• Journal of Korean Institute of Industrial Engineers
• /
• v.21 no.4
• /
• pp.507-517
• /
• 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 the military operations research society of Korea
• /
• v.24 no.2
• /
• pp.43-56
• /
• 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
• /
• v.12 no.1
• /
• pp.41-45
• /
• 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.

• International Journal of Reliability and Applications
• /
• v.12 no.2
• /
• pp.117-122
• /
• 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
• /
• v.17 no.3
• /
• pp.188-196
• /
• 2017

Purpose: The purpose of this research is to determine optimal replacement age using non-informative prior information and Bayesian method. Methods: We propose a novel approach using Bayesian method to determine the optimal replacement age in block replacement policy by defining the prior probability with data on failure time and repair time. The Marcov Chain Monte Carlo simulation is used to investigate the asymptotic distribution of posterior parameters. Results: An optimal replacement age of block replacement policy is determined which minimizes cost and nonoperating time when no information on prior distribution of parameters is given. Conclusion: We find the posterior distribution of parameters when lack of information on prior distribution, so that the optimal replacement age which minimizes the total cost and maximizes the total values is determined.

• Journal of Korean Society for Quality Management
• /
• v.21 no.2
• /
• pp.109-120
• /
• 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 Korean Society of Industrial and Systems Engineering
• /
• v.20 no.44
• /
• pp.263-271
• /
• 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.

• Korean Management Science Review
• /
• v.15 no.2
• /
• pp.143-152
• /
• 1998

This paper focuses on the determining the optimal replacement interval and the corresponding minimum cost of replacement for the renewal T-53 engine. It is assumed that sample failure data of T-53 engine are drawn from the mixed population, and then parameters of the failure distributions are estimated. On the basis of the above situation, the Multi-step Weibull distributions are estimated and then the optimal replacement time of T-53 engine is determined. This paper shows that if the replacement time is reduced to 2000 hours, the 2,217won of the replacement cost per unit time is only saved but also reliability of the T-53 engine is increased.

• Journal of Applied Reliability
• /
• v.12 no.2
• /
• pp.57-66
• /
• 2012

In this paper, we consider the periodic preventive maintenance model for a repairable system following the expiration of replacement-repair warranty. Under this preventive maintenance model, we derive the expressions for the expected cycle length, the expected total cost and the expected cost rate per unit time. Also, we determine the optimal preventive maintenance period and the optimal preventive maintenance number by minimizing the expected cost rate per unit time. Finally, the optimal periodic preventive maintenance policy is given for Weibull distribution case.

• Journal of Applied Reliability
• /
• v.13 no.4
• /
• pp.229-239
• /
• 2013

This paper reports a manner to use a Bayesian approach to derive the optimal replacement policy. In order to produce a system with minimal repair warranty, a replacement model with the extended warranty is considered. Within the warranty period, the failed system is minimally repaired by the manufacturer at no cost to the end-user. The failure time is assumed to follow a Weibull distribution with unknown parameters. The expected cost rate per unit time, from the end-user's viewpoints, is induced by the Bayesian approach, and the optimal replacement policy to minimize the cost rate is proposed. Finally, a numerical example illustrating to derive the optimal replacement policy based on the Bayesian approach is described.