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


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.

Publication DOI: https://doi.org/10.1080/01605682.2019.1678406
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis Group in Journal of the Operational Research Society on 20 Nov 2019, available online at: http://www.tandfonline.com/10.1080/01605682.2019.1678406
Uncontrolled Keywords: Discrete-event simulation,OR,analytics,big data,literature review,Management Information Systems,Strategy and Management,Management Science and Operations Research,Marketing
Publication ISSN: 1476-9360
Last Modified: 13 May 2024 16:01
Date Deposited: 20 Nov 2019 13:57
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Related URLs: https://www.tan ... 82.2019.1678406 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2021
Published Online Date: 2019-11-20
Accepted Date: 2019-09-25
Authors: Greasley, Andrew (ORCID Profile 0000-0001-6413-3978)
Edwards, John (ORCID Profile 0000-0003-3979-017X)



Version: Accepted Version

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