• Journal of Korea Society of Industrial Information Systems
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• v.20 no.4
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• pp.75-87
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• 2015

A redundant structure typically consists of primary component and standby component taking over the function of the primary component when the primary component fails. In this research, we consider a redundant structure in which a standby component can take over the function of more than one primary component when primary components fail. And we assume that the system has multi-state according to the states of components while all components have two states. This system is called as the multi-state redundant system with a multi-functional standby component. This type of redundant structure is frequently adapted by the system such as an aircraft in which the weight is an important design factor. In this paper, we propose new reliability model for this multi-state redundant system with a multi-functional standby component in order for evaluating the reliability of the system. Under the assumption that all components have constant failure rate, we evaluate the reliability of the system by applying Markov analysis method. And we investigate the effect of the multi-functional standby component by comparing reliabilities of the parallel system with multi-functional standby component and a simple parallel system and a parallel system with redundant structure.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.354-359
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• 2006

The analysis of large-scale water resources systems is often complicated by the presence of multiple reservoirs and diversions, the uncertainty of unregulated inflows and demands, and conflicting objectives. Reinforcement learning is presented herein as a new approach to solving the challenging problem of stochastic optimization of multi-reservoir systems. The Q-Learning method, one of the reinforcement learning algorithms, is used for generating integrated monthly operation rules for the Keum River basin in Korea. The Q-Learning model is evaluated by comparing with implicit stochastic dynamic programming and sampling stochastic dynamic programming approaches. Evaluation of the stochastic basin-wide operational models considered several options relating to the choice of hydrologic state and discount factors as well as various stochastic dynamic programming models. The performance of Q-Learning model outperforms the other models in handling of uncertainty of inflows.