New stochastic models for preventive maintenance and maintenance optimizationAuthor-Name: Lee, Hyunju
Ji Hwan Cha
European Journal of Operational Research, 2016, vol. 255, issue 1, 80-90
Abstract:
This paper considers periodic preventive maintenance policies for a deteriorating repairable system. On each failure the system is repaired and, at the planned times, it is periodically maintained to improve its reliability performance. Most of periodic preventive maintenance (PM) models for repairable systems have been studied assuming that the failure process between two PMs follows the nonhomogeneous Poisson process (NHPP), implying the minimal repair on each failure. However, in this paper, we assume that the failure process between two PMs follows a new counting process which is a generalized version of the NHPP. We develop two types of PM models and study detailed properties of the optimal policies which minimize the long-run expected cost rates. Numerical examples are also provided.
Keywords: Optimal periodic maintenance; Repair type; Generalized Polya process; Worse-than-minimal-repair; Stochastic intensity (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221716302399
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:255:y:2016:i:1:p:80-90
DOI: 10.1016/j.ejor.2016.04.020
Access Statistics for this article
European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati
More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().