• 산업공학
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• 제25권1호
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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.

• 한국건설관리학회논문집
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• 제5권2호
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• pp.211-218
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• 2004

다수의 시설물을 보유한 기관에서는 유지보수와 리모델링(M/R) 프로젝트들이 동시에 진행되고 있다. 내부기능인력에 의해 프로젝트를 수행하는 자원제약 환경에서 프로젝트 관리자는 각 프로젝트의 완료일과 기관내 가용한 자원에 대한 제한을 고려하며 프로그램을 계획하고 관리하게 된다. 다중 자원제약 M/R 프로그램의 효과적 관리를 위해 이 논문은 장기적인 프로그램 계획축과 단기적인 일정창으로 구성되는 2단계 계층관리모델을 제안한다. 프로그램 플랜을 위한 연동계획기법은 연속되는 프로젝트 요청의 불확실성에 대처하기 위해 제안되었다. 일정창 속에서 마스터-일정계획을 수립하기 위해 개발된 PCR 일정계획 알고리즘은 M/R 프로그램에서의 작업반내 기능인력의 배치계획을 제공한다. 스프레드쉬트 인터페이스를 활용하여 M/R 프로그램의 일정계획 시스템을 개발하였고, 연속된 3개의 일정창으로 설계된 시뮬레이션 실험을 통해 개발된 일정계획 알고리즘과 기존의 자원제약 일정계획 휴리스틱의 성능을 비교하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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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.

• 대한산업공학회지
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• 제1권1호
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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.

• 한국산학기술학회논문지
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• 제20권10호
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• pp.149-158
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• 2019

본 논문에서는 다수의 구성품으로 구성된 시스템에 대해 Hidden Failure를 고려하여 최적의 계획 정비 스케줄링 문제에 대해 연구하였다. 계획 정비 스케줄링의 목적은 장기 운용 유지비용 최소화이다. 시스템 측면에서는 다수의 구성품을 그룹화하여 검사함으로써 시스템이 정지함으로 인해 발생할 수 있는 비용을 최소화 할 수 있다. 또한 각 구성품 별로 적절한 검사 주기를 선정하여 고장을 조기에 확인함으로써 고장 발생으로 인한 비용을 최소화 할 수 있다. 본 논문에서는 계산의 복잡성을 줄이기 위해 기존 연구에서 사용한 "Base interval approach"를 적용하였으며 추가적으로 다수 구성품의 동시 검사를 통한 검사비용 절감 효과를 고려하였다. 검사비용은 소요되는 시설, 장비 및 인력 비용으로 구성된다고 가정하였으며 유사한 구성품을 동시에 검사함으로써 검사비용이 절감되는 경우에 대한 분석을 실시하였다. 본 논문에서는 검사비용 절감 효과를 비교하기 위해 기존 연구에서 활용한 사례를 대상으로 분석을 실시하였고 이에 따른 비용 절감 효과를 시뮬레이션을 통해 비교하여 제시한다.

• 산업공학
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• 제22권1호
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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.

• 산업경영시스템학회지
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• 제38권3호
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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.

• 전기학회논문지
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• 제67권5호
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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.

• 전기학회논문지
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• 제66권2호
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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.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2010년도 춘계학술대회 논문집
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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.