TY - GEN
T1 - A Dynamic Fit-Out Scheduling Framework for Digital Twin-Enabled Modular Integrated Construction
AU - Chen, Qiqi
AU - Ding, Jiyuchen
AU - Sun, Mingyue
AU - Zhao, Zhiheng
AU - Huang, George Q.
N1 - Publisher Copyright:
© IFIP International Federation for Information Processing 2024.
PY - 2024/9
Y1 - 2024/9
N2 - Modular Integrated Construction (MiC) is a novel construction method for its approved space-saving and time-saving for the construction industry. Fit-out construction, as the last essential process of MiC module assembly, significantly affects the delivery of MiC modules for its complexity and flexibility in operations. Emerging technologies, such as the Internet-of-Things (IoT) and Digital Twin (DT), capture real-time fit-out operation data and worker information, which reshape the fit-out job scheduling problem. This study proposes a dynamic fit-out scheduling framework by integrating job scheduling and worker assignment for DT-enabled MiC. First, this study defines the workflow of fit-out operations and the fit-out scheduling problem in MiC. The new fit-out scheduling problem considers job scheduling and worker assignment simultaneously. Second, this work designs a DT-enabled framework for cyber-physical operational synchronisation in MiC regarding workers, operations and jobs. Third, this work implements a dynamic fit-out scheduling method based on Deep Reinforcement Learning. This work intends to conduct experimental analysis using real-life case data. This work aims to provide a real-time data-driven solution for fit-out scheduling problems in MiC.
AB - Modular Integrated Construction (MiC) is a novel construction method for its approved space-saving and time-saving for the construction industry. Fit-out construction, as the last essential process of MiC module assembly, significantly affects the delivery of MiC modules for its complexity and flexibility in operations. Emerging technologies, such as the Internet-of-Things (IoT) and Digital Twin (DT), capture real-time fit-out operation data and worker information, which reshape the fit-out job scheduling problem. This study proposes a dynamic fit-out scheduling framework by integrating job scheduling and worker assignment for DT-enabled MiC. First, this study defines the workflow of fit-out operations and the fit-out scheduling problem in MiC. The new fit-out scheduling problem considers job scheduling and worker assignment simultaneously. Second, this work designs a DT-enabled framework for cyber-physical operational synchronisation in MiC regarding workers, operations and jobs. Third, this work implements a dynamic fit-out scheduling method based on Deep Reinforcement Learning. This work intends to conduct experimental analysis using real-life case data. This work aims to provide a real-time data-driven solution for fit-out scheduling problems in MiC.
KW - Digital Twin
KW - Fit-out Construction
KW - Modular Integrated Construction
KW - Scheduling
KW - Worker Assignment
UR - https://www.scopus.com/pages/publications/85204538805
U2 - 10.1007/978-3-031-71645-4_12
DO - 10.1007/978-3-031-71645-4_12
M3 - Conference article published in proceeding or book
AN - SCOPUS:85204538805
SN - 9783031716447
T3 - IFIP Advances in Information and Communication Technology
SP - 168
EP - 179
BT - Advances in Production Management Systems. Production Management Systems for Volatile, Uncertain, Complex, and Ambiguous Environments - 43rd IFIP WG 5.7 International Conference, APMS 2024, Proceedings
A2 - Thürer, Matthias
A2 - Riedel, Ralph
A2 - von Cieminski, Gregor
A2 - Romero, David
PB - Springer Science and Business Media Deutschland GmbH
T2 - 43rd IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2024
Y2 - 8 September 2024 through 12 September 2024
ER -