IDEAS home Printed from https://ideas.repec.org/a/pal/jorsoc/v56y2005i8d10.1057_palgrave.jors.2601915.html
   My bibliography  Save this article

Knowledge-based improvement: simulation and artificial intelligence for identifying and improving human decision-making in an operations system

Author

Listed:
  • S Robinson

    (University of Warwick)

  • T Alifantis

    (University of Warwick)

  • J S Edwards

    (Aston University)

  • J Ladbrook

    (Ford Motor Company, Dunton Engineering Centre (15/4A-F04-D), Laindon, Basildon)

  • A Waller

    (Lanner Group, The Oaks)

Abstract

The performance of most operations systems is significantly affected by the interaction of human decision-makers. A methodology, based on the use of visual interactive simulation (VIS) and artificial intelligence (AI), is described that aims to identify and improve human decision-making in operations systems. The methodology, known as ‘knowledge-based improvement’ (KBI), elicits knowledge from a decision-maker via a VIS and then uses AI methods to represent decision-making. By linking the VIS and AI representation, it is possible to predict the performance of the operations system under different decision-making strategies and to search for improved strategies. The KBI methodology is applied to the decision-making surrounding unplanned maintenance operations at a Ford Motor Company engine assembly plant.

Suggested Citation

  • S Robinson & T Alifantis & J S Edwards & J Ladbrook & A Waller, 2005. "Knowledge-based improvement: simulation and artificial intelligence for identifying and improving human decision-making in an operations system," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(8), pages 912-921, August.
    • Handle: RePEc:pal:jorsoc:v:56:y:2005:i:8:d:10.1057_palgrave.jors.2601915
    DOI: 10.1057/palgrave.jors.2601915
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/palgrave.jors.2601915
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/palgrave.jors.2601915?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. George S. Fishman, 1978. "Grouping Observations in Digital Simulation," Management Science, INFORMS, vol. 24(5), pages 510-521, January.
    2. J Moffat, 2000. "Representing the command and control process in simulation models of conflict," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(4), pages 431-439, April.
    3. R D Hurrion, 2000. "A sequential method for the development of visual interactive meta-simulation models using neural networks," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(6), pages 712-719, June.
    4. Hurrion, R. D., 1991. "Intelligent Visual Interactive Modelling," European Journal of Operational Research, Elsevier, vol. 54(3), pages 349-356, 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. Loureiro, Sandra Maria Correia & Guerreiro, João & Tussyadiah, Iis, 2021. "Artificial intelligence in business: State of the art and future research agenda," Journal of Business Research, Elsevier, vol. 129(C), pages 911-926.
    2. Stewart Robinson & Stavrianna Dimitriou & Kathy Kotiadis, 2017. "Addressing the sample size problem in behavioural operational research: simulating the newsvendor problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(3), pages 253-268, March.

    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. S Robinson, 2005. "Discrete-event simulation: from the pioneers to the present, what next?," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(6), pages 619-629, June.
    2. Robinson, Stewart, 2002. "General concepts of quality for discrete-event simulation," European Journal of Operational Research, Elsevier, vol. 138(1), pages 103-117, April.
    3. Lada, Emily K. & Wilson, James R., 2006. "A wavelet-based spectral procedure for steady-state simulation analysis," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1769-1801, November.
    4. L Dodd & J Moffat & J Smith, 2006. "Discontinuity in decision-making when objectives conflict: a military command decision case study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(6), pages 643-654, June.
    5. Mingchang Chih, 2019. "An Insight into the Data Structure of the Dynamic Batch Means Algorithm with Binary Tree Code," Mathematics, MDPI, vol. 7(9), pages 1-8, August.
    6. Logendran, Rasaratnam & Talkington, Diane, 1997. "Analysis of cellular and functional manufacturing systems in the presence of machine breakdown," International Journal of Production Economics, Elsevier, vol. 53(3), pages 239-256, December.
    7. Srinagesh Gavirneni & Douglas J. Morrice & Peter Mullarkey, 2004. "Simulation Helps Maxager Shorten Its Sales Cycle," Interfaces, INFORMS, vol. 34(2), pages 87-96, April.
    8. Song, Wheyming Tina & Chih, Mingchang, 2013. "Run length not required: Optimal-mse dynamic batch means estimators for steady-state simulations," European Journal of Operational Research, Elsevier, vol. 229(1), pages 114-123.
    9. Moffat, James & Medhurst, John, 2009. "Modelling of human decision-making in simulation models of conflict using experimental gaming," European Journal of Operational Research, Elsevier, vol. 196(3), pages 1147-1157, August.
    10. Harper, Alison & Mustafee, Navonil & Yearworth, Mike, 2021. "Facets of trust in simulation studies," European Journal of Operational Research, Elsevier, vol. 289(1), pages 197-213.
    11. Natalie M. Steiger & James R. Wilson, 2001. "Convergence Properties of the Batch Means Method for Simulation Output Analysis," INFORMS Journal on Computing, INFORMS, vol. 13(4), pages 277-293, November.
    12. Kalloniatis, Alexander C. & McLennan-Smith, Timothy A. & Roberts, Dale O., 2020. "Modelling distributed decision-making in Command and Control using stochastic network synchronisation," European Journal of Operational Research, Elsevier, vol. 284(2), pages 588-603.
    13. Song, Wheyming T. & Chih, Mingchang, 2010. "Extended dynamic partial-overlapping batch means estimators for steady-state simulations," European Journal of Operational Research, Elsevier, vol. 203(3), pages 640-651, June.
    14. Draman, Murat & Kuban Altinel, I & Bajgoric, Nijaz & Tamer Unal, Ali & Birgoren, Burak, 2002. "A clone-based graphical modeler and mathematical model generator for optimal production planning in process industries," European Journal of Operational Research, Elsevier, vol. 137(3), pages 483-496, March.
    15. Robinson, Stewart, 2007. "A statistical process control approach to selecting a warm-up period for a discrete-event simulation," European Journal of Operational Research, Elsevier, vol. 176(1), pages 332-346, January.
    16. Natalie M. Steiger & James R. Wilson, 2002. "An Improved Batch Means Procedure for Simulation Output Analysis," Management Science, INFORMS, vol. 48(12), pages 1569-1586, December.
    17. Au, Grace & Paul, Ray J., 1996. "Visual interactive modelling: A pictorial simulation specification system," European Journal of Operational Research, Elsevier, vol. 91(1), pages 14-26, May.
    18. K Hoad & S Robinson & R Davies, 2010. "Automated selection of the number of replications for a discrete-event simulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(11), pages 1632-1644, November.
    19. J Moffat & I Campbell & P Glover, 2004. "Validation of the mission-based approach to representing command and control in simulation models of conflict," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(4), pages 340-349, April.
    20. John R. Birge, 2023. "Uses of Sub-sample Estimates to Reduce Errors in Stochastic Optimization Models," Papers 2310.07052, arXiv.org.

    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:56:y:2005:i:8:d:10.1057_palgrave.jors.2601915. 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.