Opportunity-based age replacement with a one-cycle criterion

Coolen‐Schrijner, Pauline; Shaw, Simon; Coolen, Frank P. A.

doi:10.1057/jors.2008.99

Cited by 16 publications

(11 citation statements)

References 17 publications

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“…Coolen-Schrijner and Coolen [13] used a onecycle optimality criterion for standard age replacement, with a nonparametric statistical approach for the unit's random failure time, which also enabled the information from previous units to be taken into account for planning of preventive replacement of the next unit. Coolen-Schrijner et al [16] present opportunity-based age replacement with a one-cycle criterion, in line with the approach by Dekker and Dijkstra [15].…”

Section: Opportunity-based Age Replacementmentioning

confidence: 96%

Extended opportunity-based age replacement with a one-cycle criterion

Venkat

Coolen

Coolen‐Schrijner

2010

Proceedings of the Institution of Mechanical Engineers, Part O:

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Age replacement is a well-established theoretical model for guidance on preventive replacement of technical units, with the intention to replace units before failure, in a manner that is most cost-effective. Traditionally, the cost criterion is based on assumed use of the same replacement strategy over a period of infinite length, enabling application of the renewal reward theorem to derive the units' optimal preventive replacement age. However, in many situations the number of renewals involving exchangeable units is typically small, which undermines the use of this traditional cost criterion. As an alternative, minimal average costs per unit time over a single cycle has been proposed, and studied for several age replacement scenarios.In this paper, this work is briefly reviewed, followed by presentation of results on the use of the one-cycle criterion for extended opportunity-based age replacement. In this model, a unit is replaced correctively in case of failure or preventively upon reaching a specific age, as in the standard age replacement model. In addition, the unit is replaced preventively if opportunities occur during a period before the optimal age replacement time, for example in case production stops due to failure or preventive replacement of other units in the same production process. As such, costs of preventive replacement in the latter case are likely to be smaller than if preventive replacement of the specific unit requires a production stop, making it clear that it will be attractive to benefit from such opportunities occurring within a certain time range prior to scheduled preventive replacement. Optimality results for this model, with the one-cycle criterion, are presented and illustrated, and it is shown how limiting cases coincide with several other age replacement scenarios.Iskandar and Sandoh (An extended opportunity-based age replacement policy. RAIRO Ops Res., 2000, 34, 145-154) studied this model with the renewal reward criterion, but they assumed that all preventive replacements would incur the same costs. A generalization of their results is presented, allowing different costs for the two preventive replacement scenarios. This enables the comparison of the optimal replacement strategies according to the one-cycle criterion and the renewal reward criterion.

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Section: Opportunity-based Age Replacementmentioning

confidence: 96%

Extended opportunity-based age replacement with a one-cycle criterion

Venkat

Coolen

Coolen‐Schrijner

2010

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…Other approaches could be used, such as to use a one-cycle criterion (e.g. [12], [36]) and this might indeed describe more closely the pressing need to postpone. In spite of this, we develop the elements of the cost-rate: the expected cost per renewal cycle and the expected length of a renewal cycle.…”

Section: Maintenance Model Notationmentioning

confidence: 99%

“…These models allow one to explore maintenance planning options for systems that are close to retirement. The works from Coolen-Schrijner et al [12] and Venkat et al [36] present also alternatives to an infinite planning horizon. These authors propose the use of the 'one-cycle' optimality criterion.…”

Section: Finite Horizon Modelmentioning

confidence: 99%

A study of postponed replacement in a delay time model

Berrade

Scarf

Cavalcante

2017

Reliability Engineering & System Safety

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We develop a delay time model for a one component system with postponed replacement to analyze situations in which maintenance might not be executed immediately upon discovery of a defect in the system. Reasons for postponement are numerous: to avoid production disruption or unnecessary or ineffective replacement; to prepare for replacement; to extend component life; to wait for an opportunity. This paper explores conditions that make postponement cost-effective. We are interested in modelling the reality in which a maintainer either prioritizes functional continuity or is not confident of the inspection test indicating a defective state. In some cases more frequent inspection and a longer time limit for postponement are recommended to take advantage of maintenance opportunities, characterized by their low cost, arising after a positive inspection. However, when the cost of failure increases, a significant reduction in the time limit of postponement interval is observed. The examples reveal that both the time to defect arrival and delay time have a significant effect upon the cost-effectiveness of maintenance at the limit of postponement. Also, more simply, we find that opportunities must occur frequently enough and inspection should be a high quality procedure to risk postponement.

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“…A generalization of A (n) in order to deal with right-censored observations has also been presented [12] and was e.g. used in the development of NPI-based methods for opportunity-based replacement models in operational research [14,15] NPI has also been developed for multiple future observations, say m future observations, based on data consisting of n observations. This is based on A (n+m 1) , which implies A (n+k) for all k = 0, ..., m 2 [6,24].…”

Section: Preliminariesmentioning

confidence: 99%

Nonparametric predictive inference for stock returns

Baker¹,

Coolen‐Maturi

Coolen

2016

Journal of Applied Statistics

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. In finance, inferences about future asset returns are typically quantified with the use of parametric distributions and single-valued probabilities. It is attractive to use less restrictive inferential methods, including nonparametric methods which do not require distributional assumptions about variables, and imprecise probability methods which generalise the classical concept of probability to set-valued quantities. Main attractions include the flexibility of the inferences to adapt to the available data and that the level of imprecision in inferences can reflect the amount of data on which these are based. This paper introduces nonparametric predictive inference (NPI) for stock returns. NPI is a statistical approach based on few assumptions, with inferences strongly based on data and with uncertainty quantified via lower and upper probabilities. NPI is presented for inference about future stock returns, as a measure for risk and uncertainty, and for pairwise comparison of two stocks based on their future aggregate returns. The proposed NPI methods are illustrated using historical stock market data.

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Opportunity-based age replacement with a one-cycle criterion

Cited by 16 publications

References 17 publications

Extended opportunity-based age replacement with a one-cycle criterion

Extended opportunity-based age replacement with a one-cycle criterion

A study of postponed replacement in a delay time model

Nonparametric predictive inference for stock returns

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