IGLC.net
EXPORT DATE: 19 June 2026

@CONFERENCE{Qi2026,
author={Qi, Ruixuan and Xu, Jinying and Zheng, Ruiyan },
editor={Hamzeh, Farook and Poshdar, Mani and Garcia-Lopez,, Nelly P. },
title={A multi-agent data-to-model workflow for make-ready assessment}, 
journal={Proceedings of the 34th Annual Conference of the International Group for Lean Construction (IGLC 34)}, 
booktitle={Proceedings of the 34th Annual Conference of the International Group for Lean Construction (IGLC 34)}, 
year={2026},
pages={238-249},
url={http://www.iglc.net/papers/details/2555},
doi={10.24928/2026/0266},
affiliation={Graduate student, National University of Singapore, Singapore, e1538237@u.nus.edu ; Assistant Professor, Department of the Built Environment, National University of Singapore, Singapore; jinying.xu@nus.edu.sg, orcid.org/0000-0001-9589-6396 ; PhD student, Department of the Built Environment, National University of Singapore, Singapore; zhengruiyan@u.nus.edu, orcid.org/0009-0009-8421-0614 },
abstract={The Last Planner System (LPS) is widely recognized as an effective lean approach for improving workflow reliability. Although the theoretical framework of LPS has been well established, its practical implementation remains challenging in real projects. A major barrier lies in initiating and maintaining Lookahead planning, which is intended to connect phase scheduling with commitment planning. Current practice often shows gaps, including lack of detailed data required for Make-Ready assessment in Lookahead planning.
This paper proposes a multi-agent, data-to-model workflow that lowers the entry barrier to Lookahead planning by extracting project-specific Lookahead rules and readiness assessments directly from commonly available progress schedule data. Instead of training a generalized predictive model, the workflow coordinates specialized agents to sequentially normalize tasks, infer dependencies, reconstruct production states, and support readiness scoring with an interpretable learning component.
The workflow is demonstrated using real data from a high-rise building project. The case suggests that meaningful Make-Ready assessments can be derived from standard project schedule data. It may help practitioners reduce reliance on subjective judgment and fragmented information in Lookahead planning, while offering a practical method to push near-term work commitments further in LPS adoption. },
author_keywords={Last Planner System, lookahead planning, make-ready assessment, multi-agent systems, data-to-model workflow. },
address={Singapore, Singapore },
issn={2789-0015 },
publisher={ },
language={English},
document_type={Conference Paper},
source={IGLC},
}