: Shavazipour, B., Kwakkel, J. H., & Miettinen, K. (2024). Let decision-makers direct the search for robust solutions: An interactive framework for multiobjective robust optimization under deep uncertainty. Available at SSRN 4782234. It also includes the code for the proposed benchmark river problem with deeply uncertain parameters