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Authors: Vladimir Samsonov 1 ; Marco Kemmerling 1 ; Maren Paegert 1 ; Daniel Lütticke 1 ; Frederick Sauermann 2 ; Andreas Gützlaff 2 ; Günther Schuh 2 and Tobias Meisen 3

Affiliations: 1 Institute of Information Management in Mechanical Engineering, RWTH Aachen University, Aachen, Germany ; 2 Laboratory for Machine Tools and Production Engineering WZL, RWTH Aachen University, Aachen, Germany ; 3 Chair of Technologies and Management of Digital Transformation, University of Wuppertal, Wuppertal, Germany

Keyword(s): Manufacturing Control, Production Scheduling, Job Shop Scheduling, Deep Reinforcement Learning, Combinatorial Optimization.

Abstract: Computing solutions to job shop problems is a particularly challenging task due to the computational hardness of the underlying optimization problem as well as the often dynamic nature of given environments. To address such scheduling problems in a more flexible way, such that changing circumstances can be accommodated, we propose a reinforcement learning approach to solve job shop problems. As part of our approach, we propose a new reward shaping and devise a novel action space, from which a reinforcement learning agent can sample actions, which is independent of the job shop problem size. A number of experiments demonstrate that our approach outperforms commonly used scheduling heuristics with regard to the quality of the generated solutions. We further show that, once trained, the time required to compute solutions using our methodology increases less sharply as the problem size grows than exact solution methods making it especially suitable for online manufacturing control tasks.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Samsonov, V.; Kemmerling, M.; Paegert, M.; Lütticke, D.; Sauermann, F.; Gützlaff, A.; Schuh, G. and Meisen, T. (2021). Manufacturing Control in Job Shop Environments with Reinforcement Learning. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8; ISSN 2184-433X, pages 589-597. DOI: 10.5220/0010202405890597

@conference{icaart21,
author={Vladimir Samsonov. and Marco Kemmerling. and Maren Paegert. and Daniel Lütticke. and Frederick Sauermann. and Andreas Gützlaff. and Günther Schuh. and Tobias Meisen.},
title={Manufacturing Control in Job Shop Environments with Reinforcement Learning},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={589-597},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010202405890597},
isbn={978-989-758-484-8},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Manufacturing Control in Job Shop Environments with Reinforcement Learning
SN - 978-989-758-484-8
IS - 2184-433X
AU - Samsonov, V.
AU - Kemmerling, M.
AU - Paegert, M.
AU - Lütticke, D.
AU - Sauermann, F.
AU - Gützlaff, A.
AU - Schuh, G.
AU - Meisen, T.
PY - 2021
SP - 589
EP - 597
DO - 10.5220/0010202405890597