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Generalized Response Surface Methodology: A New Metaheuristic

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Author Info

  • Kleijnen, J.P.C.

    (Tilburg University, Center for Economic Research)

Abstract

Generalized Response Surface Methodology (GRSM) is a novel general-purpose metaheuristic based on Box and Wilson.s Response Surface Methodology (RSM).Both GRSM and RSM estimate local gradients to search for the optimal solution.These gradients use local first-order polynomials.GRSM, however, uses these gradients to estimate a better search direction than the steepest ascent direction used by RSM.Moreover, GRSM allows multiple responses, selecting one response as goal and the other responses as constrained variables.Finally, these estimated gradients may be used to test whether the estimated solution is indeed optimal.The focus of this paper is optimization of simulated systems.

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Bibliographic Info

Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2006-77.

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Date of creation: 2006
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Handle: RePEc:dgr:kubcen:200677

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Web page: http://center.uvt.nl

Related research

Keywords: experimental design; multivariate regression analysis; least squares; Karush-Kuhn-Tucker conditions; bootstrap;

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  1. Kleijnen, Jack P.C. & den Hertog, Dick & Angun, Ebru, 2006. "Response surface methodology's steepest ascent and step size revisited: Correction," European Journal of Operational Research, Elsevier, vol. 170(2), pages 664-666, April.
  2. Kleijnen, J.P.C. & Hertog, D. den & Angun, M.E., 2002. "Response Surface Methodology's Steepest Ascent and Step Size Revisited," Discussion Paper 2002-64, Tilburg University, Center for Economic Research.
  3. Kleijnen, J.P.C., 1993. "Simulation and optimization in production planning: a case study," Open Access publications from Tilburg University urn:nbn:nl:ui:12-369798, Tilburg University.
  4. Kleijnen, J.P.C., 1997. "Experimental Design for Sensitivity Analysis, Optimization and Validation of Simulation Models," Discussion Paper 1997-52, Tilburg University, Center for Economic Research.
  5. Kleijnen, J.P.C. & Wan, J., 2006. "Optimization of Simulated Inventory Systems: OptQuest and Alternatives," Discussion Paper 2006-75, Tilburg University, Center for Economic Research.
  6. Kleijnen, J.P.C., 2006. "White Noise Assumptions Revisited: Regression Models and Statistical Designs for Simulation Practice," Discussion Paper 2006-50, Tilburg University, Center for Economic Research.
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