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Modelling breakdown durations in simulation models of engine assembly lines

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
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.

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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
ORCID for Christine Currie: ORCID iD orcid.org/0000-0002-7016-3652

Catalogue record

Date deposited: 04 Jun 2009
Last modified: 11 Dec 2021 03:50

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Contributors

Author: Lanting Lu
Thesis advisor: Christine Currie ORCID iD

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