Monte Carlo methods are simulation algorithms to estimate a numerical quantity in a statistical model of a real system … stimulated simulation analysts to develop ever more realistic models, so that the net result has not been faster execution of … simulation experiments; e.g., some modern simulation models need hours or days for a single 'run' (one replication of one …

, develop a `robust' methodology that accounts for uncertain environments. Our methodology uses Taguchi's view of the uncertain …

simulation models. This contribution discusses the different types of DOE for these three domains, but focusses on random … simulation. DOE may have two goals: sensitivity analysis including factor screening and optimization. This contribution starts …

, however, develops a 'robust' methodology for uncertain environments. This methodology uses Taguchi's view of the uncertain …

experiment; this design fixes the input combinations of the simulation model. These regression models uses a sequence of local …This chapter surveys two methods for the optimization of real-world systems that are modelled through simulation. These …-estimated through sequential designs. "Robust" optimization may use RSM or Kriging, and accounts for uncertainty in simulation inputs …

An important goal of simulation is optimization of the corresponding real system. We focus on simulation models with …, we treat the simulation model as a black box. We assume that the simulation is computationally expensive; therefore, we … use an inexpensive metamodel (approximation, emulator, surrogate) of the simulation model. A popular metamodel type is a …

This article uses a sequentialized experimental design to select simulation input combinations for global optimization …/output data of the simulation model (computer code). This design and analysis adapt the classic "expected improvement" (EI) in … estimator uses parametric bootstrapping. Classic EI and bootstrapped EI are compared through various test functions, including …