@inproceedings{442083924ae04a1a8bc07c83e6017dfb,
title = "A Customizable Simulator for Artificial Intelligence Research to Schedule Semiconductor Fabs",
abstract = "Optimal scheduling of semiconductor fabs is a huge challenge due to the problem scale and complexity. New dispatching strategies are usually developed and tested using simulators of different fidelity levels. This work presents a scalable, open-source tool for simulating factories up to real-world size, aiming to support the research into new scheduling algorithms from prototyping to large-scale experiments. The simulator comes with a declarative environment definition framework and is out of the box usable with existing reinforcement learning methods, priority-based rules, or evolutionary algorithms. We verify our tool on large-scale public instances and provide proof-of-concept demonstrations of the reinforcement learning interface's usage.",
keywords = "benchmarking, gym environment, reinforcement learning, scheduling, semiconductor manufacturing, simulation",
author = "Benjamin Kovacs and Pierre Tassel and Ramsha Ali and Mohammed El-Kholany and Martin Gebser and Georg Seidel",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 33rd Annual SEMI Advanced Semiconductor Manufacturing Conference : ASMC 2022, ASMC 2022 ; Conference date: 02-05-2022 Through 05-05-2022",
year = "2022",
doi = "10.1109/ASMC54647.2022.9792520",
language = "English",
series = "ASMC (Advanced Semiconductor Manufacturing Conference) Proceedings",
publisher = "Institute of Electrical and Electronics Engineers",
booktitle = "2022 33rd Annual SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2022",
address = "United States",
}