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 …

This tutorial reviews the design and analysis of simulation experiments. These experiments may have various goals … the input combinations of the simulation model. However, before a regression or Kriging metamodel is applied, the many … inputs of the underlying realistic simulation model should be screened; the tutorial focuses on sequential bifurcation …

This contribution presents an overview of sensitivity analysis of simulation models, including the estimation of … also reviews factor screening for simulation models with very many factors, focusing on the so-called 'sequential … aim at the optimization of the simulated system, allowing multiple random simulation outputs …

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 …

This paper presents a novel heuristic for constrained optimization of random computer simulation models, in which one … of the simulation outputs is selected as the objective to be minimized while the other outputs need to satisfy … prespecified target values. Besides the simulation outputs, the simulation inputs must meet prespecified constraints including the …