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针对半导体生产线清洗—炉管区存在的Lot动态达到的并行批处理机优化调度问题,提出了嵌套分区差分进化算法,该算法充分利用嵌套分区算法的全局并行搜索的优势和差分进化算法较强的局部寻优能力。差分进化算法用来优化嵌套分区框架各可行域中抽样得到的样本群,使嵌套分区在选区阶段能更精确跟踪最有希望域,减少算法的回溯过程。通过仿真模型和实际生产线数据对该调度方案进行了比较验证,结果表明,所提算法较其他启发式算法能更有效降低总加权拖期交货损失。
In order to solve the problem of batch scheduling optimization of Lot in dynamic cleaning of the semiconductor production line, a nested partitioned differential evolution algorithm is proposed. This algorithm makes full use of the advantages of global parallel search in nested partitioning algorithm and the differential evolution algorithm Local optimization ability. The differential evolution algorithm is used to optimize the sample group sampled in each feasible region of the nested partition framework so that the nested partition can track the most promising domain more precisely during the selection phase and reduce the backtracking process of the algorithm. The simulation results show that the proposed algorithm can reduce the total weight of delayed delivery more effectively than other heuristics.