• Journal of Korean Institute of Industrial Engineers
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• v.21 no.4
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• pp.507-517
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• 1995

The failure rate of an item depends on operational environment. When an item has a chance failure period and a wearout failure period in sequel, the severity of operational environment causes the increase in the slop of wearout failure rate or the increase in the magnitude of chance failure rate. For such a change of operational environment, this paper concerns the change of optimal preventive replacement time. Two preventive replacement policies, age replacement policy and periodic replacement policy with minimal repair, are considered. Investigated properties are: (a) in age replacement policy, optimal preventive replacement time increases as the chance failure rate increases and optimal preventive replacement time decreases as the slope of wearout failure rate increases, and (b) in periodic replacement policy with minimal repair, optimal preventive replacement time increases as the slope of wearout failure rate increases; however, the change of chance failure rate does not alter the optimal preventive replacement time.

• 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.

• Journal of the military operations research society of Korea
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• v.24 no.2
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• pp.43-56
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• 1998

This paper focuses on the optimal replacement time of engine modules of the F16 aircraft. Generally, the optimal replacement time of those should be determined to minimize the replacement cost due to out of order and opportunistic replacement cost of operation cost of remaining period. This paper determined the optimal replacement time by using the opportunistic replacement algorithm that is developed by Forbes and Wyatt. Some real data are utilized but a few data is estimated due to limitation of data. As a result, fan module only reaches to the opportunistic replacement time. The optimal replacement time of the fan module is derived as 1740 cycles. Therefore, the optimal replacement policy of engine modules of the F16 is that fan module should be replaced whenever it is out of order under 1740 cycles and whenever core module is out of order over 1740 cycles.

• Industrial Engineering and Management Systems
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• v.12 no.1
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• pp.41-45
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• 2013

Age replacement policy is a commonly policy in maintenance management of spare part. It means that a spare part is always replaced at failure or fixed time after its installation, whichever occurs first. An optimal age replacement policy of spare parts concerns with finding the optimal replacement time determined by minimizing the expected cost per unit time. The age of the part was generally assumed to be a random variable in the past literatures, but in many situations, there are few or even no observed data to estimate the probability distribution of part's lifetime. In order to solve this phenomenon, a new uncertain age replacement policy has been proposed recently, in which the age of the part was assumed to be an uncertain variable. This paper discusses the optimal age replacement policies by dealing with the parts' lifetimes as different distributed uncertain variables. Several results on the optimal age replacement time are provided when the lifetimes are described by the uncertain linear, zigzag and lognormal distributions.

• Journal of Korean Society on Water Environment
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• v.23 no.3
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• pp.356-366
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• 2007

In this paper a methodology is developed to prioritize replacement of water distribution pipes according to the economical efficiency of replacement and assess the long-term effects of water main replacement policies on water distribution systems. The methodology is implemented with MATLAB to develop a computer algorithm which is used to apply the methodology to a case study water distribution system. A pipe break prediction model is used to estimate future costs of pipe repair and replacement, and the economically optimal replacement time of a pipe is estimated by obtaining the time at which the present worth of the total costs of repair and replacement is minimum. The equation for estimating the present worth of the total cost is modified to reflect the fact that a pipe can be replaced in between of failure events. The results of the analyses show that about 9.5% of the pipes in the case study system is required to be replaced within the planning horizon. Analyses of the yearly pipe replacement requirements for the case study system are provided along with the compositions of the replacement. The effects of water main replacement policies, for which yearly replacement length scenario and yearly replacement budget scenario are used, during a planning horizon are simulated in terms of the predicted number of pipe failures and the saved repair costs.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.78-89
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• 2022

The purpose of this study was to propose useful suggestion by analyzing preventive replacement policy under which there are minor and major failure. Here, major failure is defined as the failure of system which causes the system to stop working, however, the minor failure is defined as the situation in which the system is working but there exists inconvenience for the user to experience the degradation of performance. For this purpose, we formulated an expected cost rate as a function of periodic replacement time and the number of system update cycles. Then, using the probability and differentiation theory, we analyzed the cost rate function to find the optimal points for periodic replacement time and the number of system update cycles. Also, we present a numerical example to show how to apply our model to the real and practical situation in which even under the minor failure, the user of system is not willing to replace or repair the system immediately, instead he/she is willing to defer the repair or replacement until the periodic or preventive replacement time. Optimal preventive replacement timing using two variables, which are periodic replacement time and the number of system update cycles, is provided and the effects of those variables on the cost are analyzed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.263-271
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• 1997

This paper presents a preventive maintenance model for determining the preventive replacement period of a system in which a failure rate is affected by the cumulative damage of fault and inspection. Especially, the failure rate function is considered to be a function of the cumulative damage of the fault and inspection time. Types of replacement considered are preventive replacement and failure replacement. Failure rate and expected cost function between replacement are derived. An optimal policy is obtained that minimizes the average cost per unit time for preventive replacement, failure replacement, inspection and repair.

• International Journal of Reliability and Applications
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• v.12 no.2
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• pp.117-122
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• 2011

In this paper, we suggest the optimal replacement policy following the expiration of repair warranty when the cost of minimal repair depends on the age of system. To do so, we first explain the replacement model under repair warranty. And then the optimal replacement policy following the expiration of repair warranty is discussed from the user's point of view. The criterion used to determine the optimality of the replacement model is the expected cost rate per unit time, which is obtained from the expected cycle length and the expected total cost for our replacement model. The numerical examples are given for illustrative purpose.

• Journal of Applied Reliability
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• v.11 no.4
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• pp.409-417
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• 2011

In this paper, we consider a replacement model following the expiration of warranty. In other words, this paper proposes the optimal replacement policy for a repairable system following the expiration of payable renewing replacement-non-renewing minimal repair warranty. The expected cost rate per unit time from the user's perspective is used to determine the optimality of the replacement policy. Thus, we derive the expressions for the expected cycle length and the expected total cost to obtain the expected cost rate per unit time. Finally, the numerical examples are presented for illustrative purpose.

• Journal of Integrative Natural Science
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• v.14 no.4
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• pp.197-204
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• 2021

Under the two-phase warranty, the warranty period is divided into two intervals, one of which is for renewing replacement warranty, and the other is for minimal repair warranty. Jung^[13] discusses the two types of extended two-phase warranty models. In this paper, we suggest the replacement model after the extended two-phase warranty that has been proposed by Jung^[13]. To determine the optimal replacement policy, we adopt the expected cost rate per unit time. So, the expressions for the total expected cost, the expected length of the cycle and the expected cost rate per unit time from the user's point of view are derived. Also, we discuss the optimal replacement policy and the uniqueness of the solution for the optimization. Furthermore, the numerical examples are provided to illustrate the proposed the replacement model.