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A methodology for fitting and validating metamodels in simulation

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Author Info

  • Kleijnen, Jack P. C.
  • Sargent, Robert G.

Abstract

This expository paper discusses the relationships among metamodels, simulation models, and problem entities. A metamodel or response surface is an approximation of the input/output function implied by the underlying simulation model. There are several types of metamodel: linear regression, splines, neural networks, etc. This paper distinguishes between fitting and validating a metamodel. Metamodels may have different goals: (i) understanding, (ii) prediction, (iii) optimization, and (iv) verification and validation. For this metamodeling, a process with thirteen steps is proposed. Classic design of experiments (DOE) is summarized, including standard measures of fit such as the R-square coefficient and cross-validation measures. This DOE is extended to sequential or stagewise DOE. Several validation criteria, measures, and estimators are discussed. Metamodels in general are covered, along with a procedure for developing linear regression (including polynomial) metamodels.

(This abstract was borrowed from another version of this item.)

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File URL: http://www.sciencedirect.com/science/article/B6VCT-3XMPN89-2/2/9c52750398bfbcf28fdd6db2d0240b5b
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Bibliographic Info

Article provided by Elsevier in its journal European Journal of Operational Research.

Volume (Year): 120 (2000)
Issue (Month): 1 (January)
Pages: 14-29

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Handle: RePEc:eee:ejores:v:120:y:2000:i:1:p:14-29

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Web page: http://www.elsevier.com/locate/eor

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References

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  1. Kleijnen, J.P.C., 1992. "Verification and validation of simulation models," Research Memorandum 542, Tilburg University, Faculty of Economics and Business Administration.
  2. Jack P. C. Kleijnen, 1992. "Regression Metamodels for Simulation with Common Random Numbers: Comparison of Validation Tests and Confidence Intervals," Management Science, INFORMS, vol. 38(8), pages 1164-1185, August.
  3. Kleijnen, J.P.C., 1995. "Case study: Statistical validation of simulation models," Open Access publications from Tilburg University urn:nbn:nl:ui:12-369796, Tilburg University.
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Citations

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Cited by:
  1. Husslage, B.G.M. & Dam, E.R. van & Hertog, D. den & Stehouwer, H.P. & Stinstra, E., 2003. "Coordination of Coupled Black Box Simulations in the Construction of Metamodels," Discussion Paper 2003-2, Tilburg University, Center for Economic Research.
  2. Durieux, Severine & Pierreval, Henri, 2004. "Regression metamodeling for the design of automated manufacturing system composed of parallel machines sharing a material handling resource," International Journal of Production Economics, Elsevier, vol. 89(1), pages 21-30, May.
  3. Graham, Tennille, 2005. "On the Road to Better Management: An investigation into the benefits of managing the impacts of dryland salinity on roads," 2005 Conference (49th), February 9-11, 2005, Coff's Harbour, Australia 137921, Australian Agricultural and Resource Economics Society.
  4. Marjolijn Haasnoot & Hans Middelkoop & Astrid Offermans & Eelco Beek & Willem Deursen, 2012. "Exploring pathways for sustainable water management in river deltas in a changing environment," Climatic Change, Springer, vol. 115(3), pages 795-819, December.
  5. Vonk Noordegraaf, Antonie & Nielen, Mirjam & Kleijnen, Jack P. C., 2003. "Sensitivity analysis by experimental design and metamodelling: Case study on simulation in national animal disease control," European Journal of Operational Research, Elsevier, vol. 146(3), pages 433-443, May.
  6. Galelli, S. & Gandolfi, C. & Soncini-Sessa, R. & Agostani, D., 2010. "Building a metamodel of an irrigation district distributed-parameter model," Agricultural Water Management, Elsevier, vol. 97(2), pages 187-200, February.
  7. Poropudas, Jirka & Virtanen, Kai, 2011. "Simulation metamodeling with dynamic Bayesian networks," European Journal of Operational Research, Elsevier, vol. 214(3), pages 644-655, November.
  8. Ekren, Orhan & Ekren, Banu Yetkin, 2008. "Size optimization of a PV/wind hybrid energy conversion system with battery storage using response surface methodology," Applied Energy, Elsevier, vol. 85(11), pages 1086-1101, November.
  9. Reis dos Santos, Pedro M. & Isabel Reis dos Santos, M., 2009. "Using subsystem linear regression metamodels in stochastic simulation," European Journal of Operational Research, Elsevier, vol. 196(3), pages 1031-1040, August.
  10. Noguera, Jose H. & Watson, Edward F., 2006. "Response surface analysis of a multi-product batch processing facility using a simulation metamodel," International Journal of Production Economics, Elsevier, vol. 102(2), pages 333-343, August.
  11. Strang, Kenneth David, 2012. "Importance of verifying queue model assumptions before planning with simulation software," European Journal of Operational Research, Elsevier, vol. 218(2), pages 493-504.
  12. Batmaz, Inci & Tunali, Semra, 2003. "Small response surface designs for metamodel estimation," European Journal of Operational Research, Elsevier, vol. 145(2), pages 455-470, March.
  13. Kleijnen, Jack P.C. & Mehdad, E., 2012. "Kriging in Multi-response Simulation, including a Monte Carlo Laboratory (Replaced by 2014-012)," Discussion Paper 2012-039, Tilburg University, Center for Economic Research.
  14. Ekren, Orhan & Ekren, Banu Y. & Ozerdem, Baris, 2009. "Break-even analysis and size optimization of a PV/wind hybrid energy conversion system with battery storage - A case study," Applied Energy, Elsevier, vol. 86(7-8), pages 1043-1054, July.
  15. Wise, Russell M. & Cacho, Oscar J., 2006. "Optimal Land-Use Decisions in the Presence of Carbon Payments and Fertilizer Subsidies: An Indonesian Case Study," 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia 25356, International Association of Agricultural Economists.
  16. Tunali, S. & Batmaz, I., 2003. "A metamodeling methodology involving both qualitative and quantitative input factors," European Journal of Operational Research, Elsevier, vol. 150(2), pages 437-450, October.
  17. Shi, Wen & Shang, Jennifer & Liu, Zhixue & Zuo, Xiaolu, 2014. "Optimal design of the auto parts supply chain for JIT operations: Sequential bifurcation factor screening and multi-response surface methodology," European Journal of Operational Research, Elsevier, vol. 236(2), pages 664-676.
  18. van der Gaag, Monique A. & Vos, Fred & Saatkamp, Helmut W. & van Boven, Michiel & van Beek, Paul & Huirne, Ruud B. M., 2004. "A state-transition simulation model for the spread of Salmonella in the pork supply chain," European Journal of Operational Research, Elsevier, vol. 156(3), pages 782-798, August.
  19. Marrel, Amandine & Iooss, Bertrand & Van Dorpe, Fran├žois & Volkova, Elena, 2008. "An efficient methodology for modeling complex computer codes with Gaussian processes," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4731-4744, June.

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