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A dynamic time warped clustering technique for discrete event simulation-based system analysis
journal contribution
posted on 2015-07-14, 00:00 authored by Michael JohnstoneMichael Johnstone, Vu LeVu Le, James ZhangJames Zhang, Bruce GunnBruce Gunn, Saeid Nahavandi, Douglas CreightonDouglas CreightonAbstract 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.
History
Journal
Expert systems with applicationsVolume
42Issue
21Pagination
8078 - 8085Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
0957-4174Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2015, ElsevierUsage metrics
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