Scalable ML Methods to Optimize KPIs in Real-World Manufacturing Processes
Abstract
The goal of this work is to develop novel methods to solve the semiconductor fab scheduling problem. The problem can be modeled as a flexible job-shop with large instances and specific constraints related to special machine and job characteristics. To investigate the problem, we develop a tool to simulate small to large-scale instances of the problem. Using the simulator, we aim to develop new dispatching strategies using genetic programming and reinforcement learning.
Cite
Text
Kovács. "Scalable ML Methods to Optimize KPIs in Real-World Manufacturing Processes." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/831Markdown
[Kovács. "Scalable ML Methods to Optimize KPIs in Real-World Manufacturing Processes." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/kovacs2022ijcai-scalable/) doi:10.24963/IJCAI.2022/831BibTeX
@inproceedings{kovacs2022ijcai-scalable,
title = {{Scalable ML Methods to Optimize KPIs in Real-World Manufacturing Processes}},
author = {Kovács, Benjamin},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {5857-5858},
doi = {10.24963/IJCAI.2022/831},
url = {https://mlanthology.org/ijcai/2022/kovacs2022ijcai-scalable/}
}