IJCAI 2022poster0 citations
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.
Machine Learning (ML): GeneralPlanning, Routing, and Scheduling (PRS): General
BibTeX
@inproceedings{ijcai2022p831,
title = {Scalable ML Methods to Optimize KPIs in Real-World Manufacturing Processes},
author = {Kovács, Benjamin},
booktitle = {Proceedings of the Thirty-First International Joint Conference on
Artificial Intelligence, {IJCAI-22}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Lud De Raedt},
pages = {5857--5858},
year = {2022},
month = {7},
note = {Doctoral Consortium},
doi = {10.24963/ijcai.2022/831},
url = {https://doi.org/10.24963/ijcai.2022/831},
}