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Classification analysis for simulation of the duration of machine breakdowns

Author

Listed:
  • L Lu

    (School of Mathematics, University of Southampton)

  • C S M Currie

    (School of Mathematics, University of Southampton)

  • R C H Cheng

    (School of Mathematics, University of Southampton)

  • J Ladbrook

    (Dunton Technical Centre, Ford Motor Company)

Abstract

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 Cramér-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.

Suggested Citation

  • L Lu & C S M Currie & R C H Cheng & J Ladbrook, 2011. "Classification analysis for simulation of the duration of machine breakdowns," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(4), pages 760-767, April.
    • Handle: RePEc:pal:jorsoc:v:62:y:2011:i:4:d:10.1057_jors.2010.33
    DOI: 10.1057/jors.2010.33
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    References listed on IDEAS

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    1. Lanting Lu & Christine S. M. Currie, 2010. "Evaluation Of The Arrows Method For Classification Of Data," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 27(01), pages 121-142.
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