• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.1
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• pp.87-98
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• 2016

A single machine scheduling problem for jobs with stochastic processing time is considered in this study. Shortest processing time (SPT) sequencing according to the expected processing times of jobs is optimal for schedules with minimal expected mean flow time when all the jobs arrive to be scheduled and their expected processing times are known. However, SPT sequencing according to the expected processing time may not be optimal for the minimization of the mean flow time when the actual processing times of jobs are known. This study evaluates the complexity of SPT sequencing through a comparison of the mean flow times of schedules based on the expected processing times and actual processing times of randomly generated jobs. Evaluation results show that SPT sequencing according to the expected flow time exhibits chaotic variation to the optimal mean flow time. The relative deviation from the optimal mean flow time increases as the number of jobs, processing time, or coefficient of variation increases.

• Journal of the military operations research society of Korea
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• v.18 no.1
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• pp.61-73
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• 1992

Up to the present, the operating time has been studied on only a single aircraft attacking a single target or multiple targets under enemy threats. This study is to determine optimal operating time and appropriate size of aircrafts attacking multiple targets. Measures of mission effectiveness is defined through derivation of the probability of the various events associated with operating. By using these measures, the expected benefit of operating and the expected cost of operating are generated as a function of time. To formulate operating time determination model, the expected gain of operating is defined as the difference between the expected benefit of operating and the expected cost of operating. The model can be used to determine optimal operating time which maximizes the expected gain of operating, and can be used as the basis for determining the appropriate size of aircrafts.

• Journal of Applied Reliability
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• v.12 no.2
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• pp.57-66
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• 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 Korean Society for Quality Management
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• v.48 no.1
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• pp.201-214
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• 2020

Purpose: The purpose of this paper is to determine an optimal number of cycle times for the replacement under the circumstance where the system is replaced at the periodic time and the multiple number of working cycles whichever occurs first and the system is minimally repaired between the replacements if it fails. Methods: The system is replaced at periodic time () or cycle time, whichever occurs first, and is repaired minimally when it fails between successive replacements. To determine the optimal number of cycle times, the expected total cost rate is optimized with respect to the number of cycle times, where the expected total cost rate is defined as the ratio of the expected total cost between replacements to the expected time between replacements. Results: In this paper, we conduct a sensitivity analysis to find the following results. First, when the expected number of failures per unit time increases, the optimal number of cycle times decreases. Second, when the periodic time for replacement becomes longer, the optimal number of cycle times decreases. Third, when the expected value for exponential distribution of the cycle time increases, the optimal number of cycle times increases. Conclusion: A mathematical model is suggested to find the optimal number of cycle times and numerical examples are provided through the sensitivity analysis on the model parameters to see the patterns for changes of the optimal number of cycle times.

• Journal of Industrial Convergence
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• v.2 no.2
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• pp.127-147
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• 2004

This paper examines the time-series relations among expected return, risk, and book-to-market(B/M) at the portfolio level. The time-series analysis is a natural alternative to cross-sectional regressions. An alternative feature of the time-series regressions is that they focus on changes in expected returns, not on average returns. Using the time-series analysis, we can directly test whether the three-factor model explains time-varying expected returns better than the characteristic-based model. These results should help distinguish between the risk and mispricing stories. We find that B/M is strongly associated with changes in risk, as measured by the Fama and French(1993) three-factor model. After controlling for changes in risk, B/M contains little additional information about expected returns. The evidence suggests that the three-factor model explains time-varying expected returns better than the characteristic-based model.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.889-901
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• 2003

In this paper, we consider the optimal replacement policies following the expiration of the combination warranty. The combination warranty can be divided into the renewing combination warranty and the non-renewing combination warranty. The criterion used to determine the optimal replacement period is the overall value function based on the expected cost and the expected downtime. Thus, we obtain the expected cost rate per unit time and the expected downtime per unit time for our model. And then the overall value function suggested by Jiagn and Ji(2002) is applied to obtain the optimal replacement period. The numerical examples are presented for illustrative purpose.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.743-752
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• 2006

This paper considers a Bayesian approach to replacement policy model with minimal repair. We use the criterion based on the expected cost and the expected downtime to determine the optimal replacement period. To do so, we obtain the expected cost rate per unit time and the expected downtime per unit time, respectively. When the failure time is Weibull distribution with uncertain parameters, a Bayesian approach is established to formally express and update the uncertain parameters for determining an optimal maintenance policy. Especially, the overall value function suggested by Jiagn and Ji(2002) is applied to obtain the optimal replacement period. The numerical examples are presented for illustrative purpose.

• Journal of Applied Reliability
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• v.6 no.3
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• pp.195-203
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• 2006

This paper deals with cost analysis model in periodic maintenance policy. The repair policy with minimal repair is considered as follow : as the occurrence of failure between minimal repair and periodic interval time, unit is replaced by a new unit before the periodic maintenance time comes. Then total expected cost per unit time is calculated according to time delta t in a view of customer's. The total expected costs are included repair and usage cost : operating, fixed, minimal repair, periodic maintenance and new unit expected cost. Numerical example is shown in which failure time of item has Normal distribution.

• 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.