Semiconductor manufacturing is widely recognized as one of the most complex production processes today, with a great deal of batch processing equipment scheduling problems. At the same time, more and more research and efforts have been made to optimize the process of decision making in scheduling batch processing machines. Based on previous studies that often combine machine learning algorithms with practical production applications, this paper provides a new approach to the scheduling process using an advanced genetic algorithm. Specifically, this paper offers an algorithm concerning both productivity and efficiency, provides the specific steps of the algorithm. Finally, a few possible future directions for the algorithm are discussed.
Paper
Full text
Scheduling Optimization for Batch Processing Machines Using Advanced Genetic Algorithm
Semantic Scholar · Engineering · 2021
Abstract
Semiconductor manufacturing is widely recognized as one of the most complex production processes today, with a great deal of batch processing equipment scheduling problems. At the same time, more and more research and efforts have been made to optimize the process of decision making in scheduling batch processing machines. Based on previous studies that often combine machine learning algorithms with practical production applications, this paper provides a new approach to the scheduling process using an advanced genetic algorithm. Specifically, this paper offers an algorithm concerning both productivity and efficiency, provides the specific steps of the algorithm. Finally, a few possible future directions for the algorithm are discussed.