A dynamic programming - Markov chain algorithm for determining optimal component replacement policies
An algorithm is developed to determine the optimal component replacement rules to follow in managing a particular class of equipment. The work follows basically the models developed previously by S.E. Dreyfus and R.A. Howard. However, a different Markov state description has been used to extend the...
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ndltd-UBC-oai-circle.library.ubc.ca-2429-347752018-01-05T17:47:40Z A dynamic programming - Markov chain algorithm for determining optimal component replacement policies Young, G. Glen Replacement of industrial equipment -- Mathematical models. Markov processes. An algorithm is developed to determine the optimal component replacement rules to follow in managing a particular class of equipment. The work follows basically the models developed previously by S.E. Dreyfus and R.A. Howard. However, a different Markov state description has been used to extend the application of these models to systems of more than one component subject to stochastic failure and for which the failure of any component renders the entire system inoperative. The model, in effect, selects optimal replacement alternatives as individual components fail, under the uncertainty of further failures occurring in the same transition interval. The model was programmed for an I.B.M. 360/67 computer and the results for a hypothetical problem were checked through renewal theory. Forestry, Faculty of Graduate 2011-05-24T22:20:44Z 2011-05-24T22:20:44Z 1970 Text Thesis/Dissertation http://hdl.handle.net/2429/34775 eng For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use. University of British Columbia |
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language |
English |
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Replacement of industrial equipment -- Mathematical models. Markov processes. |
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Replacement of industrial equipment -- Mathematical models. Markov processes. Young, G. Glen A dynamic programming - Markov chain algorithm for determining optimal component replacement policies |
description |
An algorithm is developed to determine the optimal component replacement rules to follow in managing a particular
class of equipment. The work follows basically the models developed previously by S.E. Dreyfus and R.A. Howard. However, a different Markov state description has been used to extend the application of these models to systems of more than one component subject to stochastic failure and for which the failure of any component renders the entire system inoperative. The model, in effect, selects optimal replacement alternatives as individual components fail, under the uncertainty of further failures occurring in the same transition interval. The model was programmed for an I.B.M. 360/67 computer and the results for a hypothetical problem were checked through renewal theory. === Forestry, Faculty of === Graduate |
author |
Young, G. Glen |
author_facet |
Young, G. Glen |
author_sort |
Young, G. Glen |
title |
A dynamic programming - Markov chain algorithm for determining optimal component replacement policies |
title_short |
A dynamic programming - Markov chain algorithm for determining optimal component replacement policies |
title_full |
A dynamic programming - Markov chain algorithm for determining optimal component replacement policies |
title_fullStr |
A dynamic programming - Markov chain algorithm for determining optimal component replacement policies |
title_full_unstemmed |
A dynamic programming - Markov chain algorithm for determining optimal component replacement policies |
title_sort |
dynamic programming - markov chain algorithm for determining optimal component replacement policies |
publisher |
University of British Columbia |
publishDate |
2011 |
url |
http://hdl.handle.net/2429/34775 |
work_keys_str_mv |
AT younggglen adynamicprogrammingmarkovchainalgorithmfordeterminingoptimalcomponentreplacementpolicies AT younggglen dynamicprogrammingmarkovchainalgorithmfordeterminingoptimalcomponentreplacementpolicies |
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1718595299985850368 |