A Hybrid Intelligent Decision Support System for Spare Parts Inventory Control using Neural Network and Gene Algorithms Approach

Y. Zeng, L. Wang, T. Chen, and Y. Lu (PRC)


Spare parts, criticality class, artificial neural network, gene algorithms, intelligent decision support system


This paper presents a web & knowledge-based intelligent decision support system (IDSS) for spare parts inventory control 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 inventory control IDSS to obtain reasonable replenishment parameters that can be helpful for reducing of total inventory holding costs. The proposed IDSS was successful in decreasing inventories holding costs significantly by modifying the unreasonable purchase applications while maintaining the target service level.

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