, (ii) uncertainty or risk analysis, (iii) screening, (iv) validation, and (v) optimization. The main question is: when … following five stages in the analysis of a simulation model. 1) Validation: the availability of data on the real system … determines which type of statistical technique to use for validation. 2) Screening: in the simulation's pilot phase the really …

simulation.This analysis uses regression analysis to approximate the input/output transformation that is implied by the … simulation model; the resulting regression model is also known as metamodel, response surface, compact model, emulator, etc ….Regression analysis gives better results when the simulation experiment is well designed, using classical statistical designs (such as …

combination of the following four proven techniques: (1) discrete-event simulation, (2) heuristic optimization, (3) risk or … uncertainty analysis, and (4) bootstrapping.This methodology is illustrated through a case study on production control systems …

This paper proposes a novel method to select an experimental design for interpolation in simulation.Though the paper … focuses on Kriging in deterministic simulation, the method also applies to other types of metamodels (besides Kriging), and to … stochastic simulation.The paper focuses on simulations that require much computer time, so it is important to select a design …