IGLC.net
EXPORT DATE: 19 June 2026

@CONFERENCE{Shidqi2026,
author={Shidqi, Arif H. N. and Wibowo, Mochamad A. and Fatchur, Adian and Ramdhan, Alfain },
editor={Hamzeh, Farook and Poshdar, Mani and Garcia-Lopez,, Nelly P. },
title={Prediction of logistics performance and allocation optimization in construction projects using Catboost, LightGBM, and XGboost based on lean construction principles}, 
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={227-237},
url={http://www.iglc.net/papers/details/2554},
doi={10.24928/2026/0265},
affiliation={Doctoral Candidate, Department of Civil Engineering, Universitas Diponegoro, Semarang, Indonesia,  haidarsanath@gmail.com, orcid.org/0009-0005-1957-2820  ; Professor, Department of Civil Engineering, Universitas Diponegoro, Semarang, Indonesia,  agung.wibowo@ft.undip.ac.id, orcid.org/0000-0002-5434-9107   ; Professor, Department of Computer Engineering, Universitas Diponegoro, Semarang, Indonesia,  adian@ce.undip.ac.id, orcid.org/0000-0002-1921-9358   ; Bachelor’s, Department of Civil Engineering, Universitas Diponegoro, Semarang, Indonesia,  alfainnaga@gmail.com, orcid.org/0009-0003-7544-9507  },
abstract={Supply Chain Management performance and strategic optimization allocation are essential determinants of construction project productivity and operational efficiency. Delays in material distribution, misallocation of workforce, and underused equipment often result in workflow interruptions and waste, which contradict the Lean Construction ideals of continuous flow and waste minimization. While the use of machine learning in construction management has expanded in recent years, research focusing on predictive frameworks for logistics and resource utilization grounded in lean methodology remains scarce. This study introduces a machine learning framework designed to forecast logistics outcomes and resource allocation efficiency in construction projects through the application of CatBoost, LightGBM, and XGBoost algorithms. The dataset comprises variables such as material delivery attributes, workforce capacity, task complexity, and project operating conditions, whereas logistics performance metrics and resource utilization indicators form the prediction targets. Model evaluation employs suitable regression and classification criteria. The experimental results reveal that boosting based techniques can effectively model intricate relationships within construction logistics data. Among the algorithms tested, CatBoost performs most effectively, particularly in managing categorical attributes. The proposed framework enables data driven, proactive decisions to optimize resource deployment and support lean oriented project execution by minimizing idle time, delays, and inefficiencies. },
author_keywords={Construction logistic, lean construction, machine learning. },
address={Singapore, Singapore },
issn={2789-0015 },
publisher={ },
language={English},
document_type={Conference Paper},
source={IGLC},
}