A dynamic time warped clustering technique for discrete event simulation-based system analysis

Johnstone, Michael, Le, Vu Thanh, Zhang, James, Gunn, Bruce, Nahavandi, Saeid and Creighton, Douglas 2015, A dynamic time warped clustering technique for discrete event simulation-based system analysis, Expert systems with applications, vol. 42, no. 21, pp. 8078-8085, doi: 10.1016/j.eswa.2015.06.040.

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Title A dynamic time warped clustering technique for discrete event simulation-based system analysis
Author(s) Johnstone, MichaelORCID iD for Johnstone, Michael orcid.org/0000-0002-3005-8911
Le, Vu ThanhORCID iD for Le, Vu Thanh orcid.org/0000-0003-0648-6112
Zhang, JamesORCID iD for Zhang, James orcid.org/0000-0002-8367-9893
Gunn, Bruce
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Creighton, DouglasORCID iD for Creighton, Douglas orcid.org/0000-0002-9217-1231
Journal name Expert systems with applications
Volume number 42
Issue number 21
Start page 8078
End page 8085
Total pages 8
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-07-14
ISSN 0957-4174
Keyword(s) Clustering
Discrete event simulation
Dynamic time warping
K-medoids
Simulation trajectory analysis
Summary Abstract This paper introduces a novel approach for discrete event simulation output analysis. The approach combines dynamic time warping and clustering to enable the identification of system behaviours contributing to overall system performance, by linking the clustering cases to specific causal events within the system. Simulation model event logs have been analysed to group entity flows based on the path taken and travel time through the system. The proposed approach is investigated for a discrete event simulation of an international airport baggage handling system. Results show that the method is able to automatically identify key factors that influence the overall dwell time of system entities, such as bags that fail primary screening. The novel analysis methodology provides insight into system performance, beyond that achievable through traditional analysis techniques. This technique also has potential application to agent-based modelling paradigms and also business event logs traditionally studied using process mining techniques.
Language eng
DOI 10.1016/j.eswa.2015.06.040
Field of Research 080105 Expert Systems
091006 Manufacturing Processes and Technologies (excl Textiles)
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30074927

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