https://doi.org/10.24928/2026/0246
A proactive digital twin framework for dynamic pull scheduling in modular construction factories
Nima Moghimi1, Sahar H. Shamaee2, Qipei Mei3, Vicente A. Gonzalez4 & Farook Hamzeh5
1Corresponding Author, Ph.D. Student, Department of Civil and Environmental Engineering, University of Alberta, Canada, [email protected], orcid.org/0009-0008-4733-12762Graduate Research Assistant, Department of Civil and Environmental Engineering, University of Alberta, Canada, [email protected]
3Assistant Professor, Department of Civil and Environmental Engineering, University of Alberta, Canada, [email protected]
4Hal Kvisle Professor and Tier 1 Canada Research Chair in Digital Lean Construction, Infrastructure Human Tech (IHT) Lab, Strategic Projects Insight Centre in Engineering (SPICE), Department of Civil and Environmental Engineering, Faculty of Engineering, University of Alberta, Canada, [email protected], orcid.org/0000-0003-3408-3863
5Professor, Department of Civil and Environmental Engineering, University of Alberta, Canada, [email protected], orcid.org/0000-0002-3986-9534
Abstract
Achieving continuous flow in modular construction is challenging due to high-mix variability, where traditional static methods often fail, leading to excess Work-in-Process (WIP) and flow stagnation. This study proposes a Digital Twin framework utilizing a novel "Offline Learning, Online Control" architecture. The methodology employs Simulation-Based Optimization to derive optimal control policies offline, which are synthesized into a lightweight surrogate model. This model drives an autonomous Scheduler Agent that acts as a virtual pacemaker, regulating real-time flow to shift production from a 'push' to a dynamic 'pull' strategy. Specifically, the agent monitors the cumulative count of finished units at critical downstream constraints, throttling upstream releases until the cumulative count of finished units from the current project surpasses a dynamically calculated threshold. To validate this approach, a case study was conducted on a wall panel assembly line in Edmonton, Canada. For this specific implementation, a Genetic Algorithm was used to train a linear regression surrogate policy against stochastic demand scenarios. Experimental results demonstrate a 38.5% reduction in average Cycle Time (Lead Time) compared to the facility’s baseline practice. These findings confirm that integrating simulation-based learning with real-time surrogate control successfully stabilizes flow efficiency, minimizes WIP accumulation, and prevents gridlock in constrained manufacturing environments.
Keywords
Lean construction, pull scheduling, modular construction, digital twin, simulation-based optimization
Files
Reference
Download: BibTeX | RIS Format
Reference in APA 7th edition format:
Moghimi, N., Shamaee, S. H., Mei, Q., Gonzalez, V. A. & Hamzeh, F.. (2026). A proactive digital twin framework for dynamic pull scheduling in modular construction factories. In Hamzeh, F., Poshdar, M., & Garcia-Lopez,, N. P. (Eds.), Proceedings of the 34th Annual Conference of the International Group for Lean Construction (IGLC 34) (pp. 837–848). https://doi.org/10.24928/2026/0246
Shortened reference for use in IGLC papers:
Moghimi, N., Shamaee, S. H., Mei, Q., Gonzalez, V. A. & Hamzeh, F.. (2026). A proactive digital twin framework for dynamic pull scheduling in modular construction factories. IGLC34. https://doi.org/10.24928/2026/0246
Founded in 1993, the IGLC is an international network of researchers from practice and academia in architecture, engineering, and construction (AEC) who feel that the practice, education, and research of the AEC industry have to be radically renewed in order to respond to the global challenges ahead.
2015 © All Rights Reserved.