Facility Layout Generation Using Hierarchical Reinforcement Learning

Shunsuke Furuta, Hiroyuki Nakagawa, Hiroyuki Nakagawa, Tatsuhiro Tsuchiya

2025

Abstract

Facility Layout Problem (FLP), which is an optimization problem aimed at determining the optimal placement of facilities within a specified site, faces limitations in existing methods that use genetic algorithms (GA) and metaheuristic approaches. These methods require accurately specifying constraints for facility placement, making them difficult to utilize effectively in environments with few skilled workers. In layout generation using reinforcement learning-based methods, the need to consider multiple requirements results in an expanded search space, which poses a challenge. In this study, we implemented a system that adopts hierarchical reinforcement learning and evaluated its performance by applying it to existing benchmark problems. As a result, we were able to confirm that the system could stably generate facility layouts that meet the given conditions while addressing the issues found in previous methods.

Paper Citation

in Harvard Style

Furuta S., Nakagawa H. and Tsuchiya T. (2025). Facility Layout Generation Using Hierarchical Reinforcement Learning. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 150-157. DOI: 10.5220/0013098200003890

in Bibtex Style

@conference{icaart25,
author={Shunsuke Furuta and Hiroyuki Nakagawa and Tatsuhiro Tsuchiya},
title={Facility Layout Generation Using Hierarchical Reinforcement Learning},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={150-157},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013098200003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Facility Layout Generation Using Hierarchical Reinforcement Learning
SN - 978-989-758-737-5
AU - Furuta S.
AU - Nakagawa H.
AU - Tsuchiya T.
PY - 2025
SP - 150
EP - 157
DO - 10.5220/0013098200003890
PB - SciTePress