IDEAS home Printed from https://ideas.repec.org/a/bpj/mcmeap/v4y1998i3p181-230n2.html
   My bibliography  Save this article

Techniques for Monte Carlo Optimizing

Author

Listed:
  • Arsham H.

Abstract

No abstract is available for this item.

Suggested Citation

  • Arsham H., 1998. "Techniques for Monte Carlo Optimizing," Monte Carlo Methods and Applications, De Gruyter, vol. 4(3), pages 181-230, December.
    • Handle: RePEc:bpj:mcmeap:v:4:y:1998:i:3:p:181-230:n:2
    DOI: 10.1515/mcma.1998.4.3.181
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/mcma.1998.4.3.181
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/mcma.1998.4.3.181?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. Yunker, James M. & Tew, Jeffrey D., 1994. "Simulation optimization by genetic search," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 37(1), pages 17-28.
    2. Azadivar, Farhad & Lee, Young-Hae, 1988. "Optimization of discrete variable stochastic systems by computer simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 30(4), pages 331-345.
    3. Sigrún Andradóttir, 1996. "Optimization of the Transient and Steady-State Behavior of Discrete Event Systems," Management Science, INFORMS, vol. 42(5), pages 717-737, May.
    4. Stephen M. Robinson, 1996. "Analysis of Sample-Path Optimization," Mathematics of Operations Research, INFORMS, vol. 21(3), pages 513-528, August.
    5. Gary Koehler, 1997. "New directions in genetic algorithm theory," Annals of Operations Research, Springer, vol. 75(0), pages 49-68, January.
    6. Douglas J. Morrice & Indranil R. Bardhan, 1995. "A Weighted Least Squares Approach to Computer Simulation Factor Screening," Operations Research, INFORMS, vol. 43(5), pages 792-806, October.
    7. Cao, Xi-Ren, 1996. "Perturbation analysis of discrete event systems: Concepts, algorithms, and applications," European Journal of Operational Research, Elsevier, vol. 91(1), pages 1-13, May.
    8. Sigrún Andradöttir, 1996. "A Scaled Stochastic Approximation Algorithm," Management Science, INFORMS, vol. 42(4), pages 475-498, April.
    9. Clark, Dean S., 1984. "Necessary and sufficient conditions for the Robbins-Monro method," Stochastic Processes and their Applications, Elsevier, vol. 17(2), pages 359-367, July.
    10. Russell R. Barton & John S. Ivey, Jr., 1996. "Nelder-Mead Simplex Modifications for Simulation Optimization," Management Science, INFORMS, vol. 42(7), pages 954-973, July.
    11. M. Hossein Safizadeh & Robert Signorile, 1994. "Optimization of Simulation via Quasi-Newton Methods," INFORMS Journal on Computing, INFORMS, vol. 6(4), pages 398-408, November.
    12. Futschik, A. & Pflug, G. Ch., 1997. "Optimal allocation of simulation experiments in discrete stochastic optimization and approximative algorithms," European Journal of Operational Research, Elsevier, vol. 101(2), pages 245-260, September.
    13. Joan M. Donohue & Ernest C. Houck & Raymond H. Myers, 1995. "Simulation Designs for the Estimation of Quadratic Response Surface Gradients in the Presence of Model Misspecification," Management Science, INFORMS, vol. 41(2), pages 244-262, February.
    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. Chen, M. & Liu, S. & Tieszen, L.L. & Hollinger, D.Y., 2008. "An improved state-parameter analysis of ecosystem models using data assimilation," Ecological Modelling, Elsevier, vol. 219(3), pages 317-326.
    2. Arsham Hossein, 2007. "Monte Carlo Techniques for Parametric Finite Multidimensional Integral Equations," Monte Carlo Methods and Applications, De Gruyter, vol. 13(3), pages 173-195, 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. Kao, Chiang & Chen, Shih-Pin, 2006. "A stochastic quasi-Newton method for simulation response optimization," European Journal of Operational Research, Elsevier, vol. 173(1), pages 30-46, August.
    2. 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.
    3. Arsham Hossein, 2007. "Monte Carlo Techniques for Parametric Finite Multidimensional Integral Equations," Monte Carlo Methods and Applications, De Gruyter, vol. 13(3), pages 173-195, August.
    4. Safizadeh, M. Hossein, 2002. "Minimizing the bias and variance of the gradient estimate in RSM simulation studies," European Journal of Operational Research, Elsevier, vol. 136(1), pages 121-135, January.
    5. Rosen, Scott L. & Harmonosky, Catherine M. & Traband, Mark T., 2007. "A simulation optimization method that considers uncertainty and multiple performance measures," European Journal of Operational Research, Elsevier, vol. 181(1), pages 315-330, August.
    6. Kleijnen, J.P.C., 2006. "Generalized Response Surface Methodology : A New Metaheuristic," Other publications TiSEM 9a11fb77-d2e1-4c6e-8bba-0, Tilburg University, School of Economics and Management.
    7. Hachicha, Wafik & Ammeri, Ahmed & Masmoudi, Faouzi & Chachoub, Habib, 2010. "A comprehensive literature classification of simulation optimisation methods," MPRA Paper 27652, University Library of Munich, Germany.
    8. 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.
    9. Yongchao Liu & Huifu Xu & Jane J. Ye, 2011. "Penalized Sample Average Approximation Methods for Stochastic Mathematical Programs with Complementarity Constraints," Mathematics of Operations Research, INFORMS, vol. 36(4), pages 670-694, November.
    10. David J. Eckman & Shane G. Henderson & Sara Shashaani, 2023. "Diagnostic Tools for Evaluating and Comparing Simulation-Optimization Algorithms," INFORMS Journal on Computing, INFORMS, vol. 35(2), pages 350-367, March.
    11. Yang, Taho & Chou, Pohung, 2005. "Solving a multiresponse simulation-optimization problem with discrete variables using a multiple-attribute decision-making method," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 68(1), pages 9-21.
    12. Alfieri, Arianna & Matta, Andrea, 2012. "Mathematical programming formulations for approximate simulation of multistage production systems," European Journal of Operational Research, Elsevier, vol. 219(3), pages 773-783.
    13. W. Liu & Y. H. Dai, 2001. "Minimization Algorithms Based on Supervisor and Searcher Cooperation," Journal of Optimization Theory and Applications, Springer, vol. 111(2), pages 359-379, November.
    14. Sun, Haoze & Weng, Chengguo & Zhang, Yi, 2017. "Optimal multivariate quota-share reinsurance: A nonparametric mean-CVaR framework," Insurance: Mathematics and Economics, Elsevier, vol. 72(C), pages 197-214.
    15. Arianna Alfieri & Andrea Matta & Giulia Pedrielli, 2015. "Mathematical programming models for joint simulation–optimization applied to closed queueing networks," Annals of Operations Research, Springer, vol. 231(1), pages 105-127, August.
    16. Özdemir, Deniz & Yücesan, Enver & Herer, Yale T., 2013. "Multi-location transshipment problem with capacitated production," European Journal of Operational Research, Elsevier, vol. 226(3), pages 425-435.
    17. Jörg Fliege & Huifu Xu, 2011. "Stochastic Multiobjective Optimization: Sample Average Approximation and Applications," Journal of Optimization Theory and Applications, Springer, vol. 151(1), pages 135-162, October.
    18. Qi Fan & Jiaqiao Hu, 2018. "Surrogate-Based Promising Area Search for Lipschitz Continuous Simulation Optimization," INFORMS Journal on Computing, INFORMS, vol. 30(4), pages 677-693, November.
    19. 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.
    20. Flam, Sjur Didrik & Mirman, Leonard J., 1998. "Groping for optimal growth," Journal of Economic Dynamics and Control, Elsevier, vol. 23(2), pages 191-207, September.

    More about this item

    Statistics

    Access and download statistics

    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:bpj:mcmeap:v:4:y:1998:i:3:p:181-230:n:2. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.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.