Lu, Lanting, Currie, Christine S.M., Cheng, Russell C.H. and Ladbrook, John
Classification analysis for simulation of the duration
of machine breakdowns.
Journal of the Operational Research Society (doi:10.1057/jors.2010.33).
Full text not available from this repository.
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
Actions (login required)