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This paper presents artificial neural networks (ANNs) for the criticality class evaluating of spare parts in a power plant. Two learning methods are utilized in the ANNs, namely back propagation (BP) and BP-particle swarm optimization (BP-PSO). The reliability of the models is tested by comparing their classification ability with a hold-out sample and an external data set. The results show that both...
Th is paper presents a Web-based intelligent decision support system (JDSS) for spare parts joint replenishment in a nuclear power plant. In this study, we integrate the artificial neural network and gene algorithms-based spare parts criticality class identifying system to confirm the target service level, and the Web-based joint replenishment IDSS to obtain reasonable inventory control parameters...
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