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dc.contributor.authorSun, Y.
dc.contributor.authorAw, G.
dc.contributor.authorLoxton, Ryan
dc.contributor.authorTeo, Kok Lay
dc.date.accessioned2017-01-30T10:39:28Z
dc.date.available2017-01-30T10:39:28Z
dc.date.created2014-08-04T20:00:23Z
dc.date.issued2014
dc.identifier.citationSun, Y. and Aw, G. and Loxton, R. and Teo, K.L. 2014. An optimal machine maintenance problem with probabilistic state constraints. Information Sciences. 281: pp. 386-398.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/4479
dc.identifier.doi10.1016/j.ins.2014.05.051
dc.description.abstract

We consider a machine that is maintained via two types of maintenance action: (i) continuous (minor) maintenance that curbs natural degradation of the machine; and (ii) overhaul (major) maintenance that takes place at certain discrete time points and significantly improves the condition of the machine. We introduce an impulsive stochastic differential equation to model the condition of the machine over the time horizon. The problem we investigate is to choose the continuous maintenance rate and the overhaul maintenance times to minimize the total cost of operating and maintaining the machine, where probabilistic state constraints are imposed to ensure that the machine’s state and output meet minimum acceptable levels with high probability. This impulsive stochastic optimal control problem is first transformed into a deterministic optimal control problem with state jumps and continuous inequality constraints. We then show that this equivalent problem can be solved using a combination of the control parameterization technique, the time-scaling transformation, and the constraint transcription method. Finally, we illustrate our approach by solving a numerical example.

dc.publisherElsevier Inc
dc.subjectImpulsive system
dc.subjectNonlinear optimization
dc.subjectMaintenance scheduling
dc.subjectOptimal control
dc.titleAn optimal machine maintenance problem with probabilistic state constraints
dc.typeJournal Article
dcterms.source.volume281
dcterms.source.startPage386
dcterms.source.endPage398
dcterms.source.issn00200255
dcterms.source.titleInformation Sciences
curtin.note

This is the author’s version of a work that was accepted for publication in the Journal, Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences, Volume 281, 10 October 2014, Pages 386–398. http://doi.org/10.1016/j.ins.2014.05.051

curtin.departmentDepartment of Mathematics and Statistics
curtin.accessStatusOpen access


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