Open AccessScheduling Optimization for Batch Processing Machines Using Advanced Genetic Algorithm
Author(s) -
Yimeng Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1883/1/012016
Subject(s) - computer science , scheduling (production processes) , genetic algorithm scheduling , batch processing , fair share scheduling , dynamic priority scheduling , two level scheduling , industrial engineering , distributed computing , algorithm , mathematical optimization , engineering , schedule , mathematics , programming language , operating system
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.