Semiconductor manufacturing (SM) system is one of the most complicated hybrid processes involved continuously variable dynamical systems and discrete event dynamical systems. The optimization and scheduling of semiconductor fabrication has long been a hot research direction in automation. Bottleneck is the key factor to a SM system, which seriously influences the throughput rate, cycle time, time-delivery rate, etc. Efficient prediction for the bottleneck of a SM system provides the best support for the consequent scheduling. Because categorical data (product types, releasing strategies) and numerical data (work in process, processing time, utilization rate, buffer length, etc.) have significant effect on bottleneck, an improved adaptive network-based fuzzy inference system (ANFIS) was adopted in this study to predict bottleneck since conventional neural network-based methods accommodate only numerical inputs. In this improved ANFIS, the contribution of categorical inputs to firing strength is reflected through a transformation matrix. In order to tackle high-dimensional inputs, reduce the number of fuzzy rules and obtain high prediction accuracy, a fuzzy c-means method combining binary tree linear division method was applied to identify the initial structure of fuzzy inference system. According to the experimental results, the main-bottleneck and sub-bottleneck of SM system can be predicted accurately with the proposed method.
柔性制造系统(flexible manufacturing system,FMS)调度问题属于NP难题,针对该问题利用赋时库所Petri网(timed place Petri net,TPPN)对FMS进行建模,在此基础上,提出了利用改进的嵌套分区(nested partitions,NP)算法对建立的TPPN模型进行优化调度.采用来自半导体制造的末端测试系统数据做数值试验,结果表明,基于TPPN模型的改进嵌套分区优化调度算法较其它常用算法具有更好的平衡指数,可有效降低最大完工时间.
