IDEAS home Printed from https://ideas.repec.org/a/pal/jorsoc/v60y2009i6d10.1057_palgrave.jors.2602614.html
   My bibliography  Save this article

Response surface methodology with stochastic constraints for expensive simulation

Author

Listed:
  • E Angün

    (Galatasaray University)

  • J Kleijnen

    (Tilburg University)

  • D den Hertog

    (Tilburg University)

  • G Gürkan

    (Tilburg University)

Abstract

This article investigates simulation-based optimization problems with a stochastic objective function, stochastic output constraints, and deterministic input constraints. More specifically, it generalizes classic response surface methodology (RSM) to account for these constraints. This Generalized RSM—abbreviated to GRSM—generalizes the estimated steepest descent—used in classic RSM—applying ideas from interior point methods, especially affine scaling. This new search direction is scale independent, which is important for practitioners because it avoids some numerical complications and problems commonly encountered. Furthermore, the article derives a heuristic that uses this search direction iteratively. This heuristic is intended for problems in which simulation runs are expensive, so that the search needs to reach a neighbourhood of the true optimum quickly. The new heuristic is compared with OptQuest, which is the most popular heuristic available with several simulation software packages. Numerical illustrations give encouraging results.

Suggested Citation

  • E Angün & J Kleijnen & D den Hertog & G Gürkan, 2009. "Response surface methodology with stochastic constraints for expensive simulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(6), pages 735-746, June.
    • Handle: RePEc:pal:jorsoc:v:60:y:2009:i:6:d:10.1057_palgrave.jors.2602614
    DOI: 10.1057/palgrave.jors.2602614
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/palgrave.jors.2602614
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/palgrave.jors.2602614?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Michael C. Fu, 2002. "Feature Article: Optimization for simulation: Theory vs. Practice," INFORMS Journal on Computing, INFORMS, vol. 14(3), pages 192-215, August.
    2. J. C. Helton & D. R. Anderson & M. G. Marietta & R. P. Rechard, 1997. "Performance Assessment for the Waste Isolation Pilot Plant: From Regulation to Calculation for 40 CFR 191.13," Operations Research, INFORMS, vol. 45(2), pages 157-177, April.
    3. Kleijnen, J.P.C. & van Beers, W.C.M. & van Nieuwenhuyse, I., 2008. "Constrained Optimization in Simulation : A Novel Approach," Other publications TiSEM e49ba0fc-853c-4a13-b564-d, Tilburg University, School of Economics and Management.
    4. den Hertog, Dick & Stehouwer, Peter, 2002. "Optimizing color picture tubes by high-cost nonlinear programming," European Journal of Operational Research, Elsevier, vol. 140(2), pages 197-211, July.
    5. Kleijnen, J.P.C. & van Beers, W.C.M. & van Nieuwenhuyse, I., 2008. "Constrained Optimization in Simulation : A Novel Approach," Discussion Paper 2008-95, Tilburg University, Center for Economic Research.
    6. 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.
    7. Ruud, Paul A., 2000. "An Introduction to Classical Econometric Theory," OUP Catalogue, Oxford University Press, number 9780195111644.
    8. S-K S Fan & E del Castillo, 1999. "Calculation of an optimal region of operation for dual response systems fitted from experimental data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(8), pages 826-836, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kuo-Hao Chang & L. Jeff Hong & Hong Wan, 2013. "Stochastic Trust-Region Response-Surface Method (STRONG)---A New Response-Surface Framework for Simulation Optimization," INFORMS Journal on Computing, INFORMS, vol. 25(2), pages 230-243, May.
    2. Antonio Del Prete & Rodolfo Franchi & Stefania Cacace & Quirico Semeraro, 2020. "Optimization of cutting conditions using an evolutive online procedure," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 481-499, February.
    3. M Laguna & J Molina & F Pérez & R Caballero & A G Hernández-Díaz, 2010. "The challenge of optimizing expensive black boxes: a scatter search/rough set theory approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 53-67, January.
    4. Satyajith Amaran & Nikolaos V. Sahinidis & Bikram Sharda & Scott J. Bury, 2016. "Simulation optimization: a review of algorithms and applications," Annals of Operations Research, Springer, vol. 240(1), pages 351-380, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Angun, M.E., 2004. "Black box simulation optimization : Generalized response surface methodology," Other publications TiSEM 2548e953-54ce-44e2-8c5b-7, Tilburg University, School of Economics and Management.
    2. 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.
    3. Kleijnen, J.P.C., 2008. "Design of Experiments : An Overview," Other publications TiSEM e05cfa13-127a-48c9-a5b6-7, Tilburg University, School of Economics and Management.
    4. Kleijnen, J.P.C., 2008. "Design of Experiments : An Overview," Discussion Paper 2008-70, Tilburg University, Center for Economic Research.
    5. Noordhoek, Marije & Dullaert, Wout & Lai, David S.W. & de Leeuw, Sander, 2018. "A simulation–optimization approach for a service-constrained multi-echelon distribution network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 292-311.
    6. Jan R. Magnus & Wendun Wang & Xinyu Zhang, 2016. "Weighted-Average Least Squares Prediction," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 1040-1074, June.
    7. Zheng, Liang & Xue, Xinfeng & Xu, Chengcheng & Ran, Bin, 2019. "A stochastic simulation-based optimization method for equitable and efficient network-wide signal timing under uncertainties," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 287-308.
    8. Nathan H. Miller, 2008. "Competition When Consumers Value Firm Scope," EAG Discussions Papers 200807, Department of Justice, Antitrust Division.
    9. Doko Tchatoka, Firmin Sabro, 2012. "Specification Tests with Weak and Invalid Instruments," MPRA Paper 40185, University Library of Munich, Germany.
    10. Helton, Jon C. & Johnson, Jay D. & Sallaberry, Cédric J., 2011. "Quantification of margins and uncertainties: Example analyses from reactor safety and radioactive waste disposal involving the separation of aleatory and epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 1014-1033.
    11. John M. Abowd & Francis Kramarz & Sébastien Pérez-Duarte & Ian M. Schmutte, 2018. "Sorting Between and Within Industries: A Testable Model of Assortative Matching," Annals of Economics and Statistics, GENES, issue 129, pages 1-32.
    12. Lullit Getachew & Robin C. Sickles, 2007. "The policy environment and relative price efficiency of Egyptian private sector manufacturing: 1987|88-1995|96," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(4), pages 703-728.
    13. Hugo Benítez-Silva & Debra Dwyer & Wayne-Roy Gayle & Thomas Muench, 2008. "Expectations in micro data: rationality revisited," Empirical Economics, Springer, vol. 34(2), pages 381-416, March.
    14. Roberto Aringhieri & Giuliana Carello & Daniela Morale, 2016. "Supporting decision making to improve the performance of an Italian Emergency Medical Service," Annals of Operations Research, Springer, vol. 236(1), pages 131-148, January.
    15. Jianxin Fang & Brenda Cheang & Andrew Lim, 2023. "Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey," Sustainability, MDPI, vol. 15(17), pages 1-44, August.
    16. Antonio Ciccone & Giovanni Peri, 2005. "Long-Run Substitutability Between More and Less Educated Workers: Evidence from U.S. States, 1950-1990," The Review of Economics and Statistics, MIT Press, vol. 87(4), pages 652-663, November.
    17. Gianmarco I.P. Ottaviano & Giovanni Peri, 2005. "Rethinking the Gains from Immigration: Theory and Evidence from the U.S," NBER Working Papers 11672, National Bureau of Economic Research, Inc.
    18. Warren B. Powell, 2010. "Rejoinder ---The Languages of Stochastic Optimization," INFORMS Journal on Computing, INFORMS, vol. 22(1), pages 23-25, February.
    19. Rose, Heather, 2006. "Do gains in test scores explain labor market outcomes?," Economics of Education Review, Elsevier, vol. 25(4), pages 430-446, August.
    20. Florian Heiss, 2016. "Discrete Choice Methods with Simulation," Econometric Reviews, Taylor & Francis Journals, vol. 35(4), pages 688-692, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:jorsoc:v:60:y:2009:i:6:d:10.1057_palgrave.jors.2602614. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave-journals.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.