.png){kind=icon}
返回
Automated construction scheduling using deep reinforcement learning with valid action sampling
.png){kind=icon}
OA
AI
DOI：10.1016/j.autcon.2024.105622.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The growing demand for buildings and infrastructures requires optimal construction schedules under real-world complexities. This paper presents an automated scheduling optimization method for large construction projects, considering real-world constraints and the need for rescheduling. A Deep Reinforcement Learning (DRL) model with a Valid Action Sampling (VAS) mechanism is proposed to optimize schedules. The method integrates a Graph Convolutional Network (GCN) for feature extraction and includes a reward shaping mechanism to expedite convergence. The proposed method outperforms traditional methods with reduced project duration and runtime in both scheduling and rescheduling cases. This advancement benefits construction managers seeking efficient and flexible project management. The findings inspire future research into broader and more practical construction scheduling solutions utilizing DRL.
Keyword:
Construction scheduling
Reinforcement learning
Action masking
Reward shaping
Graph neural network
Off-site construction
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
11.5
论文数:
6.3K
被引数:
4.2W
机构
引用论文
MASS SOCIOGENIC ILLNESS BY PROXY: PARENTALLY REPORTED EPIDEMIC IN AN ELEMENTARY SCHOOL.png)
The Lancet
The LancetIF0
PREAI
分享收藏分享收藏Study of bi-directional buck-boost converter topologies for application in electrical vehicle motor drives应用于电动汽车电机驱动的双向buck-boost变换器拓扑研究
PREAI
分享收藏A heuristic rule adaptive selection approach for multi-work package project scheduling problem多工作包项目调度问题的启发式规则自适应选择方法
PREAI
分享收藏
查看更多内容