Automatic Facility Layout Design System Using Deep Reinforcement Learning

Hikaru Ikeda, Hiroyuki Nakagawa, Tatsuhiro Tsuchiya

2023

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.

Paper Citation

in Harvard Style

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, pages 221-230. DOI: 10.5220/0011678500003393

in Bibtex Style

@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},
}

in EndNote Style

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
AU - Ikeda H.
AU - Nakagawa H.
AU - Tsuchiya T.
PY - 2023
SP - 221
EP - 230
DO - 10.5220/0011678500003393