摘要
半导体生产制造系统具有大规模、工艺繁杂、随机性大、可重入等显著特点。以半导体最终测试阶段批处理调度为基础,把学习-遗忘效应应用到典型半导体批调度问题中,构建基于学习-遗忘效应的批调度模型。分别结合调度问题和调度模型对双层算法(粒子群算法&萤火虫算法)进行设计,通过仿真实验检验了双层算法在求解具有学习遗忘效应的批调度模型方面的可行性和有效性,并对比分析以最大完工时间为优化目标的实验结果,探讨学习遗忘效应对半导体批调度问题的影响程度,对实际半导体生产具有重要指导意义。
Semiconductor manufacturing system has significant characteristics such as large scale, multifarious craft, randomness and reentrant. Based on the processing batch scheduling, which is the final testing phase of semiconductors, we apply the learning and forgetting effects to the semiconductor scheduling which is the typical batch scheduling problem, and build a batch scheduling model with the learning and forgetting effects. Combining with the scheduling problem and scheduling model respectively, we propose a two-level algorithm(particle swarm optimization algorithm & firefly algorithm), and through the simulation experiments we verify the feasibility and effectiveness of the two-level algorithm in solving the model of the batch scheduling with the learning and forgetting effects. And we discuss the influence of the learning and forgetting effects on semiconductor batch scheduling comparing with the results of the optimal makespan in the experiment, which is of important guiding significance for actual semiconductor production.
作者
叶春明
侯丰龙
赵静
YE Chun-ming;HOU Feng-long;ZHAO Jing(School of Business, University of Shanghai for Science & Technology, Shanghai 200093, China)
出处
《运筹与管理》
CSSCI
CSCD
北大核心
2019年第7期192-199,共8页
Operations Research and Management Science
基金
国家自然科学基金资助项目(71840003)
上海理工大学科技发展资助项目(2018KJFZ043)
关键词
半导体批调度
学习效应
遗忘效应
调度模型
双层算法
semiconductor batch scheduling
learning effect
forgetting effects
scheduling model
two-level algorithm
