• Journal of Korean Society for Quality Management
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• v.32 no.4
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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.

• IE interfaces
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• v.23 no.2
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• pp.147-155
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• 2010

Reliability allocation is defined as a problem of determination of the reliability for subsystems and components to achieve target system reliability. The determination of both optimal component reliability and the number of component redundancy allowing mixed components to maximize the system reliability under resource constraints is called reliability-redundancy allocation problem(RAP). The main objective of this study is to suggest a mathematical programming model and a hybrid parallel genetic algorithm(HPGA) for reliability-redundancy allocation problem that decides both optimal component reliability and the number of component redundancy to maximize the system reliability under cost and weight constraints. The global optimal solutions of each example are obtained by using CPLEX 11.1. The component structure, reliability, cost, and weight were computed by using HPGA and compared the results of existing metaheuristic such as Genetic Algoritm(GA), Tabu Search(TS), Ant Colony Optimization(ACO), Immune Algorithm(IA) and also evaluated performance of HPGA. The result of suggested algorithm gives the same or better solutions when compared with existing algorithms, because the suggested algorithm could paratactically evolved by operating several sub-populations and improve solution through swap, 2-opt, and interchange processes. In order to calculate the improvement of reliability for existing studies and suggested algorithm, a maximum possible improvement(MPI) was applied in this study.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.8
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• pp.1004-1009
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• 2014

The redundancy allocation problem has usually considered only the component redundancy at the lowest-level for the enhancement of system reliability. A system can be functionally decomposed into system, module, and component levels. Modular redundancy can be more effective than component redundancy at the lowest-level because in modular systems, duplicating a module composed of several components can be easier, and requires less time and skill. We consider a multi-level redundancy allocation problem in which all cases of redundancy for system, module, and component levels are considered. A tabu search of memory-based mechanisms that balances intensification with diversification via the short-term and long-term memory is proposed for its solution. To the best of our knowledge, this is the first attempt to use a tabu search for this problem. Our tabu search algorithm is compared with the previous genetic algorithm for the problem on the new composed test problems as well as the benchmark problems from the literature. Computational results show that the proposed method outstandingly outperforms the genetic algorithm for almost all test problems.

• Journal of Korean Society for Quality Management
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• v.41 no.3
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• pp.413-421
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• 2013

Purpose: System availability and life cycle cost are often used to evaluate the system performance and is influenced by the operation and maintenance characteristic. In this paper, we propose the method to improve life cycle cost and satisfy the target availability through redundancy allocation. Methods: We consider the redundancy is available at all items in multi level system. Thus, we assume that sub-assembly, module, components can be duplicated. Simulation and genetic algorithm are employed to optimize redundancy allocation. Results: Target availability is higher, the life cycle cost is increased. In addition, the items for redundancy are selected at higher level in multi level system if target availability is higher. Conclusion: We could know that target availability affects the duplication number of items and the selection of redundancy items. For further study, we will consider new optimization algorithms to compare with the proposed GA algorithm and improve optimization performance.

• Journal of Applied Reliability
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• v.1 no.1
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• pp.1-8
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• 2001

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 lower 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 a genetic algorithm is proposed. An example illustrates the procedure.

• Journal of Korean Society for Quality Management
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• v.43 no.1
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• pp.31-42
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• 2015

Purpose: System consists of a lot of units with coherent function. In design phase, various units could be considered with the same function. In this study, we consider the alternative units with the same function and redundancy allocation to maximize system reliability in multi level system. Methods: The redundancy allocation problem with the alternative units in multi level system is formulated. Memetic algorithm(MA) is proposed to optimize the redundancy allocation problem. In addition, the performance of the proposed algorithm is explained by a numerical experiment. Results: MA showed better results than genetic algorithm(GA) and the convergence of the solutions in MA was also faster than GA. In addition, we could know from experiment that system reliability is increased and the chosen unit for redundancy allocation is changed if cost limit is increased. Conclusion: The chose unit for redundancy allocation is changed as resource constraints. It means we need to consider the alternative units in system design. In the future, we need to consider various problem related to redundancy allocation in multi level system and develop the better method to enhance search performance.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.9
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• pp.504-511
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• 2000

The reliability of system is to become a important concern in developed industry. The controller based on the reliability is so important position. PPC(Plant Protection Controller) is for plant protection and human life by fault detection and control action against the transient condition of plant. The protection system of the nuclear reactor and chemical reactor are representative of PPC. This paper presents analysis of PPC relaibility formal problem statement of optimal redundancy based on the reliability for PPC. And the problem is optimized by genetic algorithm, The genetic algorithms is useful algorithm in case of large searching complex gradient existence local minimum. The genetic algorithms is useful algorithm is case of large searching complex gradient existence local minimum. The ability and effectiveness of the proposed optimization is demonstrated by the target reliability of one channel. PPC. using the failure rate based on the MIL-HDBK-217

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.69.3-69
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• 2002

The reliability of system is to become a important concern in developed industry. The controller based on the reliability is so important position. A reliable system is for system protection and human life by fault detection and control action against the transient condition of system. The aerospace system , nuclear reactor and chemical reactor are representative of a reliable system. This paper presents analysis of reliable system reliability, formal problem statement of optimal redundancy based on the reliability for a reliable system. And the problem is optimized by genetic algorithm. The genetic algorithms is useful algorithm in case of...

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.1
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• pp.118-127
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• 2014

The configuration such as series, parallel and k-out-of-n of a repairable system directly affects its reliability. The maintenance strategy can also affect the overall performance of the system. The objective of this work is to investigate the possible trade-off between the configuration of a repairable k-out-of-n system and its maintenance strategy. The redundancy is considered to be the design decision variables, whereas the preventive maintenance period is considered to be the maintenance decision variables. The optimization model is used to minimize the overall life cycle cost associated with the system, considering constraint on reliability. Finally, genetic algorithm is used to find the optimal values for the decision variables. The result is compared with optimal values for considering redundancy and maintenance respectively.

• Proceedings of the Korean Reliability Society Conference
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• 2000.11a
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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.