Modeling and analyzing the breakdown process
Modeling and analyzing the breakdown process
An Operation Dependent Environment Change (ODEC) model has been proposed to model the breakdown process of a real production line. The model allows for an individual machine's breakdown rates to alternate between low and high when the machine is operational. The ODEC model is natural and allows for the times between breakdowns to be positively auto-correlated, which is what exhibits in the data from an engine production line of a major UK automotive manufacturer. A Markov-modulated Poisson Process with two hidden Markov states (MMPP2) method has been proposed to estimate the transition rates. This enables us to solve the performance measure of the production line, i.e. the throughput, analytically using a Markovian model.
3662-3663
Pan, Shu
3bd5f8c2-9b37-460f-9817-eafa9f2f021e
19 January 2017
Pan, Shu
3bd5f8c2-9b37-460f-9817-eafa9f2f021e
Pan, Shu
(2017)
Modeling and analyzing the breakdown process.
Roeder, T.M.K., Frazier, P.I., Szechtman, R., Zhou, E., Huschka, T. and Chick, S.E.
(eds.)
In 2016 Winter Simulation Conference (WSC).
IEEE.
.
(doi:10.1109/WSC.2016.7822388).
Record type:
Conference or Workshop Item
(Paper)
Abstract
An Operation Dependent Environment Change (ODEC) model has been proposed to model the breakdown process of a real production line. The model allows for an individual machine's breakdown rates to alternate between low and high when the machine is operational. The ODEC model is natural and allows for the times between breakdowns to be positively auto-correlated, which is what exhibits in the data from an engine production line of a major UK automotive manufacturer. A Markov-modulated Poisson Process with two hidden Markov states (MMPP2) method has been proposed to estimate the transition rates. This enables us to solve the performance measure of the production line, i.e. the throughput, analytically using a Markovian model.
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Accepted/In Press date: 17 September 2016
e-pub ahead of print date: 11 December 2016
Published date: 19 January 2017
Venue - Dates:
2016 Winter Simulation Conference (WSC), , Washington D.C., United States, 2016-12-11 - 2016-12-14
Organisations:
Mathematical Sciences
Identifiers
Local EPrints ID: 408679
URI: http://eprints.soton.ac.uk/id/eprint/408679
ISSN: 0891-7736
PURE UUID: 7bad4f17-75f5-4379-a233-db9f316a30c3
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Date deposited: 26 May 2017 04:01
Last modified: 15 Mar 2024 14:08
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Contributors
Author:
Shu Pan
Editor:
T.M.K. Roeder
Editor:
P.I. Frazier
Editor:
R. Szechtman
Editor:
E. Zhou
Editor:
T. Huschka
Editor:
S.E. Chick
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