Modelling breakdown durations in simulation models of engine assembly lines
Modelling breakdown durations in simulation models of engine assembly lines
Machine failure is often an important source of variability and so it is essential
to model breakdowns in manufacturing simulation models accurately. This thesis
describes the modelling of machine breakdown durations in simulation models of
engine assembly lines. To simplify the inputs to the simulation models for complex
machining and assembly lines, the Arrows classification method has been derived
to group machines with similar distributions of breakdown durations, where
the Two-Sample Cram´er-von Mises statistic and bootstrap resampling are used to
measure the similarity of two sets of data. We use finite mixture distributions fitted
to the breakdown durations data of groups of machines as the input models for
the simulation models. We evaluate the complete modelling methodology that involves
the use of the Arrows classification method and finite mixture distributions,
by analysing the outputs of the simulation models using different input distributions
for describing the machine breakdown durations. Details of the methods and
results of the grouping processes will be presented, and will be demonstrated using
examples.
Lu, Lanting
995a0288-56c7-4d1e-840b-ef46e2084bb7
May 2009
Lu, Lanting
995a0288-56c7-4d1e-840b-ef46e2084bb7
Currie, Christine
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Lu, Lanting
(2009)
Modelling breakdown durations in simulation models of engine assembly lines.
University of Southampton, School of Mathematics, Doctoral Thesis, 231pp.
Record type:
Thesis
(Doctoral)
Abstract
Machine failure is often an important source of variability and so it is essential
to model breakdowns in manufacturing simulation models accurately. This thesis
describes the modelling of machine breakdown durations in simulation models of
engine assembly lines. To simplify the inputs to the simulation models for complex
machining and assembly lines, the Arrows classification method has been derived
to group machines with similar distributions of breakdown durations, where
the Two-Sample Cram´er-von Mises statistic and bootstrap resampling are used to
measure the similarity of two sets of data. We use finite mixture distributions fitted
to the breakdown durations data of groups of machines as the input models for
the simulation models. We evaluate the complete modelling methodology that involves
the use of the Arrows classification method and finite mixture distributions,
by analysing the outputs of the simulation models using different input distributions
for describing the machine breakdown durations. Details of the methods and
results of the grouping processes will be presented, and will be demonstrated using
examples.
Text
Thesis_Lanting_Lu.pdf
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More information
Published date: May 2009
Organisations:
University of Southampton, Operational Research
Identifiers
Local EPrints ID: 66333
URI: http://eprints.soton.ac.uk/id/eprint/66333
PURE UUID: 2ad1b640-8a39-4656-87ac-284f48d7999c
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Date deposited: 04 Jun 2009
Last modified: 14 Mar 2024 02:47
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Author:
Lanting Lu
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