Intelligent Control of Construction Manufacturing Processes using Deep Reinforcement Learning

Ian Flood, Paris Flood

2022

Abstract

This paper is concerned with the development and evaluation of a reinforcement learning approach to the control of factory based construction operations. The unique challenges associated with controlling construction work is first discussed: uneven and uncertain demand, high customization, the need to fabricate work to order, and a lack of opportunity to stockpile work. This is followed by a review of computational approaches to this problem, specifically those based on heuristics and machine learning. A description is then given of a model of a factory for producing precast reinforced concrete components, and a proposed reinforcement learning strategy for training a neural network based agent to control this system. The performance of this agent is compared to that of rule-of-thumb and random policies for a series of protracted simulation production runs. The reinforcement learning method was found to be promising, outperforming the two competing strategies for much of the time. This is significant given that there is high potential for improvement of the method. The paper concludes with an indication of areas of proposed future research.

Paper Citation

in Harvard Style

Flood I. and Flood P. (2022). Intelligent Control of Construction Manufacturing Processes using Deep Reinforcement Learning. In Proceedings of the 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH, ISBN 978-989-758-578-4, pages 112-122. DOI: 10.5220/0011309600003274

in Bibtex Style

@conference{simultech22,
author={Ian Flood and Paris Flood},
title={Intelligent Control of Construction Manufacturing Processes using Deep Reinforcement Learning},
booktitle={Proceedings of the 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,},
year={2022},
pages={112-122},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011309600003274},
isbn={978-989-758-578-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,
TI - Intelligent Control of Construction Manufacturing Processes using Deep Reinforcement Learning
SN - 978-989-758-578-4
AU - Flood I.
AU - Flood P.
PY - 2022
SP - 112
EP - 122
DO - 10.5220/0011309600003274