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Classification analysis for simulation of the duration of machine breakdowns

Record type: Article

Machine failure can have a significant impact on the throughput of manufacturing systems, therefore accurate
modelling of breakdowns in manufacturing simulation models is essential. Finite mixture distributions have
been successfully used by Ford Motor Company to model machine breakdown durations in simulation models
of engine assembly lines. These models can be very complex, with a large number of machines. To simplify
the modelling we propose a method of grouping machines with similar distributions of breakdown durations,
which we call the Arrows Classification Method, where the Two-Sample Cram´er-von-Mises statistic is used
to measure the similarity of two sets of the data. We evaluate the classification procedure by comparing the
throughput of a simulation model when run with mixture models fitted to individual machine breakdown
durations; mixture models fitted to group breakdown durations; and raw data. Details of the methods and
results of the classification will be presented, and demonstrated using an example

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Citation

Lu, Lanting, Currie, Christine S.M., Cheng, Russell C.H. and Ladbrook, John (2010) Classification analysis for simulation of the duration of machine breakdowns Journal of the Operational Research Society (doi:10.1057/jors.2010.33).

More information

Published date: 21 April 2010
Keywords: simulation, classification, manufacturing, bootstrapping
Organisations: Operational Research

Identifiers

Local EPrints ID: 149237
URI: http://eprints.soton.ac.uk/id/eprint/149237
ISSN: 0160-5682
PURE UUID: ae8b6bc4-14a2-4637-b3d1-8c162fa3bb03

Catalogue record

Date deposited: 30 Apr 2010 07:58
Last modified: 18 Jul 2017 19:30

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Contributors

Author: Lanting Lu
Author: John Ladbrook

University divisions


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