This paper studies simulation-based optimization with multiple outputs. It assumes that the simulation model has one random objective function and must satisfy given constraints on the other random outputs. It presents a statistical procedure for testing whether a specific input combination...

This paper derives a novel procedure for testing the Karush-Kuhn-Tucker (KKT) first-order optimality conditions in models with multiple random responses.Such models arise in simulation-based optimization with multivariate outputs. This paper focuses on expensive simulations, which have small...

This contribution discusses experiments with many factors: the case study includes a simulation model with 92 factors. The experiments are guided by sequential bifurcation. This method is most efficient and effective if the true input/output behavior of the simulation model can be approximated...

Textbooks on Design of Experiments invariably start by explaining why one-factor-at-a-time (OAT) is an inferior method. Here we will show that in a model with all interactions a variant of OAT is extremely efficient, provided that we only have non-negative parameters and that there are only a...