This chapter surveys two methods for the optimization of real-world systems that are modelled through simulation. These … experiment; this design fixes the input combinations of the simulation model. These regression models uses a sequence of local …-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 …

simulated system. EGO treats the simulation model as a black-box, and balances local and global searches. In deterministic … simulation, EGO uses ordinary Kriging (OK), which is a special case of universal Kriging (UK). In our EGO variant we use … intrinsic Kriging (IK), which eliminates the need to estimate the parameters that quantify the trend in UK. In random simulation …

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 …

. As a novel metamodel we introduce intrinsic Kriging, for either deterministic or random simulation. For deterministic … simulation we study the famous 'e fficient global optimization' (EGO) method, substituting intrinsic Kriging for universal … Kriging. For random simulation we investigate a state-of-the-art two-stage algorithm accounting for heteroscedastic variances …

Kriging provides metamodels for deterministic and random simulation models. Actually, there are several types of … estimation of the trend in the input-output data of the underlying simulation model; this estimation deteriorates the Kriging … replications that varies with the input combination of the simulation model. To compare the performance of intrinsic Kriging and …

In practice, most computers generate simulation outputs sequentially, so it is attractive to analyze these outputs … experiment #1 the simulation outputs are normal. This experiment suggests that Wald (1945)’s SPRT with estimated variance gives …

This paper studies simulation-based optimization with multiple outputs. It assumes that the simulation model has one …

This article illustrates simulation optimization through an (s, S) inventory management system. In this system, the … satisfied for some random simulation responses, namely the service or fill rate, and for some deterministic simulation inputs …