Repair strategies in an uncertain environment
Repair strategies in an uncertain environment
This research deals with repair strategies which maximise the time until a catastrophic event - There is a vital need for the equipment, and it is unable to respond. We examine the case where the need for the equipment varies over time according to a Markov chain. This means that the environment can be in different states, each with their own probability of the initiating event occurring. We describe the form of the optimal policy under this uncertain environment by Markov Decision Process.
We also look at conflict situations where the environment is controlled by an opponent. In this case the opponent’s actions force the need for the equipment, and this situation is modelled as a stochastic game. For this research, we develop stochastic game models with global and local constraints on effort. In the model with global constraints on effort, we introduce the idea of a constraint on the average effort undertaken by the opponent over the total history of the game so far. We naively describe this as a sleep index in that the opponent needs to sleep for a certain percentage of the time. We also expand these results to the situation where the advantage of a rest or quiescent period is discounted the further in the past it is, but always has a positive effect. In the model with local constraints on effort we look at games where the benefit to the opponent of being ‘able to sleep’ only lasts for a finite period and is then lost completely.
As extension of the first research, we also consider training. Because training is very important to increase the operator’s responding ability against an initiating event, this new model is more realistic. However, a problem with training is that it increases wear and tear of stand-by units. We develop discrete time Markov decision process formulations for this problem in order to investigate the form of the optimal action policy.
University of Southampton
Kim, Yeek-Hyun
3d447e79-426b-4e34-955d-80fd5838b20a
2004
Kim, Yeek-Hyun
3d447e79-426b-4e34-955d-80fd5838b20a
Kim, Yeek-Hyun
(2004)
Repair strategies in an uncertain environment.
University of Southampton, Doctoral Thesis.
Record type:
Thesis
(Doctoral)
Abstract
This research deals with repair strategies which maximise the time until a catastrophic event - There is a vital need for the equipment, and it is unable to respond. We examine the case where the need for the equipment varies over time according to a Markov chain. This means that the environment can be in different states, each with their own probability of the initiating event occurring. We describe the form of the optimal policy under this uncertain environment by Markov Decision Process.
We also look at conflict situations where the environment is controlled by an opponent. In this case the opponent’s actions force the need for the equipment, and this situation is modelled as a stochastic game. For this research, we develop stochastic game models with global and local constraints on effort. In the model with global constraints on effort, we introduce the idea of a constraint on the average effort undertaken by the opponent over the total history of the game so far. We naively describe this as a sleep index in that the opponent needs to sleep for a certain percentage of the time. We also expand these results to the situation where the advantage of a rest or quiescent period is discounted the further in the past it is, but always has a positive effect. In the model with local constraints on effort we look at games where the benefit to the opponent of being ‘able to sleep’ only lasts for a finite period and is then lost completely.
As extension of the first research, we also consider training. Because training is very important to increase the operator’s responding ability against an initiating event, this new model is more realistic. However, a problem with training is that it increases wear and tear of stand-by units. We develop discrete time Markov decision process formulations for this problem in order to investigate the form of the optimal action policy.
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Published date: 2004
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Local EPrints ID: 465273
URI: http://eprints.soton.ac.uk/id/eprint/465273
PURE UUID: 83d408a5-519b-4e4d-bf5b-d3d60da40e96
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Date deposited: 05 Jul 2022 00:34
Last modified: 16 Mar 2024 20:04
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Author:
Yeek-Hyun Kim
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