Multi-objective optimization algorithms are used in the building design process to find optimal solutions for design problems. Typically, these algorithms provide the decision maker with a Pareto front containing trade-off solutions. Since these solutions are equally good, the decision maker needs a method to select the most appropriate solution. In this paper, we propose a selection method that ranks the Pareto solutions according to their performance robustness. This allows the decision maker to select the most robust design solution. The proposed method is applied to an optimization problem of a building case study. The building performance robustness of this building is assessed for uncertainties in user behavior, since, for many buildings, that is a parameter with a high uncertainty and a high influence on the building performance. Our study shows that the proposed method provides the decision maker with useful information for the decision making process
