• Journal of the Korean Institute of Educational Facilities
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• v.26 no.6
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• pp.29-37
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• 2019

The research institute has a function to get the research outcome through the various experiments, data collection and analysis. Therefore, research building is important to keep the research condition or experiment environment. But buildings would be deteriorated and leaded into the deterrence of research. Maintenance is planned to protect the research building condition through various general repair or heavy repair. The heavy repair is generally conducted in massive repair scope or main components preparation. In this paper, it aimed at analyzing the effect of the massive or main components repair with inputted cost and its resulted output. In order to analyze the effect of a massive repair, it used the Benefit/Cost analysis and sensitivity analysis. Results of this study are as follows : The benefit/cost analysis shows that research building whose researcher continuously live and study has good effect. On the contrary, pace of the the experimental function is not good effect in benefit/cost analysis. But the experimental function is indispensible to get the research outcom for the research goal. Therefore, the experimental function will be planned to repair and get the historical repair data because the proper repair time would be prepared to cut down the repair cost.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2014.05a
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• pp.162-163
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• 2014

Operation and maintenance stage consists the largest portion of project life cycle cost. Appropriate management and analysis of such stages have massive effect on the total project cost. The effective prediction of optimized repair period is one of main factors in ㅌ management. However, it has been analyzed that the prediction of appropriate repair period revealed limitations in reliability. Therefore, this study suggests a methodology of repair period prediction by dividing finished projects into similar groups with same properties to be compared with the target project using quantitative variables.

• Journal of the Korean Institute of Educational Facilities
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• v.23 no.6
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• pp.27-34
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• 2016

Although the scale and number of university facilities have increased, the maintenance of these facilities has not been efficient as maintenance costs incurred during facility operation periods, as well as management time, have not been sufficiently considered. In this study, the maintenance record data of national universities was compared and analyzed to analyze factors that incur maintenance costs according to the uses and costs of the facilities. According to the results, the number of cases of maintenance and repair work for basic educational facilities was shown to be the largest, accounting for 79%. The number of maintenance and repair work occurred according to work type was examined. According to the results, the highest ratio of maintenance and repair work occurred in the field of construction, specifically, the maintenance costs required for facility supplementation accounted for the highest ratio. Maintenance costs per unit area decreased over time in the newer university facilities, while showing a tendency to increase in older university facilities. However, there were cases where maintenance costs did not show any trends due to factors such as the limited maintenance budgets owing to the characteristics of public service facilities and facilities in the stage of deterioration or destruction after the stage of stabilization. This study is considered to be helpful for efficient university facility maintenance plans and budget calculations.

• 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.

• KIEAE Journal
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• v.14 no.5
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• pp.103-109
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• 2014

Apartment housing should require the systematic maintenance to provide the decent living condition during its life. Each household should participate the maintenance activities and pay for the repair. Therefore, the required cost for repair would be needed to plan in the repair schedule because each household could not pay the much repair money at a time. After apartment constructed, a long term repair plan would be prepared in repair time, repair scope and a required cost. A few studies are said that the repair cost depends on the building deterioration, elapsed year and management factors. The above factors are not be certain to affect the repair management while it may be important to prepare the required money and the repair time. In this paper, it aimed at analyzing the correlation between the repair management and the management factors, especially total area, number of household. This would educe the unit cost per number of household and management area in the individual boiler and elevator with full change and full repair. The unit cost per number of household and area for full change are about 199,000 won/household and $1,954won/m^2$ in the individual boiler respectively. The unit cost of the elevator for full change is 94,000 won/household and $5,429won/m^2$ respectively. Second, this study shows that the elapsed year after construction would not be related the repair unit cost.

• Journal of Korean Society for Quality Management
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• v.31 no.1
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• pp.142-147
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• 2003

Burn-in is a widely used method to eliminate the initial failures. Preventive maintenance policy such as block replacement with minimal repair at failure is often used in field operation. In this, paper burn-in and maintenance policy are taken into consideration at the same time. The cost of a minimal repair is assumed to be a non-decreasing function of its age. The problems of determining optimal burn-in times and optimal maintenance policy are considered.

• 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.

• International Journal of Reliability and Applications
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• v.12 no.1
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• pp.41-48
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• 2011

This paper considers the maintenance model suggested by Jung and Park (2010) to adopt the Bayesian approach and obtain an optimal replacement policy following the expiration of NFRRW. As the criteria to determine the optimal maintenance period, we use the expected cost during the life cycle of the system. When the failure times are assumed to follow a Weibull distribution with unknown parameters, we propose an optimal maintenance policy based on the Bayesian approach. Also, we describe the revision of uncertainty about parameters in the light of data observed. Some numerical examples are presented for illustrative purpose.

• Journal of the military operations research society of Korea
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• v.30 no.2
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• pp.96-107
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• 2004

This paper deals with $\ulcorner$Requirement Decision Model for Repair Parts supplied by the Government$\lrcorner$ which is to reduce Aircraft Contract Maintenance Cost. It aims to find solutions to the fundamental problems of the Aircraft Contract Maintenance System. Under the current Aircraft Contract Maintenance System, it is hard to forecast the exact demand of repair parts, so support rate of Repair Parts supplied by the Government is restricted under 50 percent. It is inevitable to purchase Repair Parts from the firm with much higher price than those of Government source. However, absence of fixed demand pattern makes it difficult to improve accuracy of demand forecast. As a solution to these problems, this model prevents a cost increase due to the unit price difference between Repair Parts supplied by the Government and Repair Parts purchased by the Firm. It also reflects demand characteristics of each repair part, and prevents continual stock increase by setting an upper limit on the amount of Repair Parts supplied by the Government. The effectiveness of this model is verified by empirical analysis using the latest raw data. By applying this model to real situation, we expect to reduce about 4 billion won every year.

• Journal of Applied Reliability
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• v.11 no.2
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• pp.167-176
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• 2011

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 periodic time and the number for the periodic preventive maintenance by using Nakagawa's Algorithm, which minimizes the expected cost per unit time.