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
Avramidis, Athanasios
d6c4b6b6-c0cf-4ed1-bbe1-a539937e4001
15 December 2016
Pan, Shu
3bd5f8c2-9b37-460f-9817-eafa9f2f021e
Avramidis, Athanasios
d6c4b6b6-c0cf-4ed1-bbe1-a539937e4001
Pan, Shu and Avramidis, Athanasios
(2016)
Modeling and analyzing the breakdown process.
2016 Winter Simulation Conference (WSC), , Washington D.C., United States.
11 - 14 Dec 2016.
.
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.
Text
wsc16poster
- Author's Original
More information
Accepted/In Press date: 1 July 2016
e-pub ahead of print date: 15 December 2016
Published date: 15 December 2016
Venue - Dates:
2016 Winter Simulation Conference (WSC), , Washington D.C., United States, 2016-12-11 - 2016-12-14
Organisations:
Mathematical Sciences, Operational Research
Identifiers
Local EPrints ID: 411137
URI: http://eprints.soton.ac.uk/id/eprint/411137
PURE UUID: ea519de6-f676-43a2-b6b2-798e96c2a5cb
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Date deposited: 14 Jun 2017 16:31
Last modified: 17 Mar 2024 03:12
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Author:
Shu Pan
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