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Authors: Hikaru Ikeda ; Hiroyuki Nakagawa and Tatsuhiro Tsuchiya

Affiliation: Institute of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, Japan

Keyword(s): Deep Reinforcement Learning, Machine Learning, Layout Design, Facility Layout Problem, Analytic Hierarchy Process.

Abstract: Facility layout designing aims to deploy functional objects in appropriate locations within the logistics facilities and production facilities. The designer’s ability to create a layout is a major factor in the quality of the layout because they need to satisfy functional requirements like lead time, relations among functional objects to deploy and material handling costs. In this paper, a deep reinforcement learning (RL) based automatic layout design system is developed. Deep Q-Networt (DQN) is introduced to solve facility layout problem (FLP) by the adaptability of RL with the expression of deep neural networks. We apply the developed system to the existing FLP and compare the layout result with conventional RL based system. Consequently, the performance improvement was confirmed in terms of the relations among units in the created layout comparing to the RL based system.

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Paper citation in several formats:
Ikeda, H., Nakagawa, H. and Tsuchiya, T. (2023). Automatic Facility Layout Design System Using Deep Reinforcement Learning. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-623-1; ISSN 2184-433X, SciTePress, pages 221-230. DOI: 10.5220/0011678500003393

@conference{icaart23,
author={Hikaru Ikeda and Hiroyuki Nakagawa and Tatsuhiro Tsuchiya},
title={Automatic Facility Layout Design System Using Deep Reinforcement Learning},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2023},
pages={221-230},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011678500003393},
isbn={978-989-758-623-1},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Automatic Facility Layout Design System Using Deep Reinforcement Learning
SN - 978-989-758-623-1
IS - 2184-433X
AU - Ikeda, H.
AU - Nakagawa, H.
AU - Tsuchiya, T.
PY - 2023
SP - 221
EP - 230
DO - 10.5220/0011678500003393
PB - SciTePress

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