Abstract: efficiency，slow response，and resource waste caused by high customization，multiple constraint coupling，and frequent dynamic disturbances-this study proposes a multi-objective intelligent scheduling algorithm that integrates dynamic priority rules，elastic constraint modeling，and a rush order disturbance handling mechanism. By combining an adaptive genetic algorithm with a rolling horizon optimization framework，a dynamic scheduling model tailored to the characteristics of the valve industry is constructed. Simulation and validation results show that the proposed algorithm significantly reduces the average order scheduling cycle time by 18.7%，compresses the response time for urgent rush orders to within 15 minutes，increases the overall equipment effectiveness (OEE) to 92.3%，and lowers the rate of energy re-consumption by 26.5%，thereby providing a quantifiable，efficient，agile，and green scheduling solution for the valve industry. The developed dynamic multi-objective optimization framework and hybrid solution strategy offer valuable insights and serve as an important reference for discrete manufacturing.