@inproceedings{Zeng2026_2561,
  author = {Zeng, Xianghui and Jiang, Liu and Cheng, Zhiyuan and Chen, Yu},
  editor = {Hamzeh, Farook and Poshdar, Mani and Garcia-Lopez,, Nelly P. and Gan, Vincent},
  title = {Lean post-typhoon recovery: multi-agent look-ahead and constraints for the Last Planner System},
  booktitle = {Proceedings of the 34th Annual Conference of the International Group for Lean Construction (IGLC 34)},
  year = {2026},
  pages = {250--259},
  address = {Singapore, Singapore},
  issn = {2789-0015},
  doi = {10.24928/2026/0273},
  url = {https://www.iglc.net/papers/details/2561},
  abstract = {Post-typhoon recovery involves high-variability work in which plan reliability is frequently undermined by late discovery of constraints, fragmented information, and rework. Rapid post-typhoon building recovery requires integrating heterogeneous evidence and converting it into actionable and auditable plans. This paper proposes a Lean Construction informed, knowledge-driven framework that combines a multi-modal knowledge graph (MMKG) with multi-agent orchestration for end-to-end building damage assessment and recovery planning, aligned with the Last Planner System (LPS) through look-ahead planning, make-ready checks, and explicit constraint management. The MMKG captures emergency management criteria, typhoon evolution and exposure status, building-level damage evidence, and reusable post-disaster recovery templates. The approach uses the MMKG as a shared and traceable evidence base, enabling multi-agent orchestration to generate a constraint log, assign make-ready status, and support weekly work plan release under explicit rule-based conditions. Typhoon Yagi is used as an illustrative proof-of-concept case to demonstrate how heterogeneous post-typhoon evidence can be translated into LPS-oriented planning artifacts. The results indicate that the framework is feasible for generating traceable planning outputs under multi-source uncertainty; however, quantitative field-scale validation and execution stage performance assessment remain future work.},
  keywords = {Last Planner System, multi-agent, knowledge-driven, multi-modal knowledge graph, Typhoon disaster.},
}