• IE interfaces
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• v.25 no.1
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• pp.127-133
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• 2012

This paper considers the preventive maintenance scheduling problem of the cluster tool which is one of the most important manufacturing equipments in the next-generation semiconductor production environment. We define a random process that expresses the successive amount of chemicals accumulating inside the tool. Based on the renewal theory, we find the expected value and probability distribution of the time that the amount of accumulated chemicals exceeds a predetermined level. For a given probability that the accumulated chemicals exceeds the predetermined level we present a method to obtain the number of chamber operations to perform the preventive maintenance of that chamber. In addition, a method to get the preventive maintenance schedule for the whole cluster tool is presented. A numerical example is provided to illustrate our method.

• Korean Journal of Construction Engineering and Management
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• v.5 no.2 s.18
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• pp.211-218
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• 2004

In a large owner organization, a program manager of multiple maintenance and remodeling projects has experienced increasing scale and complexity of coordinating the M/R projects with in-house technicians who belong to multiple trade shops. This paper proposes a dual-level hierarchical planning strategy that consists of a program master plan in the long-term horizon and a master construction schedule in an operational scheduling window. A rolling horizon approach to the program master plan is proposed to deal with the external uncertainty of unknown stream of project requests. A resource-constrained scheduling algorithm is developed to generate the master construction schedule in a scheduling window. During development of the algorithm, more emphasis is placed on long-term organizational resource continuity, especially flow management of program constraint resources, than ephemeral events of an individual activity and project. Monte Carlo simulation experiments of three scheduling windows are used to evaluate the relative performance of the proposed scheduling algorithm against three popular scheduling heuristics for resource-constrained multiple projects.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.157.3-157
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• 2001

In this paper, the optimal maintenance scheduling for turbine with considering maintenance replacement rate was proposed in order to reduce the maintenance cost during the whole period of operation, meanwhile keeping current reliability of turbine. The proposed method is only based on a few limited available data with various factors relating to maintenance replacement and repair of turbine. The proposed method will be adopted by Kyushu Electric Power Co., Inc. from April in 2002 to determine the maintenance schedule of thermal power plants.

• Journal of Korean Institute of Industrial Engineers
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• v.1 no.1
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• pp.93-98
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• 1975

This paper presents a generalized model for determining minimum cost preventive maintenance schedules where accurate failure data are not available except the "average"(mean) and the "typical" value(mode) of the component lifetime.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.149-158
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• 2019

The scheduling of planned maintenance problem of a system consisting of a number of components was studied. The purpose of maintenance scheduling is to minimize the cost of maintaining long-term operations. On the system side, the cost of a system shutdown can be minimized by grouping and inspecting a number of components. In addition, proper inspection cycles can be selected for each component to identify the failure sufficiently early to minimize the cost of the failure. To reduce the complexity of the calculations, the 'base interval approach' used in previous studies was applied and, in addition, the inspection cost savings from simultaneous inspections of multiple components were considered. To compare the effectiveness of inspection cost savings, this paper presents the results of simulation analysis performed by referring to the cases in the existing studies.

• IE interfaces
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• v.22 no.1
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• pp.85-94
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• 2009

Nowadays, most of the major manufacturing companies prepare their manufacturing schedule using package based solutions. Even though the accuracy of the detail scheduling result is high at implementation, however, it is low during maintenance period. The main cause of low accuracy during maintenance period is due to difficulties in maintaining the accurate level of master data. In this paper, we propose to easily maintain setup time, which is one of the most important factors required in master data to achieve good scheduling result, after changing job. This paper is mainly focused on how to deduce the factors that influence the setup time in a roll-based manufacturing field with print operations. For this purpose, we employed rule based algorithm and applied for deciding setup time for the existing product items. Likewise, it can be applied to new items without any complex setup procedures, and, finally, it displays the result of the real setup-time and calculated setup-time.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.100-107
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• 2015

Recently as the manufacturers want competitiveness in dynamically changing environment, they are trying a lot of efforts to be efficient with their production systems, which may be achieved by diminishing unplanned operation stops. The operation stops and maintenance cost are known to be significantly decreased by adopting proper maintenance strategy. Therefore, the manufacturers were more getting interested in scheduling of exact maintenance scheduling to keep smooth operation and prevent unexpected stops. In this paper, we proposedan integrated maintenance approach in injection molding manufacturing line. It consists of predictive and preventive maintenance approach. The predictive maintenance uses the statistical process control technique with the real-time data and the preventive maintenance is based on the checking period of machine components or equipment. For the predictive maintenance approach, firstly, we identified components or equipment that are required maintenance, and then machine parameters that are related with the identified components or equipment. Second, we performed regression analysis to select the machine parameters that affect the quality of the manufactured products and are significant to the quality of the products. By this analysis, we can exclude the insignificant parameters from monitoring parameters and focus on the significant parameters. Third, we developed the statistical prediction models for the selected machine parameters. Current models include regression, exponential smoothing and so on. We used these models to decide abnormal patternand to schedule maintenance. Finally, for other components or equipment which is not covered by predictive approach, we adoptedpreventive maintenance approach. To show feasibility we developed an integrated maintenance support system in LabView Watchdog Agent and SQL Server environment and validated our proposed methodology with experimental data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.5
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• pp.601-610
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• 2018

The purpose of this paper is to establish a new optimum Generator Maintenance Scheduling(GMS) considering renewable energy generator. In this paper, the total renewable energy generation is estimated using hourly capacity factor of renewable energy generator. The GMS was optimized with the objective function of maximizing the minimum reserve rate, minimizing the probabilistic production cost, minimizing the loss of load expectation, and minimizing $CO_2$ emissions. In the case study of this paper, GMS considering renewable energy and GMS not considering renewable energy are shown by each objective function. And it shows scenarios of the reliability, the environmental and economical factors when two nuclear power plants inputted and ten coal thermal power plants shut downed respectively.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.2
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• pp.305-314
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• 2017

Most of the existing researches on systemwide optimization of generator maintenance scheduling do not consider the equivalent operating hours(EOHs) mainly due to the difficulties of calculating the EOHs of the CCGTs in the large scale system. In order to estimate the EOHs not only the operating hours but also the number of start-up/shutdown during the planning period should be estimated, which requires the mathematical model to incorporate the economic dispatch model and unit commitment model. The model is inherently modelled as a large scale mixed-integer nonlinear programming problem and the computation time increases exponentially and intractable as the system size grows. To make the problem tractable, this paper proposes an EOH calculation based on demand grouping by K-means clustering algorithm. Network congestion is also considered in order to improve the accuracy of EOH calculation. This proposed method is applied to the actual Korean electricity market and compared to other existing methods.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.1191-1202
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• 2010

The objective of this paper is to propose a Binary Artificial Bee Colony (BABC) to obtain the best/optimal solution for NP-hard scheduling problem of railway track maintenance. We can greatly maximize the objective value using proposed BABC within limited computation time. The proposed BABC mechanism is very efficient to find the best solution because of employing fewer control parameters.