IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v124y2000i1p55-62.html
   My bibliography  Save this article

A methodological framework for the validation of predictive simulations

Author

Listed:
  • Fraedrich, D.
  • Goldberg, A.

Abstract

No abstract is available for this item.

Suggested Citation

  • Fraedrich, D. & Goldberg, A., 2000. "A methodological framework for the validation of predictive simulations," European Journal of Operational Research, Elsevier, vol. 124(1), pages 55-62, July.
  • Handle: RePEc:eee:ejores:v:124:y:2000:i:1:p:55-62
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(99)00117-4
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Kleijnen, Jack P. C., 1995. "Verification and validation of simulation models," European Journal of Operational Research, Elsevier, vol. 82(1), pages 145-162, April.
    2. Dery, Richard & Landry, Maurice & Banville, Claude, 1993. "Revisiting the issue of model validation in OR: An epistemological view," European Journal of Operational Research, Elsevier, vol. 66(2), pages 168-183, April.
    3. Thomas H. Naylor & J. M. Finger, 1967. "Verification of Computer Simulation Models," Management Science, INFORMS, vol. 14(2), pages 92-101, October.
    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. H. Christopher Frey & Sumeet R. Patil, 2002. "Identification and Review of Sensitivity Analysis Methods," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 553-578, June.

    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. Janová, Jitka & Hampel, David & Nerudová, Danuše, 2019. "Design and validation of a tax sustainability index," European Journal of Operational Research, Elsevier, vol. 278(3), pages 916-926.
    2. Rohit Kapoor & Bhavin J. Shah, 2016. "Simulation model for closed loop repairable parts inventory system with service level performance measures," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 23(1), pages 18-42.
    3. Edward Radosiński & Łukasz Radosiński, 2018. "Verification of a model as a scientific tool of operations research – a methodological approach," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 28(3), pages 45-62.
    4. Arnold Reisman & Muhittin Oral, 2005. "Soft Systems Methodology: A Context Within a 50-Year Retrospective of OR/MS," Interfaces, INFORMS, vol. 35(2), pages 164-178, April.
    5. Christopher J Lynch & Saikou Y Diallo & Hamdi Kavak & Jose J Padilla, 2020. "A content analysis-based approach to explore simulation verification and identify its current challenges," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-33, May.
    6. Kleijnen, J.P.C., 1995. "Statistical validation of simulation models : A case study," Other publications TiSEM 4da192cf-c3f4-40a4-aea4-5, Tilburg University, School of Economics and Management.
    7. Juan Manuel Larrosa, 2016. "Agentes computacionales y análisis económico," Revista de Economía Institucional, Universidad Externado de Colombia - Facultad de Economía, vol. 18(34), pages 87-113, January-J.
    8. Merrick, James H. & Weyant, John P., 2019. "On choosing the resolution of normative models," European Journal of Operational Research, Elsevier, vol. 279(2), pages 511-523.
    9. Pau Fonseca i Casas, 2023. "A Continuous Process for Validation, Verification, and Accreditation of Simulation Models," Mathematics, MDPI, vol. 11(4), pages 1-25, February.
    10. Günter Küppers & Johannes Lenhard, 2005. "Validation of Simulation: Patterns in the Social and Natural Sciences," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-3.
    11. Maria Franca Norese & Diana Rolando & Rocco Curto, 2023. "DIKEDOC: a multicriteria methodology to organise and communicate knowledge," Annals of Operations Research, Springer, vol. 325(2), pages 1049-1082, June.
    12. Spinelli, Raffaele & Magagnotti, Natascia, 2010. "A tool for productivity and cost forecasting of decentralised wood chipping," Forest Policy and Economics, Elsevier, vol. 12(3), pages 194-198, March.
    13. H. Christopher Frey & Sumeet R. Patil, 2002. "Identification and Review of Sensitivity Analysis Methods," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 553-578, June.
    14. Edris Yousefi Rad & Mohammad Reza Mahpeykar, 2017. "A Novel Hybrid Approach for Numerical Modeling of the Nucleating Flow in Laval Nozzle and Transonic Steam Turbine Blades," Energies, MDPI, vol. 10(9), pages 1-37, August.
    15. Tunali, S. & Batmaz, I., 2003. "A metamodeling methodology involving both qualitative and quantitative input factors," European Journal of Operational Research, Elsevier, vol. 150(2), pages 437-450, October.
    16. Patrick Afflerbach & Christopher Dun & Henner Gimpel & Dominik Parak & Johannes Seyfried, 2021. "A Simulation-Based Approach to Understanding the Wisdom of Crowds Phenomenon in Aggregating Expert Judgment," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(4), pages 329-348, August.
    17. Jacques Fontanel, 1982. "Introduction. Military expenditures and Economic Growth (France, Morocco)," Post-Print hal-03264692, HAL.
    18. Diaz, Rafael & Behr, Joshua G. & Acero, Beatriz, 2022. "Coastal housing recovery in a postdisaster environment: A supply chain perspective," International Journal of Production Economics, Elsevier, vol. 247(C).
    19. Kleijnen, J.P.C., 1995. "Sensitivity analysis and optimization of system dynamics models : Regression analysis and statistical design of experiments," Other publications TiSEM 87ee6ee0-592c-4204-ac50-6, Tilburg University, School of Economics and Management.
    20. Sander van der Hoog, 2017. "Deep Learning in (and of) Agent-Based Models: A Prospectus," Papers 1706.06302, arXiv.org.

    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:124:y:2000:i:1:p:55-62. 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.