To avoid biased sampling linked to the arbitrary choice of the repair operator, we propose to instead use the repair operator on the stochastic sampling before optimization. This repair method is referred as pre-repair process. Instead of a qNEHVI optimization following BoTorch implementation with their native constraint handling (as we described in section 2), the repaired stochastic samples are directly ranked by HVI values, and the best candidates are suggested for evaluation. Fig. illustrates the difference between post-and pre-repair methods on a simple 2D case scenario with a single optimization direction (red arrow). We see that the post-repair process favours infeasible samples that are optimizing the objective, even though these points become suboptimal after the repairing operation. Therefore, even when the repair function redistributes the infeasible points uniformly on the constraint boundary, like in this simple example, post-repair might lead to a less optimal selection of the candidates.