An optimal machine maintenance problem with probabilistic state constraints

Sun, Y.; Aw, G.; Loxton, Ryan; Teo, Kok Lay

doi:10.1016/j.ins.2014.05.051

199976_199976.pdf (1.886Mb)

Access Status

Open access

Authors

Sun, Y.

Aw, G.

Loxton, Ryan

Teo, Kok Lay

Date

2014

Type

Journal Article

Metadata

Show full item record

Citation

Sun, 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.

Source Title

Information Sciences

DOI

10.1016/j.ins.2014.05.051

ISSN

00200255

School

Department of Mathematics and Statistics

Remarks

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

URI

http://hdl.handle.net/20.500.11937/4479

Collection

Curtin Research Publications

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.