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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Main Author: Young, G. Glen
Language:English
Published: University of British Columbia 2011
Subjects:
Online Access:http://hdl.handle.net/2429/34775
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spelling 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
collection NDLTD
language English
sources NDLTD
topic Replacement of industrial equipment -- Mathematical models.
Markov processes.
spellingShingle 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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