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2015 | OriginalPaper | Buchkapitel

1. Introduction

verfasst von : Jack P. C. Kleijnen

Erschienen in: Design and Analysis of Simulation Experiments

Verlag: Springer International Publishing

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Abstract

This chapter is organized as follows. Section 1.1 defines various types of simulation. Section 1.2 defines design and analysis of simulation experiments (DASE). Section 1.3 defines DASE symbols and terms. The chapter ends with Solutions of exercises, and references.

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Nächstes Kapitel Classic Regression Metamodels and Their Designs

Acar E, Rais-Rohani M (2009) Ensemble of metamodels with optimized weight factors. Struct Multidiscip Optim 37(3):279–294CrossRef

Buchholz A, Holländer N, Sauerbrei W (2008) On properties of predictors derived with a two-step bootstrap model averaging approach—a simulation study in the linear regression model. Comput Stat Data Anal 52:2778–2793CrossRef

Borgonovo E, Plischke E (2015) Sensitivity analysis: a review of recent advances. Eur J Oper Res (in press)

Can B, Heavey C (2012) A comparison of genetic programming and artificial neural networks in metamodeling of discrete-event simulation models. Comput Oper Res 39(2):424–436CrossRef

Clarke SM, Griebsch JH, Simpson TW (2005) Analysis of support vector regression for approximation of complex engineering analyses. ASME J Mech Des 127(6):1077–1087CrossRef

Conover WJ (1999) Practical nonparametric statistics, 3rd edn. Wiley, New York

Conway RW (1963) Some tactical problems in digital simulation. Manag Sci 10(1):47–61CrossRef

De Rocquigny E, Devictor N, Tarantola S (2008) Uncertainty settings and natures of uncertainty. In: de Rocquigny E, Devictor N (eds) Tarantola suncertainty in industrial practice. Wiley, ChichesterCrossRef

Dette H, Pepelyshev A (2010) NPUA: a new approach for the analysis of computer experiments. Chemom Intell Lab Syst 104(2):333–340CrossRef

Efron B (2005) Bayesians, frequentists, and scientists. J Am Stat Assoc 100(469):1–5CrossRef

Evans JR, Olson DL (1998) Introduction to simulation and risk analysis. Prentice-Hall, Upper Saddle River

Fang K-T, Li R, Sudjianto A (2006) Design and modeling for computer experiments. Chapman & Hall/CRC, London

Forrester AIJ, Keane AJ (2009) Recent advances in surrogate-based optimization. Prog Aerosp Sci 45(1–3):50–79CrossRef

Forrester A, Sóbester A, Keane A (2008) Engineering design via surrogate modelling: a practical guide. Wiley, ChichesterCrossRef

Forrester JW (1961) Industrial dynamics. MIT, Cambridge

Giambiasi N, Carmona JC (2006) Generalized discrete event abstraction of continuous systems: GDEVS formalism. Simul Model Pract Theory 14(1):47–70CrossRef

Gorissen D (2010) Grid-enabled adaptive surrogate modeling for computer aided engineering. Ph.D. dissertation, Ghent University, Ghent

Grubler, A., Y. Ermoliev, and A. Kryazhimskiy (2015), Coping with uncertainties-examples of modeling approaches at IIASA. Technological Forecasting and Social Change, in press

Harari O, Steinberg DM (2014) Convex combination of Gaussian processes for Bayesian analysis of deterministic computer experiments. Technometrics 56(4):443–454CrossRef

Helton JC, Davis FJ (2003) Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliab Eng Syst Saf 81:23–69CrossRef

Helton JC, Hansen CW, Swift PN (2014) Performance assessment for the proposed high-level radioactive waste repository at Yucca mountain, Nevada. Reliab Eng Syst Saf 122:1–6CrossRef

Henderson SG, Nelson BL (eds) (2006) Handbooks in operations research and management science, vol 13. North-Holland, Amsterdam

Ho Y, Cao X (1991) Perturbation analysis of discrete event dynamic systems. Kluwer, DordrechtCrossRef

Kamiński, B. (2015) Interval metamodels for the analysis of simulation input-output relations. Simulation Modelling Practice and Theory, 54:86–100CrossRef

Karplus WJ (1983) The spectrum of mathematical models. Perspect Comput 3(2):4–13

Kleijnen JPC (1975) A comment on Blanning’s metamodel for sensitivity analysis: the regression metamodel in simulation. Interfaces 5(3):21–23CrossRef

Kleijnen JPC (1987) Statistical tools for simulation practitioners. Marcel Dekker, New York

Kleijnen JPC (2007) Risk analysis: frequentist and Bayesians unite! In: Yücesan E (ed) Proceedings of the 2007 INFORMS Simulation Society Research Workshop, Fontainebleau, pp 61–65

Kleijnen JPC (2008) Design and analysis of simulation experiments. Springer, New York

Law AM (2015) Simulation modeling and analysis, 5th edn. McGraw-Hill, Boston

Levy S, Steinberg DM (2010) Computer experiments: a review. AStA Adv Stat Anal 94(4):311–324CrossRef

Mordecai Y, Dori D (2013) Model-based risk-oriented robust systems design with object-process methodology. Int J Strateg Eng Asset Manag 1(4):331–354CrossRef

Müller J, Shoemaker CA (2014) Influence of ensemble surrogate models and sampling strategy on the solution quality of algorithms for computationally expensive black-box global optimization problems. J Glob Optim 60:123–144CrossRef

Nance RE, Sargent RG (2002) Perspectives on the evolution of simulation. Oper Res 50(1):161–172CrossRef

Nelson BL (2013) Foundations and methods of stochastic simulation; a first course. Springer, New YorkCrossRef

Oden JT (2006) Revolutionizing engineering science through simulation. National Science Foundation (NSF) Blue Ribbon Panel on Simulation-based Engineering Science. National Science Foundation, Arlington

Picheny V, Ginsbourger D, Richet Y, Caplin G (2013) Quantile-based optimization of noisy computer experiments with tunable precision, including comments and rejoinder. Technometrics 55(1):1–36CrossRef

Pidd M (2004)Computer simulation in management science, 5th edn. Wiley, Chichester

Poropudas J, Virtanen K (2008) Game theoretic approach to air combat simulation analysis. Systems Analysis Laboratory, Helsinki University of Technology

Rasmussen CE, Williams C (2006) Gaussian processes for machine learning. MIT, Cambridge

Razavi S, Gupta HV (2015) What do we mean by sensitivity analysis? The need for comprehensive characterization of “global” sensitivity in earth and environmental systems models. Water Resour Res 51 (in press)

Razavi S, Tolson BA, Burn DH (2012) Review of surrogate modeling in water resources. Water Resour Res 48, W07401:1–322

Regis RG (2014) Constrained optimization by radial basis function interpolation for high-dimensional expensive black-box problems with infeasible initial points. Eng Optim 46(2):218–243CrossRef

Rieck K et al (2012) Support vector machines. In: Gentle JE, Haerdle W, Mori Y (eds) Handbook of computational statistics, concepts and fundamentals, vol 1, 2nd edn. Springer, Heidelberg, pp 883–926CrossRef

Sakallı ÜS, Baykoç ÖF (2011) An optimization approach for brass casting blending problem under aletory and epistemic uncertainties. Int J Prod Econ 133(2):708–718CrossRef

Saltelli, Ratto M, Tarantola S, Campolongo F (2005) Sensitivity analysis of chemical models. Chem Rev 105(7):2811–2827CrossRef

Sanchez SM, Lucas TW, Sanchez PJ, Nannini CJ, Wan H (2012) Chapter 12: designs for large-scale simulation experiments, with applications to defense and homeland security. In: Hinkelmann K (ed) Design and analysis of experiments, volume 3, special designs and applications. Wiley, New York, pp 413–442CrossRef

Santner TJ, Williams BJ, Notz WI (2003) The design and analysis of computer experiments. Springer, New YorkCrossRef

Santos MI, Santos PM (2008) Sequential experimental designs for nonlinear regression metamodels in simulation. Simul Model Pract Theory 16(9):1365–1378CrossRef

Schriber TJ (2009) Simulation for the masses: spreadsheet-based Monte Carlo simulation. In: Rossetti MD, Hill RR, Johansson B, Dunkin A, Ingalls RG (eds) Proceedings of the 2009 Winter Simulation Conference, Austin, pp 1–11

Shepard D (1968) A two-dimensional interpolation function for irregularly-spaced data. In: Proceedings of the 1968 ACM National Conference, New York, pp 517–524. doi:10.1145/800186.810616

Shubik M (2002) Game theory and operations research: some musings 50 years later. Oper Res 50(1):192–196CrossRef

Simchi-Levi D, Kaminsky P, Simchi-Levi E (2003) Designing and managing the supply chain: concepts, strategies, and case studies, 2nd edn. Irwin/McGraw-Hill, Boston

Sprent P, Smeeton NC (2007) Applied nonparametric statistical methods, 4th edn. Chapman & Hall/CRC, Atlanta

Sterman JD (2000) Business dynamics: systems thinking and modeling for a complex world. McGraw-Hill, Homewood

Storlie C, Swiler L, Helton J, Sallaberry C (2009) Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models, SAND report 2008-6570. Sandia, Albuquerque

Swain JJ (2005) “Gaming” reality. OR/MS Today 32(6):44–55

Swain JJ (2013) Simulation software: a better reality? OR/MS Today 40(5):48–59

Tan MHY (2014) Stochastic polynomial interpolation for uncertainty quantification with computer experiments. Technometrics (in press)

Van Gelder L, Das P, Janssen H, Roels S (2014) Comparative study of metamodelling techniques in building energy simulation: guidelines for practitioners. Simul Model Pract Theory 49:245–257CrossRef

Viana FAC, Simpson TW, Balabanov V, Toropov V (2014) Metamodeling in multidisciplinary design optimization: how far have we really come? AIAA J 52(4):670–690CrossRef

Villa-Vialaneix N, Follador M, Ratto M, Leip A (2012) A comparison of eight metamodeling techniques for the simulation of N 2O fluxes and N leaching from corn crops. Environ Model Softw 34:51–66CrossRef

Vose D (2000) Risk analysis; a quantitative guide, 2nd edn. Wiley, Chichester

Wang C, Duan Q, Gong W, Ye A, Di Z, Miao C (2014) An evaluation of adaptive surrogate modeling based optimization with two benchmark problems. Environ Model Softw 60:167–179CrossRef

Wiebenga JH (2014) Robust design and optimization of forming processes. Ph.D. thesis, University of Twente, Enschede

Wu DD, Olson DL (2013) Computational simulation and risk analysis: an introduction of state of the art research. Math Comput Model 58(9–10):1581–1587CrossRef

Xie W, Nelson BL, Barton RR (2014) A Bayesian framework for quantifying uncertainty in stochastic simulation. Oper Res 62(6):1439–1452CrossRef

Zeigler BP, Praehofer H, Kim TG (2000) Theory of modeling and simulation, 2nd edn. Academic, San Diego

Zhu P, Zhang Y, Chen G (2011) Metamodeling development for reliability-based design optimization of automotive body structure. Comput Ind 62(7):729–741CrossRef

Titel: Introduction
verfasst von: Jack P. C. Kleijnen
Verlag: Springer International Publishing
Buch: Design and Analysis of Simulation Experiments
Print ISBN: 978-3-319-18086-1

Electronic ISBN: 978-3-319-18087-8

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-18087-8_1