基于多树遗传编程的存储仓与组合设备集成系统优化OA

To address the complex scheduling problem of stocker-integrated cluster tools under frequent recipe switching in advanced semiconductor manufacturing,a multi-tree genetic programming method is proposed with the optimization objective of minimizing the makespan.Considering multiple practical constraints,inclu-ding recipe switching,chamber cleaning,stocker capacity,and robot-handling operations,several representa-tive operating scenarios are constructed to verify the effectiveness of the proposed method.The experimental framework adopts a dual-tree structure:one syntax tree determines the wafer lot entry sequence,while the other specifies the assignment strategy of processing chambers.The two syntax trees are jointly optimized through a cooperative co-evolution mechanism,achieving a favorable balance between search efficiency and so-lution quality.The proposed method is evaluated on three representative case studies and compared with manu-ally designed scheduling rules.The experimental results show that the proposed method demonstrates superior scheduling performance and robustness across different operating conditions,reducing the average makespan by approximately 25%-45%.