An Intelligent Decision Support System for Spare Parts Joint Replenishment

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 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 predefined target service level