• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.791-798
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• 2014

Wu and Clements-Croome (2005) suggest the preventive maintenance (PM) model with random maintenance quality. They assume that each PM resets the failure rate to zero and the rate of increases of the failure rate gets higher after each additional PM. However a system may not be restored to as good as new immediately after the completion of PM. Thus, this paper modifies the Wu and Clements-Croome's PM model and then the optimal PM policy is suggested. To determine the optimal PM policy, we utilize the expected cost rate per unit time for our model. That is, we obtain the optimal number and the optimal period by minimizing the expected cost rate per unit time. The numerical examples are presented for illustrative purpose.

• International Journal of Reliability and Applications
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• v.2 no.2
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• pp.99-105
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• 2001

Delay time analysis is a pragmatic mathematical concept readily embraced by engineers which has been developed as a means to model maintenance decision problem. This paper considers an inspection period using delay time analysis for fishing vessel equipment. We assume that delay time has a Weibull distribution. In this paper, we determine the optimal inspection period which minimize the expected downtime per unit time. Explicit solutions for the optimal inspection are presented for illustrative purposes.

• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.151-162
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• 2013

Due to a growing demand for the second-hand product, especially for the expensive one, the warranty and maintenance policies for such products have been studied to improve the product reliability of late. In this paper we study a periodic maintenance model for the second-hand product which is purchased by the customer at the age of $x$. When purchased, the dealer provides a warranty of a fixed length during which the product is maintained periodically to reduce the failure rate of the product and thus, to improve the reliability after each maintenance is served. If a failure occurs between two successive maintenances, only minimal repair is conducted. As for the warranty policy, we adopt free non-renewing repair action on each failure, in addition to the periodic maintenance service during the warranty period. Thus, under the given warranty policy, all the maintenance and repair costs incurred during the warranty period are charged to the dealer. For the proposed periodic maintenance scheme, we formulate a cost model to evaluate the expected total cost charged to the dealer during the warranty period and derive an optimal upgrade level of the failure rate at each maintenance to minimize the expected total warranty cost from the perspective of the dealer. We also present numerical results for an optimal upgrade level based on the proposed methods.

• Communications for Statistical Applications and Methods
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• v.12 no.1
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• pp.1-9
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• 2005

Burn-in is an engineering method which is used to eliminate early failures of products or systems after they have been produced. Recently, various models for determining optimal burn-in times have been developed, where some preventive maintenance policies were considered together with burn-in problem. In this paper, a survey of recent research in burn-in is undertaken.

• Journal of the military operations research society of Korea
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• v.4 no.1
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• pp.113-123
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• 1978

Consider a series system of two units, named 1 and 2, respectively. Two units are observed at the beginning of discrete time periods t=0,1,2, $cdots$ and classified as being in one of a countable number of states. Let (i, r) be a state of the system at time t, when the state of unit 1 is i and state of unit 2 is r at time t, Under some conditions, the opportunistic replacement policy that minimizes the expected total discounted cost or the average cost of maintenance is shown to be characterized by the control limits $i^{*}(r)$ (a function of r) and $r^{*}(i)$ (a function of i) : (a) in observed state (i, r), the optimal policy for unit 1 is to replace if $i{\ge}i^{*}(r)$ and no action otherwise; (b) in observed state (i, r), the optimal policy for unit 2 is to replace if $r{\ge}r^{*}(i)$ and no action otherwise. In addition, this paper also develops optimal policy in the finite time horizon case, where time horizon is fixed or a finite integer valued r.v. with known pmf.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.999-1007
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• 2009

This paper considers the optimal periodic preventive maintenance (PM) policy following the expiration of free-repair warranty. We assume that two periodic PM models with random maintenance quality which were proposed by Wu and Clements-Croome (2005) and Jung (2006b), respectively. Given the cost structure to the user during the cycle of the product, we derive the expressions for the expected cost rate per unit time. Also, we obtain the optimal PM number and the optimal PM period by minimizing the expected cost rate per unit time. The numerical examples are presented for illustrative purpose.

• Journal of the Korean Society for Railway
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• v.16 no.3
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• pp.196-201
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• 2013

As there is no failure data for the entire lifecycle of a product, when analyzing reliability measures based on early failure data only, there may be a significant error between the estimated mean life and the real one, because it can be underestimated, or on the other hand, it can be overestimated when analyzing reliability measures based on a large amount of censored data with the failure data. To resolve the issue, this study proposes an optimal sampling estimation procedure that selects the proportion of censored data to estimate the optimal distribution with the idea that the estimated distribution could be approximated as closely as the real life distribution. This would work if we sampled the optimal proportion on the censored data, because failure data has real intrinsic distribution in any situation. We validate the proposed procedure using an actual example. If the proposed method is applied to the maintenance policy of TWC (Train to Wayside Communication) system, then we can establish the optimal maintenance policy. Thus, we expect that it will be effective for improvement of reliability and cost savings.

• Journal of Korean Society for Quality Management
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• v.21 no.2
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• pp.109-120
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• 1993

Most systems are composed of components which have different failure chracteristics. Since the failure characteristics of components is different, it is rational and reasonable to establish a maintenance model to be considered repair and replacement policies which are proper to failure characteristics of these components. This paper proposes the age replacement time for a system composed of components which have different failure characteristics. In this model, it is assumed that a system is composed of a critical failure component, a major failure component, minor failure component. If any failure occurs to critical component before its age replacement time, the system should be replaced. If any failure does not occur until its age replacement time, preventive replacement should be performed at age replacement time T. Major component is minimal repaired if any failure occurs during operation. Minor component should be replaced as soon as failure is found. This paper determines the optimal replacement time of the system which minimize, total maintenance cost and initial stock Quantity of minor component within this optimal replacement time. Numerical example illustrates these results.

• Management Science and Financial Engineering
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• v.3 no.1
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• pp.57-74
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• 1997

An EOQ-like inventory model for a manufacturing process is studied. The system is assumed to deteriorate during the production process. The results are either the production of a number of defective items, or the breakdown of the production machine. The optimal production lot size is derived. The model is extended to the case in which the probabilities of making defective items and machine breakdowns are a function of both the quantity (amount) and quality (performance) of the consumed setup cost (including the preventive maintenance cost). We further assume that the setup performance can be improved by investing in the performance improvement program. Hence, the same or a better setup outcome can be achieved with a lower setup cost. We then investigate the optimal setup cost and investment policy simultaneously, thereby achieving a better process quality and setup cost reduction concurrently.

• International Journal of Reliability and Applications
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• v.3 no.3
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• pp.113-124
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• 2002

This paper suggests the optimal periodic preventive maintenance policies after the combination warranty is expired. After the combination warranty is expired, a repairable system undergoes PM periodically and is minimally repaired at each failure. And also the system is replaced by a new system at the N th PM. In this case, we derive the mathematical formula for the expected cost rate per unit time. The optimal number and period for the periodic PM that minimize the expected cost rate per unit time are obtained. Some numerical examples are presented for illustrate purpose.