• 품질경영학회지
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• 제47권4호
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• pp.701-711
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• 2019

Purpose: The problem of optimizing redundancy allocation in multi-level systems is considered when each item in a multi-level system has alternative items with the same function. The number of redundancy of multi-level system is allocated to maximize the reliability of the system under path set and cost limitation constraints. Methods: Based on cost limitation and path set constraints, a mathematical model is established to maximize system reliability. Particle swarm optimization is employed for redundant allocation and verified by numerical experiments. Results: Comparing the particle swarm optimization method and the memetic algorithm for the 3 and 4 level systems, the particle swarm optimization method showed better performance for solution quality and search time. Particularly, the particle swarm optimization showed much less than the memetic algorithm for variation of results. Conclusion: The proposed particle swarm optimization considerably shortens the time to search for a feasible solution in MRAP with path set constraints. PS optimization is expected to reduce search time and propose the better solution for various problems related to MRAP.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2006년도 추계학술대회 논문집
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• pp.1198-1202
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• 2006

MTBF(Mean Time Between Failure)와 MTBSF(Mean Time Between Service Failure) are two representative quantitative reliability requirements for railway systems. There are the case that both of the two requirements are presented and the case that only one of them is presented in the specification of railway systems. we deal with the redundancy allocation problem to meet the two reliability requirements. The redundancy increases MTBSF while it decreases MTBF. Parallel redundancy and the exponential lifetime distribution of components are considered for the series systems. Mathematical model and example are presented for the redundancy optimization problem of minimizing the cost subjecting to MTBF and MTBSF requirements.

• 품질경영학회지
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• 제32권4호
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• pp.125-139
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• 2004

Generally, parallel redundancy is used to improve reliability in many systems. However, redundancy increases system cost, weight, volume, power, etc. Due to limited availability of these resources, the system designer has to maximize reliability subject to various constraints or minimize resources while satisfying the minimum requirement of system reliability. This paper presents GAs (Genetic Algorithms) to solve redundancy allocation in series-parallel systems. To apply the GAs to this problem, we propose a genetic representation, the method for initial population construction, evaluation and genetic operators. Especially, to improve the performance of GAs, we develop heuristic operators (heuristic crossover, heuristic mutation) using the reliability-resource information of the chromosome. Experiments are carried out to evaluate the performance of the proposed algorithm. The performance comparison between the proposed algorithm and a pervious method shows that our approach is more efficient.

• 한국신뢰성학회:학술대회논문집
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• 한국신뢰성학회 2000년도 추계학술대회
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• pp.399-404
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• 2000

Redundancy allocation problems have been considered at single-level systems and it may be the best policy in some specific situations, but not in general. With regards to reliability, it is most effective to allocate the lowest objects, because parallel-series systems are more reliable than series-parallel systems. However, the smaller and tower in the system an object is, the more time and accuracy are needed for duplicating it, and so, the cost can be decreased by using modular redundancy. Therefore, providing redundancy at high levels like as modules or subsystems, can be more economical than providing redundancy at low levels or duplicating components. In this paper, the problem in which redundancy is allocated at all level in a series system is addressed, a mixed integer nonlinear programming model is presented and genetic algorithm is proposed. An example illustrates the procedure.

• Journal of Advanced Marine Engineering and Technology
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• 제35권3호
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• pp.341-347
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• 2011

The redundancy allocation problem (RAP) is a famous NP.complete problem that has beenstudied in the system reliability area of ships and airplanes. Recently meta-heuristic techniques have been applied in this topic, for example, genetic algorithms, simulated annealing and tabu search. In particular, tabu search (TS) has emerged as an efficient algorithmic approach for the series-parallel RAP. However, the quality of solutions found by TS depends on the initial solution. As a robust and efficient methodology for the series-parallel RAP, the hybrid metaheuristic (TSA) that is a interactive procedure between the TS and SA (simulated annealing) is developed in this paper. In the proposed algorithm, SA is used to find the diversified promising solutions so that TS can re-intensify search for the solutions obtained by the SA. We test the proposed TSA by the existing problems and compare it with the SA and TS algorithm. Computational results show that the TSA algorithm finds the global optimal solutions for all cases and outperforms the existing TS and SA in cases of 42 and 56 subsystems.

• 한국통신학회논문지
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• 제32권9A호
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• pp.898-904
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• 2007

MANETs(Mobile Ad-hoc Networks)에서는 네트워크에 참여하는 노드 간 통신을 위해 노드 식별 주소를 받아야 한다. 주소는 노드 자신 또는 다른 노드에 의해 생성되며, 이 주소가 사용 가능한 유일한 주소인지 검사하기 위해 주소 중복 검사(Duplicate Address Detection)를 수행한다. 하지만 이런 방법은 MANETs의 확장성이 떨어지는 문제가 발생한다. 본 논문은 MANETs에서 이동 노드들을 그룹으로 묶어 그룹 단위로 관리 할 수 있고 제어 메시지의 교환 부하 감소 및 이동성 관리가 용이해지는 클러스터의 특성을 주소할당 프로토콜에 적용하여 주소 중복 검사에 소요되는 시간을 최소화하고 확장성 문제를 해결하는 방법을 제안한다. 본 논문에서 제안한 방법은 시뮬레이션을 통한 결과 MANETConf[2]보다 노드 수 증가에 따라 우수한 성능을 보여준다.

• 한국신뢰성학회지:신뢰성응용연구
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• 제17권4호
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• pp.332-342
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• 2017

Purpose: In this study, the lifetime distribution of a k-out-of-n system with heterogeneous components is suggested as Markov model, and the time-to-failure (TTF) distribution of each component is considered as phase-type distribution (PHD). Furthermore, based on the model, a redundancy allocation problem with a mix of components (RAPMC) is proposed. Methods: The lifetime distribution model for the system is formulated by the structured Markov chain. From the model, the various information on the system lifetime can be ascertained by the matrix-analytic (or-geometric) method. Conclusion: By the generalization of TTF distribution (PHD) and the consideration of heterogeneous components, the lifetime distribution model can delineate many real systems and be exploited for developing system operation policies such as preventive maintenance, warranty. Moreover, the effectiveness of the proposed RAPMC is verified by numerical experiments. That is, under the equivalent design conditions, it presented a system with higher reliability than RAP without component mixing (RAPCM).

• 한국항공우주학회지
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• 제32권10호
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• pp.60-66
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• 2004

위성의 자세제어 시스템의 신뢰도를 높이기 위하여 여유 추력기를 장착하는데, 이를 활용하기 위해서 효율적인 관리 방안이 요구된다. 본 논문에서는 조종력 할당 기법을 위성의 여유 추력기 관리 방안으로 제안한다. 조종력 할당 기법은 최척화 기법을 이용해서 원하는 모멘트 입력을 구현하는 여유 조종력을 결정하는 방법이다. 여기에서는 여유 추력기에 대한 할당 문제는 선형 프로그래밍 문제로 표현할 수 있음을 보인다. 또한 수치예제를 통해서 제안한 방법이 기존의 방법에 비해 효율적임을 보인다.

• 한국국방경영분석학회지
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• 제24권2호
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• pp.130-145
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• 1998

This paper is concerned with the optimization of a system reliability. Two kinds of the reliability model for optimal allocation of parallel redundancy are considered. The algorithm for solving the optimal redundancy problem is proposed by the use of dynamic programming(DP) method. The problem is approached with a standard DP formulation and the DP algorithm is applied to the model and then the optimal solution is found by the backtracking method. The method is applicable to the models having no constraints or having a cost constraint subject to a specified minimum requirement of the system reliability. A consequence of this study is that the developed computer program package are implemental for the optimization of the system reliability.

• Journal of the Korean Statistical Society
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• 제35권2호
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• pp.157-166
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• 2006

This research proposes an efficient Monte Carlo algorithm for computing error probability in high performance digital communication st stems. It characterizes special features of the problem and suggests an importance sampling algorithm specially designed to handle the problem. It uses a shifted exponential density as the importance sampling density, and shows an adaptive way of choosing the rate and the origin of the shifted exponential density. Instead of equal allocation, an intelligent allocation of the samples is proposed so that more samples are allocated to more important part of the error probability. The algorithm uses the nested feature of the error space and avoids redundancy in estimating the probability. The algorithm is applied to an example data set and shows a great improvement in accuracy of the error probability estimation.