IDEAS home Printed from https://ideas.repec.org/a/taf/tjorxx/v72y2021i2p247-267.html
   My bibliography  Save this article

Enhancing discrete-event simulation with big data analytics: A review

Author

Listed:
  • Andrew Greasley
  • John Steven Edwards

Abstract

This article presents a literature review of the use of the OR technique of discrete-event simulation (DES) in conjunction with the big data analytics (BDA) approaches of data mining, machine learning, data farming, visual analytics, and process mining. The two areas are quite distinct. DES represents a mature OR tool using a graphical interface to produce an industry strength process modelling capability. The review reflects this and covers commercial off-the-shelf DES software used in an organisational setting. On the contrary the analytics techniques considered are in the domain of the data scientist and usually involve coding of algorithms to provide outputs derived from big data. Despite this divergence the review identifies a small but emerging literature of use-cases and from this a framework is derived for a DES development methodology that incorporates the use of these analytics techniques. The review finds scope for two new categories of simulation and analytics use: an enhanced capability for DES from the use of BDA at the main stages of the DES methodology as well as the use of DES in a data farming role to drive BDA techniques.

Suggested Citation

  • Andrew Greasley & John Steven Edwards, 2021. "Enhancing discrete-event simulation with big data analytics: A review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 72(2), pages 247-267, February.
    • Handle: RePEc:taf:tjorxx:v:72:y:2021:i:2:p:247-267
    DOI: 10.1080/01605682.2019.1678406
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01605682.2019.1678406
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01605682.2019.1678406?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Athar Ajaz Khan & János Abonyi, 2022. "Simulation of Sustainable Manufacturing Solutions: Tools for Enabling Circular Economy," Sustainability, MDPI, vol. 14(15), pages 1-40, August.
    2. Badiee, Aghdas & Moshtari, Mohammad & Berenguer, Gemma, 2024. "A systematic review of operations research and management science modeling techniques in the study of higher education institutions," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).

    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:taf:tjorxx:v:72:y:2021:i:2:p:247-267. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/tjor .

    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.