• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.8
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• pp.883-888
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• 2014

Simulation is now using in various area as an effective decision analysis tool in complex environment of today. But, There is a focus to the simulation model development and execution better than result analysis. This article will emphasis to the importance of result analysis apart from model development in simulation, and will use R package for profit optimization simulation. R has a various function in statistic analysis and data manipulation, graphic display. So this research can show the value of R as a tool for simulation.

• Journal of the Korea Society for Simulation
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• v.22 no.2
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• pp.33-40
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• 2013

This paper explores a border security system based on agent-based modeling and simulation (ABMS). The ABMS software platform, map aware non-uniform automata, is used to model various scenarios and evaluate the border security system given a set of infiltrators who have evolutionary behavior governed by genetic algorithm (GA). we formulated an optimization model and approximately solved it using a GA in order to capture near optimal behavior of an infiltrating force. The results presented give two significant insights for our border security system in that optimizing the infiltrator's behavior can make a significant difference and the quantitative results regarding the infiltrator's avoidance of each asset can be viewed as capturing their relative importance.

• Journal of the Korea Society for Simulation
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• v.20 no.2
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• pp.67-76
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• 2011

Order picking in automotive service parts warehouses is considered to be the most labor-intensive operation. Such warehouses contain hundreds of thousands of items, but normally 20% of products contribute to about 80% of turnover according to Pareto's 80-20 principle. Therefore most fast moving items are located near an outbound area which is called the "Golden Zone". Order picking routing efficiency is related to productivity and labor cost. However, most companies use simple methods. In this paper, we describe a series of computational experiments over a set of test cases where, we compared various previously existing routing heuristics to an optimal algorithm. We focus on examining the influence of the golden zone on the performance and selection of routing methods. The results obtained show that the optimal routing method increases the productivity at least 17.2%, and all the routing methods have better performance as the pick up rate from the golden zone increases.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.4
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• pp.325-330
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• 2001

It has been realized that the results of circuit level simulation of neural networks, used for optimization problems, arc much different from those of algorism level simulation. In other words, the outputs converges asymptotically as time elapes, however, the input convergence depends on the value of parasitic conductance connected between input node and ground. Also, this conductance affects system performance. This paper discusses the influence of input conductance on the convergece of the continuous Hopfield neural networks. The convergence has been analyzed for the input and output nodes of neurons. Also, the characteristics of equilibrium points has been analyzed depending on different values of the input conductance.

• Journal of the Korea Institute of Military Science and Technology
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• v.2 no.1
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• pp.90-100
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• 1999

유전자 알고리즘은 전통적인 등반 알고리즘을 이용하여 구하기 어려웠던 최적화 문제를 해결하기 위한 강인한(Robust) 탐색 기법이다. 특히 목적함수가 (1)여러 개의 국부 최대치를 가지는 경우, (2)수학적으로 표현이 불가능하거나 어려운 경우, (3)목적함수에 교란 항(disturbance term)이 섞여 있을 경우도 우수한 탐색 능력을 갖는 것으로 알려져 있다. 본 논문에서는 유전자 알고리즘을 이용하여 나타나는 다양한 해집합을 형성하는 개체군을 군집성 분석(cluster analysis)을 이용하여 군집화하고, 각 군집에 부여된 군집 적합도에 따라서 최적해를 구함으로써 단순 유전자 알고리즘에 의한 최적화보다 훨씬 향상된 탐색 알고리즘을 제안하였다. 반응표면의 형태가 정형화한 테스트 함수의 형태로 나타난다고 가정한 경우에 대하여 몬테 칼로 시뮬레이션을 통하여 본 알고리즘을 적용하여 평가하고 분석하였다.

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2011.05a
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• pp.53-53
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• 2011

도금 시뮬레이션 기법은 도금 용액 개발에서부터 도금설비 제작, 공정 최적화 및 trouble shooting에 이르기까지 도금 산업 전반에서 응용이 될 수 있다. 현재 우리나라에서는 도금 시뮬레이션을 연구하는 연구자나 적용하여 사용하는 도금 업체는 매우 드물다. 본 발표에서는 도금 시뮬레이션 기법에 대한 이론과 절차 등을 설명하고 시뮬레이션 기법을 적용하여 공정 최적화나 도금 두께 균일화를 이룬 몇 가지 사례에 대하여 소개하고자 하였다.

• Journal of the Korean Society for Railway
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• v.12 no.2
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• pp.291-301
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• 2009

This paper presents the optimal design and analysis method of the train overhaul maintenance facility based on the simulation. Because the train is composed of a coach or more, we design the simulation model after analyzing the operation of train into train, coach, coach's body parts and wheel parts and soon. In simulation analysis, we consider the critical (dependent) factors and design (independent) parameters for the selection of alternatives and optimal design. Therefore, Multi Criteria Decision Making (MCDM) is proposed for the selection of alternatives and optimal method in order to find the optimal design factors. The case study for the above approach is used for the electronic locomotive overhaul maintenance facility. This paper provides a comprehensive framework for the train overhaul maintenance facility design using the simulation, MCDM and optimal methods. Therefore, the method developed for this research can be adopted for other enhancements in different but comparable situation.

• Journal of the Korea Society for Simulation
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• v.21 no.3
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• pp.79-88
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• 2012

Recently, the study in efficient operation, maintenance, and equipment-design have been growing rapidly in military industry to meet the required missions. Through out these studies, the importance of Concurrent Spare Parts(CSP) are emphasized. The CSP, which is critical to the operation and maintenance to enhance the availability, is offered together when a equipment is delivered. Despite its significance, th responsibility for determining the range and depth of CSP are done from administrative decision rather than engineering analysis. The purpose of the paper is to optimize the number of CSP per item using simulation and multiple regression. First, the result, as the change of operational availability, was gained from changing the number of change in simulation model. Second, mathematical regression was computed from the input and output data, and the number of CSP was optimized by multiple regression and linear programming; the constraint condition is the cost for optimization. The advantage of this study is to respond with the transition of constraint condition quickly. The cost per item is consistently altered in the development state of equipment. The speed of analysis, that simulation method is continuously performed whenever constraint condition is repeatedly altered, would be down. Therefore, this study is suitable for real development environment. In the future, the study based on the above concept improves the accuracy of optimization by the technical progress of multiple regression.

• Computational Structural Engineering
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• v.28 no.4
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• pp.21-24
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• 2015

SIMULIA Tosca Structure를 이용한 열변형력 최적화는 개발 과정에서 터보차저를 한층 더 개선하는데 결정적인 역할을 합니다. 열변형력 최적화는 일단 민감도 연구를 통해 터보차저의 뚜렷한 상보적 효과를 파악하여 긍정적 영향을 활용하고 부정적 영향을 해소하는데 유용합니다. 그리고 나서 이와 같이 전역으로 최적화된 형상을 토대로 국부적 형상 최적화를 실시하여 개선의 여지가 남아 있는 부위를 세부적으로 개선할 수 있습니다. 이와 같이 더욱 효율적인 개발 과정을 통해 성능과 수명이 향상된 배기용 터보차저를 개발할 수 있습니다. 게다가 시뮬레이션 및 최적화 기술을 지속적으로 활용하면 시험과 초기 비용을 절약할 수 있습니다.

• Journal of the Korea Society for Simulation
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• v.28 no.4
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• pp.21-32
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• 2019

Inspired by the social behavior models of a bird flock or fish school, particle swarm optimization (PSO) is a popular metaheuristic optimization algorithm and has been widely used from solving a complex optimization problem to learning a artificial neural network. However, PSO is difficult to apply to many real-life optimization problems involving stochastic noise, since it is originated in a deterministic environment. To resolve this problem, this paper incorporates a resampling method called the uncertainty evaluation (UE) method into PSO. The UE method allows the particles to converge on the accurate optimal solution quickly in a noisy environment by selecting the particles' global best position correctly, one of the significant factors in the performance of PSO. The results of comparative experiments on several benchmark problems demonstrated the improved performance of the propose algorithm compared to the existing studies. In addition, the results of the case study emphasize the necessity of this work. The proposed algorithm is expected to be effectively applied to optimize complex systems through digital twins in the fourth industrial revolution.