Abstract: Factor screening searches for the really important inputs (factors) among the many inputs that are changed in a realistic simulation experiment. Sequential bifurcation (SB) is a sequential method that changes groups of inputs simultaneously. SB is the most efficient and effective...

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...

Abstract: Factor screening searches for the really important inputs (factors) among the many inputs that are changed in a realistic simulation experiment. Sequential bifurcation (or SB) is a sequential method that changes groups of inputs simultaneously. SB is the most e¢ cient and effective...

Optimization of simulated systems is the goal of many methods, but most methods as- sume known environments. We, however, develop a `robust' methodology that accounts for uncertain environments. Our methodology uses Taguchi's view of the uncertain world, but replaces his statistical techniques...

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 test- ing whether a specific input combination...

The goal of factor screening is to find the really important inputs (factors) among the many inputs that may be changed in a realistic simulation experiment. A specific method is sequential bifurcation (SB), which is a sequential method that changes groups of inputs simultaneously. SB is most...

Introduction -- Classic regression metamodels and their designs -- Classic assumptions versus simulation practice -- Screening the many inputs of realistic simulation models -- Kriging metamodels and their designs -- Simulation optimization.