IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v261y2017i1p279-301.html
   My bibliography  Save this article

Comparison of Kriging-based algorithms for simulation optimization with heterogeneous noise

Author

Listed:
  • Jalali, Hamed
  • Van Nieuwenhuyse, Inneke
  • Picheny, Victor

Abstract

In this article we investigate the unconstrained optimization (minimization) of the performance of a system that is modeled through a discrete-event simulation. In recent years, several algorithms have been proposed which extend the traditional Kriging-based simulation optimization algorithms (assuming deterministic outputs) to problems with noise. Our objective in this paper is to compare the relative performance of a number of these algorithms on a set of well-known analytical test functions, assuming different patterns of heterogeneous noise. We also apply the algorithms to a popular inventory test problem. The conclusions and insights obtained may serve as a useful guideline for researchers aiming to apply Kriging-based algorithms to solve engineering and/or business problems, and may be useful in the development of future algorithms.

Suggested Citation

  • Jalali, Hamed & Van Nieuwenhuyse, Inneke & Picheny, Victor, 2017. "Comparison of Kriging-based algorithms for simulation optimization with heterogeneous noise," European Journal of Operational Research, Elsevier, vol. 261(1), pages 279-301.
    • Handle: RePEc:eee:ejores:v:261:y:2017:i:1:p:279-301
    DOI: 10.1016/j.ejor.2017.01.035
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037722171730070X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2017.01.035?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. McCormack, Richard & Coates, Graham, 2015. "A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival," European Journal of Operational Research, Elsevier, vol. 247(1), pages 294-309.
    2. D. Huang & T. Allen & W. Notz & N. Zeng, 2006. "Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models," Journal of Global Optimization, Springer, vol. 34(3), pages 441-466, March.
    3. Jack Kleijnen & Wim Beers & Inneke Nieuwenhuyse, 2012. "Expected improvement in efficient global optimization through bootstrapped kriging," Journal of Global Optimization, Springer, vol. 54(1), pages 59-73, September.
    4. Hamed Jalali & Inneke Van Nieuwenhuyse, 2015. "Simulation optimization in inventory replenishment: a classification," IISE Transactions, Taylor & Francis Journals, vol. 47(11), pages 1217-1235, November.
    5. Kleijnen, Jack P.C., 2009. "Kriging metamodeling in simulation: A review," European Journal of Operational Research, Elsevier, vol. 192(3), pages 707-716, February.
    6. Picheny, Victor & Ginsbourger, David, 2014. "Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1035-1053.
    7. Kleijnen, Jack P. C. & van Beers, Wim C. M., 2005. "Robustness of Kriging when interpolating in random simulation with heterogeneous variances: Some experiments," European Journal of Operational Research, Elsevier, vol. 165(3), pages 826-834, September.
    8. Lihua Sun & L. Jeff Hong & Zhaolin Hu, 2014. "Balancing Exploitation and Exploration in Discrete Optimization via Simulation Through a Gaussian Process-Based Search," Operations Research, INFORMS, vol. 62(6), pages 1416-1438, December.
    9. Peter Frazier & Warren Powell & Savas Dayanik, 2009. "The Knowledge-Gradient Policy for Correlated Normal Beliefs," INFORMS Journal on Computing, INFORMS, vol. 21(4), pages 599-613, November.
    10. Bruce Ankenman & Barry L. Nelson & Jeremy Staum, 2010. "Stochastic Kriging for Simulation Metamodeling," Operations Research, INFORMS, vol. 58(2), pages 371-382, April.
    11. Justin Boesel & Barry L. Nelson & Seong-Hee Kim, 2003. "Using Ranking and Selection to “Clean Up” after Simulation Optimization," Operations Research, INFORMS, vol. 51(5), pages 814-825, October.
    12. Saif, Ahmed & Elhedhli, Samir, 2016. "Cold supply chain design with environmental considerations: A simulation-optimization approach," European Journal of Operational Research, Elsevier, vol. 251(1), pages 274-287.
    13. L. Jeff Hong & Barry L. Nelson & Jie Xu, 2015. "Discrete Optimization via Simulation," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 9-44, Springer.
    14. Naoum-Sawaya, Joe & Ghaddar, Bissan & Arandia, Ernesto & Eck, Bradley, 2015. "Simulation-optimization approaches for water pump scheduling and pipe replacement problems," European Journal of Operational Research, Elsevier, vol. 246(1), pages 293-306.
    15. Ning Quan & Jun Yin & Szu Ng & Loo Lee, 2013. "Simulation optimization via kriging: a sequential search using expected improvement with computing budget constraints," IISE Transactions, Taylor & Francis Journals, vol. 45(7), pages 763-780.
    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. Songhao Wang & Szu Hui Ng & William Benjamin Haskell, 2022. "A Multilevel Simulation Optimization Approach for Quantile Functions," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 569-585, January.
    2. Dawei Zhan & Huanlai Xing, 2020. "Expected improvement for expensive optimization: a review," Journal of Global Optimization, Springer, vol. 78(3), pages 507-544, November.
    3. Xuefei Lu & Alessandro Rudi & Emanuele Borgonovo & Lorenzo Rosasco, 2020. "Faster Kriging: Facing High-Dimensional Simulators," Operations Research, INFORMS, vol. 68(1), pages 233-249, January.
    4. Rodriguez, Sergio & Ludkovski, Michael, 2020. "Probabilistic bisection with spatial metamodels," European Journal of Operational Research, Elsevier, vol. 286(2), pages 588-603.
    5. Qun Meng & Songhao Wang & Szu Hui Ng, 2022. "Combined Global and Local Search for Optimization with Gaussian Process Models," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 622-637, January.
    6. Pedrielli, Giulia & Wang, Songhao & Ng, Szu Hui, 2020. "An extended Two-Stage Sequential Optimization approach: Properties and performance," European Journal of Operational Research, Elsevier, vol. 287(3), pages 929-945.
    7. Rojas Gonzalez, Sebastian & Jalali, Hamed & Van Nieuwenhuyse, Inneke, 2020. "A multiobjective stochastic simulation optimization algorithm," European Journal of Operational Research, Elsevier, vol. 284(1), pages 212-226.
    8. Zilong Wang & Marianthi Ierapetritou, 2018. "Surrogate-based feasibility analysis for black-box stochastic simulations with heteroscedastic noise," Journal of Global Optimization, Springer, vol. 71(4), pages 957-985, August.

    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. Qun Meng & Songhao Wang & Szu Hui Ng, 2022. "Combined Global and Local Search for Optimization with Gaussian Process Models," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 622-637, January.
    2. Xuefei Lu & Alessandro Rudi & Emanuele Borgonovo & Lorenzo Rosasco, 2020. "Faster Kriging: Facing High-Dimensional Simulators," Operations Research, INFORMS, vol. 68(1), pages 233-249, January.
    3. 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.
    4. Kleijnen, Jack P.C., 2013. "Simulation-Optimization via Kriging and Bootstrapping : A Survey (Revision of CentER DP 2011-064)," Other publications TiSEM 6ac4e049-ad86-447f-aeec-a, Tilburg University, School of Economics and Management.
    5. Pedrielli, Giulia & Wang, Songhao & Ng, Szu Hui, 2020. "An extended Two-Stage Sequential Optimization approach: Properties and performance," European Journal of Operational Research, Elsevier, vol. 287(3), pages 929-945.
    6. Rojas Gonzalez, Sebastian & Jalali, Hamed & Van Nieuwenhuyse, Inneke, 2020. "A multiobjective stochastic simulation optimization algorithm," European Journal of Operational Research, Elsevier, vol. 284(1), pages 212-226.
    7. Ehsan Mehdad & Jack P. C. Kleijnen, 2018. "Efficient global optimisation for black-box simulation via sequential intrinsic Kriging," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(11), pages 1725-1737, November.
    8. Songhao Wang & Szu Hui Ng & William Benjamin Haskell, 2022. "A Multilevel Simulation Optimization Approach for Quantile Functions," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 569-585, January.
    9. Dawei Zhan & Huanlai Xing, 2020. "Expected improvement for expensive optimization: a review," Journal of Global Optimization, Springer, vol. 78(3), pages 507-544, November.
    10. Mehdad, E. & Kleijnen, Jack P.C., 2014. "Classic Kriging versus Kriging with Bootstrapping or Conditional Simulation : Classic Kriging's Robust Confidence Intervals and Optimization (Revised version of CentER DP 2013-038)," Other publications TiSEM 4915047b-afe4-4fc7-8a1c-4, Tilburg University, School of Economics and Management.
    11. Mehdad, E. & Kleijnen, Jack P.C., 2014. "Global Optimization for Black-box Simulation via Sequential Intrinsic Kriging," Other publications TiSEM 8fa8d96f-a086-4c4b-88ab-9, Tilburg University, School of Economics and Management.
    12. Morales-Enciso, Sergio & Branke, Juergen, 2015. "Tracking global optima in dynamic environments with efficient global optimization," European Journal of Operational Research, Elsevier, vol. 242(3), pages 744-755.
    13. 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.
    14. J P C Kleijnen & W C M van Beers, 2013. "Monotonicity-preserving bootstrapped Kriging metamodels for expensive simulations," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(5), pages 708-717, May.
    15. Kamiński, Bogumił, 2015. "A method for the updating of stochastic kriging metamodels," European Journal of Operational Research, Elsevier, vol. 247(3), pages 859-866.
    16. Zilong Wang & Marianthi Ierapetritou, 2018. "Surrogate-based feasibility analysis for black-box stochastic simulations with heteroscedastic noise," Journal of Global Optimization, Springer, vol. 71(4), pages 957-985, August.
    17. Peter Salemi & Jeremy Staum & Barry L. Nelson, 2019. "Generalized Integrated Brownian Fields for Simulation Metamodeling," Operations Research, INFORMS, vol. 67(3), pages 874-891, May.
    18. Peter L. Salemi & Eunhye Song & Barry L. Nelson & Jeremy Staum, 2019. "Gaussian Markov Random Fields for Discrete Optimization via Simulation: Framework and Algorithms," Operations Research, INFORMS, vol. 67(1), pages 250-266, January.
    19. Jing Xie & Peter I. Frazier & Stephen E. Chick, 2016. "Bayesian Optimization via Simulation with Pairwise Sampling and Correlated Prior Beliefs," Operations Research, INFORMS, vol. 64(2), pages 542-559, April.
    20. Tay, Timothy & Osorio, Carolina, 2022. "Bayesian optimization techniques for high-dimensional simulation-based transportation problems," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 210-243.

    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:eee:ejores:v:261:y:2017:i:1:p:279-301. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.