IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v164y2005i2p287-300.html
   My bibliography  Save this article

An overview of the design and analysis of simulation experiments for sensitivity analysis

Author

Listed:
  • Kleijnen, Jack P. C.

Abstract

Sensitivity analysis may serve validation, optimization, and risk analysis of simulation models.This review surveys classic and modern designs for experiments with simulation models.Classic designs were developed for real, non-simulated systems in agriculture, engineering, etc.These designs assume a few factors (no more than ten factors) with only a few values per factor (no more than five values).These designs are mostly incomplete factorials (e.g., fractionals).The resulting input/output (I/O) data are analyzed through polynomial metamodels, which are a type of linear regression models.Modern designs were developed for simulated systems in engineering, management science, etc.These designs allow many factors (more than 100), each with either a few or many (more than 100) values.These designs include group screening, Latin Hypercube Sampling (LHS), and other space filling designs.Their I/O data are analyzed through second-order polynomials for group screening, and through Kriging models for LHS.
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
<
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Kleijnen, Jack P. C., 2005. "An overview of the design and analysis of simulation experiments for sensitivity analysis," European Journal of Operational Research, Elsevier, vol. 164(2), pages 287-300, July.
    • Handle: RePEc:eee:ejores:v:164:y:2005:i:2:p:287-300
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(04)00083-9
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

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

    Other versions of this item:

    References listed on IDEAS

    as
    1. Russell C. H. Cheng & Jack P. C. Kleijnen, 1999. "Improved Design of Queueing Simulation Experiments with Highly Heteroscedastic Responses," Operations Research, INFORMS, vol. 47(5), pages 762-777, October.
    2. Antoniadis, Anestis & Dinh Tuan Pham, 1998. "Wavelet regression for random or irregular design," Computational Statistics & Data Analysis, Elsevier, vol. 28(4), pages 353-369, October.
    3. J P C Kleijnen & M T Smits, 2003. "Performance metrics in supply chain management," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(5), pages 507-514, May.
    4. Shu Yamada & Dennis Lin, 2002. "Construction of mixed-level supersaturated design," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 56(3), pages 205-214, December.
    5. Kleijnen, J.P.C., 2004. "Design and Analysis of Monte Carlo Experiments," Discussion Paper 2004-17, Tilburg University, Center for Economic Research.
    6. Kleijnen, J.P.C. & Smits, M.T., 2003. "Performance metrics in supply chain management," Other publications TiSEM 80777aed-0c9f-4ded-b0bb-f, Tilburg University, School of Economics and Management.
    7. Kleijnen, Jack P. C. & Sargent, Robert G., 2000. "A methodology for fitting and validating metamodels in simulation," European Journal of Operational Research, Elsevier, vol. 120(1), pages 14-29, January.
    8. Angun, M.E. & Gürkan, G. & den Hertog, D. & Kleijnen, J.P.C., 2002. "Response surface methodology revisited," Other publications TiSEM 32c35a04-3de9-4dee-a242-6, Tilburg University, School of Economics and Management.
    9. Joan M. Donohue & Ernest C. Houck & Raymond H. Myers, 1993. "Simulation Designs and Correlation Induction for Reducing Second-Order Bias in First-Order Response Surfaces," Operations Research, INFORMS, vol. 41(5), pages 880-902, October.
    10. Jack P. C. Kleijnen, 1992. "Regression Metamodels for Simulation with Common Random Numbers: Comparison of Validation Tests and Confidence Intervals," Management Science, INFORMS, vol. 38(8), pages 1164-1185, August.
    11. van Groenendaal, W.J.H., 1998. "The Economic Appraisal of Natural Gas Projects," Other publications TiSEM a0ff517c-2041-4457-adac-7, Tilburg University, School of Economics and Management.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Kleijnen, J.P.C. & Sanchez, S.M. & Lucas, T.W. & Cioppa, T.M., 2003. "A User's Guide to the Brave New World of Designing Simulation Experiments," Discussion Paper 2003-1, Tilburg University, Center for Economic Research.
    3. Kleijnen, J.P.C., 2004. "Design and Analysis of Monte Carlo Experiments," Discussion Paper 2004-17, Tilburg University, Center for Economic Research.
    4. H A Akkermans & K E van Oorschot, 2005. "Relevance assumed: a case study of balanced scorecard development using system dynamics," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(8), pages 931-941, August.
    5. Robert Engel & Worarat Krathu & Marco Zapletal & Christian Pichler & R. P. Jagadeesh Chandra Bose & Wil Aalst & Hannes Werthner & Christian Huemer, 2016. "Analyzing inter-organizational business processes," Information Systems and e-Business Management, Springer, vol. 14(3), pages 577-612, August.
    6. Hatem Elleuch & Wafik Hachicha & Habib Chabchoub, 2014. "A combined approach for supply chain risk management: description and application to a real hospital pharmaceutical case study," Journal of Risk Research, Taylor & Francis Journals, vol. 17(5), pages 641-663, May.
    7. Zhang, Xiunian & Lam, Jasmine Siu Lee, 2018. "Shipping mode choice in cold chain from a value-based management perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 110(C), pages 147-167.
    8. Alexandru Constăngioară, 2013. "Performance Metrics in Supply Chain Management. Evidence from Romanian Economy," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 15(33), pages 170-179, February.
    9. 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.
    10. Shi, Wen & Kleijnen, Jack P.C. & Liu, Zhixue, 2014. "Factor screening for simulation with multiple responses: Sequential bifurcation," European Journal of Operational Research, Elsevier, vol. 237(1), pages 136-147.
    11. Gyula Laszlo FLORIAN & Alexandru CONSTANGIOARA, 2013. "The Impact Of Performances In Romanian Supply Chains On Organizational Performances," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 7(1), pages 318-324, November.
    12. A A Syntetos & N C Georgantzas & J E Boylan & B C Dangerfield, 2011. "Judgement and supply chain dynamics," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1138-1158, June.
    13. Nihan Kabadayi & Mohammad Dehghanimohammadabadi, 2022. "Multi-objective supplier selection process: a simulation–optimization framework integrated with MCDM," Annals of Operations Research, Springer, vol. 319(2), pages 1607-1629, December.
    14. Kleijnen, J.P.C., 2003. "Supply Chain Simulation : A Survey," Discussion Paper 2003-103, Tilburg University, Center for Economic Research.
    15. Sergei P. Kazakov, 2016. "Market Orientation and Company Performance: A Russian Service Industry Perspective," HSE Working papers WP BRP 51/MAN/2016, National Research University Higher School of Economics.
    16. Maestrini, Vieri & Luzzini, Davide & Maccarrone, Paolo & Caniato, Federico, 2017. "Supply chain performance measurement systems: A systematic review and research agenda," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 299-315.
    17. Reis dos Santos, Pedro M. & Isabel Reis dos Santos, M., 2009. "Using subsystem linear regression metamodels in stochastic simulation," European Journal of Operational Research, Elsevier, vol. 196(3), pages 1031-1040, August.
    18. Kleijnen, Jack P.C. & Mehdad, E., 2014. "Multivariate Versus Univariate Kriging Metamodels for Multi-Response Simulation Models (Revision of 2012-039)," Other publications TiSEM 8a096696-f700-4cbe-9474-c, Tilburg University, School of Economics and Management.
    19. Kull, Thomas & Closs, David, 2008. "The risk of second-tier supplier failures in serial supply chains: Implications for order policies and distributor autonomy," European Journal of Operational Research, Elsevier, vol. 186(3), pages 1158-1174, May.
    20. Ganga, Gilberto Miller Devós & Carpinetti, Luiz Cesar Ribeiro, 2011. "A fuzzy logic approach to supply chain performance management," International Journal of Production Economics, Elsevier, vol. 134(1), pages 177-187, November.

    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:eee:ejores:v:164:y:2005:i:2:p:287-300. 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.