• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.193-196
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• 1997

In this paper we consider a Bayesian theoretic approach to periodic incomplete preventive maintenance with minimal repair at failure. We assume that the system failure rate is increasing as the frequency of PM increases and that the system is replaced at the K-th PM under this maintenance strategy. The optimal policies which minimize the expected cost rates are discussed. We seek the optimal periodic PM interval x and replacement time K under a Weibull failure intensity. Assuming suitable prior distribution for the Weibull parameters, we derive the posterior distribution incorporating failure data and obtain the updated optimal replacement strategies.

• Proceedings of the Korean Reliability Society Conference
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• 2005.06a
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• pp.115-120
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• 2005

This paper introduces models for preventive maintenance policies and considers periodic preventive maintenance policy with minimal repair when the failure of system occurs. It is assumed that minimal repairs do not change the failure rate of the system. The failure rate under prevention maintenance received an effect by a previously prevention maintenance and the slope of failure rate increases the model where it considered. Also the start point of failure rate under prevention maintenance considers the degradation of system and that it increases quotient, it assumed. Per unit time it bought an expectation cost from under this prevention maintenance policy. We obtain the optimal period time and the number for the periodic preventive maintenance by using Nakagawa's Algorithm, which minimizes the expected cost rate per unit time. Finally, it suppose that the failure time of a system has a Weibull distribution as an example and we obtain an expected cost rate per unit time the optimal period time and the number when cost of replacement and cost of minimal repair change.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.83-87
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• 1999

The purpose of this study is to provide the new method for selection of a close to optimal scalar control of linear time-periodic system. The case of scalar control is considered, the gain matrix being assumed to be at worst periodic with the system period T. The form of gain matrix may have various kinds but must have same period, for example, one of each element being represented by Fourier series. As the optimal gain matrix I consider the matrix ensuring the minimum value of the larger real part of the Poincare exponents of the system. Finally we present a pole placement algorithm to make the given system be stable. It is possible to determine the stability of the given periodic system without get the analytic solution. The application of the method does not require the construction of the Floquet solution. At present state of determination of the gain matrix for this case will be done only by systematic numerical search procedures.

• Proceedings of the Korea Society for Simulation Conference
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• 2003.06a
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• pp.129-134
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• 2003

In this paper, we determine optimal reduction in the lead time and setup cost for some stochastic inventory models. And we propose more general model that allow the backorder rate as a control variable. We first assume that the lead time demand follows a normal distribution. And we assume that the backorder rate is dependent on the length of lead time through the amount of shortages. The stochastic models analyzed in this paper are the classical continuous and periodic review policy models with a mixture of backorders and lost sales. For each of these models, we provide a sufficient conditions for the uniqueness of the optimal operating policy. We also develop algorithms for solving these models and provide illustrative numerical examples.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1169-1175
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• 2009

In this paper, the inferences of data obtained from periodic inspection and type I censoring for the step-stress accelerated life test are studied. The exponential distribution with a failure rate function that a log-linear function of stress and the tampered failure rate model are considered. The maximum likelihood estimators of the model parameters are estimated and also the optimal stress change time which minimize the asymptotic variance of maximum likelihood estimators of parameters is determined. A numerical example will be given to illustrate the proposed inferential procedures and the sensitivity of the asymptotic variance of the estimated mean by the guessed parameters is investigated.

• Journal of the Korea Society of Computer and Information
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• v.23 no.8
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• pp.133-142
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• 2018

Subway metro scheduling is one of the most important problems impacting passenger convenience today. To operate efficiently, the Seoul metro uses regular, periodic schedules for all lanes, both north and southbound. However, many past studies suggest that non-periodic scheduling would better optimize costs. Since the Seoul metro is continuously facing a deficit, adopting a non-periodic schedule may be necessary. Two objectives are presented; the first, to minimize the average passengers' waiting time, and the second, to minimize total costs, the sum of the passenger waiting time, and the operational costs. In this paper, we use passenger smart card data and a precise estimation of transfer times. To find the optimal time-table, a genetic algorithm is used to find the best solution for both objectives. Using Python 3.5 for the analysis, for the first objective, we are able to reduce the average waiting time, even when there are fewer trains. For the second objective, we are able to save about 4.5 thousand USD with six fewer trains.

• 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.

• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.77-86
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• 2008

This paper develops the optimal periodic preventive maintenance policy following the expiration of non-renewing warranty. We assume that Wu and Clements-Croome's (2005) periodic PM model with random maintenance quality is utilized to maintain the system after the non-renewing warranty is expired. Given the cost structure to the user during the cycle of the product, we drive the expressions for the expected cost rate per unit time. Also, 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.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.775-784
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• 2011

In this paper, we consider the periodic preventive maintenance model for repairable system following the expiration of non-renewing free replacement-repair warranty (NFRRW). 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.

• Communications for Statistical Applications and Methods
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• v.17 no.6
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• pp.865-877
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• 2010

This paper considers the periodic preventive maintenance model for a repairable system following warranty expiration. We consider three types of warranty policies: free repair warranty, pro-rata repair warranty, and combination repair warranty. Under these preventive maintenance models, we derive the expressions for the expected cycle length, the total expected cost, and the expected cost rate per unit time. In addition, we explain 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 a Weibull distribution case.