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Sequentially extending space-filling experimental designs by optimally permuting and stacking columns of the design matrix

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  • Parker, J.D.
  • Lucas, T.W.
  • Carlyle, W.M.

Abstract

Researchers make available computationally expensive designs for computer experiments for widespread use by cataloging them and providing online links. This paper presents an algorithm that augments space-filling designs (SFDs) by optimally permuting and stacking columns of the design matrix to minimize the maximum absolute pairwise correlation among columns in the new extended design. The algorithm enables researchers to augment an SFD sequentially with batches of additional design points, which improves column orthogonality and adds more degrees of freedom for fitting metamodels. We show this method improves the correlation and space-filling properties of the resulting designs and allows us to extend some classes of designs to higher dimensions that are not easily obtainable. Moreover, the resulting extended designs compare well with many leading software-generated SFDs created from scratch in the extended design space.

Suggested Citation

  • Parker, J.D. & Lucas, T.W. & Carlyle, W.M., 2024. "Sequentially extending space-filling experimental designs by optimally permuting and stacking columns of the design matrix," European Journal of Operational Research, Elsevier, vol. 319(2), pages 600-610.
  • Handle: RePEc:eee:ejores:v:319:y:2024:i:2:p:600-610
    DOI: 10.1016/j.ejor.2024.06.020
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    References listed on IDEAS

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    1. Kleijnen, Jack P.C., 2017. "Regression and Kriging metamodels with their experimental designs in simulation: A review," European Journal of Operational Research, Elsevier, vol. 256(1), pages 1-16.
    2. Zachary C. Little & Jeffery D. Weir & Raymond R. Hill & Brian B. Stone & Jason K. Freels, 2019. "Second-order extensions to nearly orthogonal-and-balanced (NOAB) mixed-factor experimental designs," Journal of Simulation, Taylor & Francis Journals, vol. 13(3), pages 226-237, July.
    3. Jack P. C. Kleijnen & Susan M. Sanchez & Thomas W. Lucas & Thomas M. Cioppa, 2005. "State-of-the-Art Review: A User’s Guide to the Brave New World of Designing Simulation Experiments," INFORMS Journal on Computing, INFORMS, vol. 17(3), pages 263-289, August.
    4. Edwin R. van Dam & Gijs Rennen & Bart Husslage, 2009. "Bounds for Maximin Latin Hypercube Designs," Operations Research, INFORMS, vol. 57(3), pages 595-608, June.
    5. Borgonovo, Emanuele & Plischke, Elmar, 2016. "Sensitivity analysis: A review of recent advances," European Journal of Operational Research, Elsevier, vol. 248(3), pages 869-887.
    6. Thomas W. Lucas & W. David Kelton & Paul J. Sánchez & Susan M. Sanchez & Ben L. Anderson, 2015. "Changing the paradigm: Simulation, now a method of first resort," Naval Research Logistics (NRL), John Wiley & Sons, vol. 62(4), pages 293-303, June.
    7. Yaping Wang & Fasheng Sun & Hongquan Xu, 2022. "On Design Orthogonality, Maximin Distance, and Projection Uniformity for Computer Experiments," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(537), pages 375-385, January.
    8. Fasheng Sun & Boxin Tang, 2017. "A Method of Constructing Space-Filling Orthogonal Designs," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 683-689, April.
    9. V. Roshan Joseph & Evren Gul & Shan Ba, 2015. "Maximum projection designs for computer experiments," Biometrika, Biometrika Trust, vol. 102(2), pages 371-380.
    10. Vieira Jr., Hélcio & Sanchez, Susan & Kienitz, Karl Heinz & Belderrain, Mischel Carmen Neyra, 2011. "Generating and improving orthogonal designs by using mixed integer programming," European Journal of Operational Research, Elsevier, vol. 215(3), pages 629-638, December.
    11. A D MacCalman & H Vieira & T Lucas, 2017. "Second-order nearly orthogonal Latin hypercubes for exploring stochastic simulations," Journal of Simulation, Taylor & Francis Journals, vol. 11(2), pages 137-150, May.
    12. Eugene L. Lawler, 1963. "The Quadratic Assignment Problem," Management Science, INFORMS, vol. 9(4), pages 586-599, July.
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