• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.643-645
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• 2003

In competitive electricity markets, each GenCo establishes generator maintenance schedule and submits to ISO those. Then, ISO reviews and arranges maintenance schedule of each GenCo to meet the standard for reliability. This paper presents the new optimization model which can apply to competitive electricity markets. The object of this model is to minimize schedules variation of each GenCo and to satisfy system reliability.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.4
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• pp.344-350
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• 2002

An alternative to traditional production planning and control systems such as MRP and JIT is the drum-buffer-rope(DBR). Using the DBR system, companies can achieve a large reduction of work-in-process (WIP) and finished-goods inventories (FGI), significant improvement in scheduling performance, and substantial earnings increase. The purpose of this paper is to analyze the effect of the DBR system in a serial production line. Using Markov process, we modeled a DBR system with three stages. For the model developed, we analyze the system characteristics and then present an optimization model for system design. The system performance is also analyzed through sensitivity analysis.

• Journal of Information Technology Services
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• v.9 no.4
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• pp.123-133
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• 2010

There are many occasions on which the critical decisions should be made in software projects. Those decisions are basically related to estimating and predicting project parameters such as costs, efforts, and duration. The project managers are looking for methods to make better decisions. The decisions about project parameters are recommended to be performed based on historical data of Similar projects. The measures of the tasks in past projects may have different shapes of distributions. we need to add those measures to get a predicted project measures. To add measures with different shapes of distribution, we need to use Monte Carlo Simulation. In this paper, we suggest applying Monte Carlo Simulation for supporting decision makings in software project. We implemented best-fit case and scheduling estimations with Cristal Ball, a commercial product of Monte Carlo simulation and showed how the suggested approach supports those critical decision makings.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.641-644
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• 2018

열간 자유 단조는 고온으로 가열한 강피에 압력을 가하여 원하는 형상을 빚는 공정이다. 가열로에서 여러 개의 강피를 동시에 가열하며 목표 온도에 도달하면 꺼내어 다음 공정을 진행한다. 이때 가열로에 투입하는 소재의 조합과 후단 공정을 위해 소재를 꺼내는 순서가 가열로의 에너지 효율에 영향을 끼친다. 본 논문에서는 열간 자유 단조의 에너지 효율을 높이기 위한 비용 예측 모형 기반 작업 계획 최적화 방안을 제안한다. 유전 알고리즘을 이용하여 가열로 강피 조합을 최적화하며 각 설비별 작업 할당 규칙에 따라 전체 작업 계획을 수립한다. 시뮬레이션 기반으로 후보 작업 계획을 평가하여 계획을 최적화 하며 이를 위해 각 설비별 공정 소요 시간 및 에너지 사용량 예측 모형을 이용한다. 예측 모형은 공정 데이터를 기반으로 기계 학습 알고리즘을 적용하여 학습한다. 또한 주기적인 재계획을 통해 예측의 불확실성으로 인해 작업의 진행이 계획대로 이루어지지 않는 문제점을 해결하고자 한다.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.98-101
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• 2004

Linear Programming(LP) is the term used for defining a wide range of optimization problems in which the objective function to be minimized or maximized is linear in the unknown variables and the constraints are a combination of linear equalities and inequalities. LP problems occur in many real-life economic situations where profits are to be maximized or costs minimized with constraint limits on resources. While the simplex method introduced in a later reference can be used for hand solution of LP problems, computer use becomes necessary even for a small number of variables. Problems involving diet decisions, transportation, production and manufacturing, product mix, engineering limit analysis in design, airline scheduling, and so on are solved using computers. This technique is called Sequential Linear Programming (SLP). This paper describes LP's problems and solves a LP's problems using the neural networks.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.939-941
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• 2019

단조는 강괴를 고온으로 가열하고 원하는 형상으로 만드는 공정이다. 가열로에 강괴를 장입하여 가열하고, 고온의 강괴에 프레스, 절단 공정을 적절히 반복하여 원하는 형상으로 만든다. 형상이 완성된 강괴의 경도 및 강도를 조절하기 위해 열처리 공정을 진행한다. 열처리로에 여러 개의 강괴를 장입하여 가열하기 때문에 에너지 비용이 많이 소모된다. 열처리 공정 비용은 열처리 공정의 종류와 장입되는 강괴들의 특성 및 수량 등에 따라서 결정된다. 열처리로에 장입할 강괴 조합을 최적화함으로써 비용을 최소화시킬 수 있다. 따라서 본 논문에서는 비용 예측 모형을 이용하여 열처리로 작업 계획을 최적화하는 방안을 제안한다. 비용 예측 모형은 IoT 인프라를 기반으로 수집한 공정 데이터를 이용하여 학습한다. 다양한 열처리로 작업 계획은 학습한 모형 기반의 시뮬레이션을 통해 평가하여 유전 알고리즘을 기반으로 최적화한다. 최적의 열처리로 작업 계획을 수립함으로써 공정 비용을 최소화하고 에너지 효율을 극대화할 수 있다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.54
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• pp.77-85
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• 2000

One form of job shop scheduling problem in contemporary automated manufacturing such as flexible manufacturing systems (FMS's) is presented which we call the FMS dispatching priority problem. The FMS dispatching priority problem seeks the best dispatching priority of parts and operations, and is essentially a combinatorial optimization problem. Because of the complicated mechanism of the system, the performance of a given dispatching priority must be evaluated via simulation. Simulated annealing have been applied to the problem, and it is found that appropriate parameter setting will be desirable to get good, if not the optimal, solutions within a limited amount of time under the presence of heavy computational burden due to simulation. More specifically, experiments reveal that initial temperature is the single most important factor among other parameters and factors, and that the appropriate initial temperature depends on the allowable computer time in such a way that the less time one can afford to spend, the lower the appropriate initial temperature should be.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.2
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• pp.63-69
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• 2019

Recently, ultra-low latency flash storage devices such as Z-SSD and Optane SSD were introduced with the significant technological improvement in the storage devices which provide much faster response time than today's other NVMe SSDs. With such ultra-low latency, $10{\mu}s$, storage devices the cost of context switch could be an overhead during interrupt-driven I/O completion process. As an interrupt-driven I/O completion process could bring an interrupt handling overhead, polling or hybrid-polling for the I/O completion is known to perform better. In this paper, we analyze tail latency problem in a polling process caused by process scheduling in data center environment where multiple applications run simultaneously under one system and we introduce our adaptive polling selection technique which dynamically selects efficient processing method between two techniques according to the system's conditions.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.3
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• pp.113-117
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• 2021

The data gathering delay and the network lifetime are important indicators to measure the service quality of wireless sensor and actuator networks (WSANs). This study proposes a dynamically cluster head (CH) selection strategy and automatic scheduling scheme of collectors for prolonging the network lifetime and shorting data gathering delay in WSAN. First the monitoring region is equally divided into several subregions and each subregion dynamically selects a sensor node as CH. These can balance the energy consumption of sensor node thereby prolonging the network lifetime. Then a task allocation method based on genetic algorithm is proposed to uniformly assign tasks to actuators. Finally the trajectory of each actuator is optimized by ant colony optimization algorithm. Simulations are conducted to evaluate the effectiveness of the proposed method and the results show that the method performs better to extend network lifetime while also reducing data delay.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.830-833
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• 2020

콜센터에서 인력 스케줄링은 매우 중요하다. 모든 콜센터에서 인건비는 고정비 성격이 강하여 차지하는 비중이 매우 높아 콜센터의 이익을 좌지우지한다. 그렇기 때문에 콜센터의 적정 인력의 고용과 배치는 인건비 뿐만 아니라 콜 성공률 또한 직결되어 있어 콜센터 운영에서 중요한 사안이라고 할 수 있다. 대부분의 콜센터가 현재까지도 관리자의 경험에 의해 인력배치를 수립하는데, 이러한 방법은 과학적이지 않으며 인원수에 영향을 미치는 모든 변수들을 고려할 수 없다. 과거 수학적 모델을 수립하는 것이었다면, 지금은 모델을 학습시키고, 학습된 모델을 기반으로 미래의 고객과 인원수를 예측해야 한다. 본 논문에서는 수리제약식을 통해 다양한 변수들을 고려하고 비선형 정수 계획법과 딥러닝 기반의 예측 값을 이용하여 비선형 정수계획법을 통해 최적의 인력배치 스케줄링을 수립하였다.