Optimizing Construction Project Scheduling and Resource Allocation Using Machine Learning Techniques

Abstract

The construction industry faces persistent challenges in project delivery, with approximately 70% of projects experiencing schedule delays and cost overruns that significantly impact profitability and stakeholder satisfaction. Traditional project management approaches often rely on deterministic scheduling methods and historical experience, which fail to adequately account for the complex interdependencies and uncertainties inherent in construction projects. This research presents a comprehensive machine learning framework for optimizing construction project scheduling and resource allocation to minimize delays, reduce cost overruns, and improve overall project performance. A novel clustering-based resource optimization component is developed using K-means algorithm to identify similar project profiles and establish optimal resource allocation patterns. This approach enables construction managers to benchmark resource requirements against comparable projects and identify potential efficiency improvements. The methodology addresses key performance indicators including schedule variance, cost overruns, quality scores, and resource utilization rates to provide a holistic view of project performance.

Introduction

The global construction industry, worth over $12 trillion annually, faces persistent challenges in project delivery, including frequent delays, budget overruns, and quality issues, with around 70% of projects delayed and 60% over budget. Despite technological progress in other sectors, construction continues to rely on outdated project management methods that struggle with increasing project complexity, coordination issues, and external uncertainties like weather or regulatory changes.

Although AI and machine learning (ML) show promise for optimizing construction processes, adoption remains limited. Most existing studies focus narrowly on individual aspects like cost estimation or scheduling, lacking integrated, actionable frameworks for holistic project optimization.

This research addresses that gap by developing a comprehensive ML-based framework for predicting project duration and cost, and optimizing resource allocation. Key contributions include:

1. Ensemble models (e.g., Gradient Boosting, Random Forest) for duration and cost prediction with construction-specific feature engineering.

2. A clustering-based approach for identifying resource optimization patterns.

3. Visualization tools for translating complex ML outputs into actionable insights.

4. Analysis of relationships between project features and outcomes to inform best practices.

Methodology

• Created a dataset of 500 synthetic projects across residential, commercial, industrial, and infrastructure sectors.

• Applied Random Forest, Gradient Boosting, and K-means clustering.

• Incorporated novel features like resource density and project complexity, along with external factors such as weather and location.

• Employed multi-objective optimization to balance cost, schedule, and quality.

Key Findings

• Industrial projects dominate the dataset, but all sectors are well-represented, ensuring model generalizability.

• A strong positive correlation exists between duration and cost, with complexity as a major cost driver.

• Duration prediction achieved high accuracy (R² = 0.984 with Gradient Boosting), while cost prediction underperformed (R² ≈ 0.3 or lower), highlighting its volatility.

• Feature importance showed that project size and labor density are the strongest predictors of duration—more than raw complexity or total workforce.

• Ratio-based features (e.g., worker per sqft) are more effective than absolute counts for predictive modeling.

Conclusion

This research presents a comprehensive machine learning framework for optimizing construction project scheduling and resource allocation, demonstrating the significant potential for data-driven approaches to address persistent challenges in construction project management. The study successfully developed and validated predictive models that achieve exceptional accuracy for duration prediction while revealing important insights about the complexity of cost forecasting in construction environments. The analysis of 500 synthetic construction projects across four major sectors revealed several critical insights that advance our understanding of construction project optimization. The machine learning models demonstrated remarkable performance in duration prediction, with the Gradient Boosting algorithm achieving an R² score of 0.984, indicating that project timelines can be predicted with high accuracy using properly engineered features. This exceptional predictive capability validates the hypothesis that construction duration is systematically related to project characteristics and resource allocation patterns, providing a strong foundation for schedule optimization strategies. Feature importance analysis revealed that project size and resource density metrics are the primary drivers of project duration, with size_sqft and worker_per_sqft accounting for the majority of predictive power. This finding emphasizes the critical importance of spatial planning and optimal resource density in construction project management, suggesting that efficiency gains can be achieved through better workforce allocation rather than simply increasing absolute resource quantities. The dominance of engineered ratio-based features over raw project attributes validates the feature engineering approach and provides actionable insights for construction managers. The study also uncovered a strong inverse relationship between schedule performance and quality outcomes, demonstrating that projects maintaining schedule discipline consistently achieve higher quality scores. This finding challenges traditional project management assumptions about the inevitable trade-offs between time, cost, and quality, suggesting that well-managed projects can simultaneously optimize multiple performance dimensions. The cost overlay analysis revealed that adequate resource investment enables projects to achieve both schedule adherence and quality excellence, indicating that underfunding projects often leads to cascading performance failures.

References

[1] Zhou, J., Love, P.E., Wang, X., Teo, K.L. and Irani, Z., 2013. A review of methods and algorithms for optimizing construction scheduling. Journal of the Operational Research Society, 64(8), pp.1091-1105. [2] Karshenas, S. and Haber, D., 1990. Economic optimization of construction project scheduling. Construction Management and Economics, 8(2), pp.135-146. [3] Rogalska, M., Bo?ejko, W. and Hejducki, Z., 2008. Time/cost optimization using hybrid evolutionary algorithm in construction project scheduling. Automation in Construction, 18(1), pp.24-31. [4] Dasovi?, B., Gali?, M. and Klanšek, U., 2020. A survey on integration of optimization and project management tools for sustainable construction scheduling. Sustainability, 12(8), p.3405. [5] Lin, J.C.W., Lv, Q., Yu, D., Srivastava, G. and Chen, C.H., 2022. Optimized scheduling of resource-constraints in projects for smart construction. Information Processing & Management, 59(5), p.103005. [6] Mishra, A., Miloudi, A., Sefene, E.M., Arroussi, C., Chekalil, I. and Muthanna, B.G.N., 2025. Hybrid deep learning model to predict the ultimate tensile strength of friction stir welded joints. Engineering Applications of Artificial Intelligence, 154, p.111001. [7] Qin, J., Hu, F., Liu, Y., Witherell, P., Wang, C.C., Rosen, D.W., Simpson, T.W., Lu, Y. and Tang, Q., 2022. Research and application of machine learning for additive manufacturing. Additive Manufacturing, 52, p.102691. [8] Ng, W.L., Goh, G.L., Goh, G.D., Ten, J.S.J. and Yeong, W.Y., 2024. Progress and opportunities for machine learning in materials and processes of additive manufacturing. Advanced Materials, 36(34), p.2310006. [9] Jin, Z., Zhang, Z., Demir, K. and Gu, G.X., 2020. Machine learning for advanced additive manufacturing. Matter, 3(5), pp.1541-1556. [10] Xu, Y., Zhou, Y., Sekula, P. and Ding, L., 2021. Machine learning in construction: From shallow to deep learning. Developments in the built environment, 6, p.100045. [11] Gondia, A., Siam, A., El-Dakhakhni, W. and Nassar, A.H., 2020. Machine learning algorithms for construction projects delay risk prediction. Journal of Construction Engineering and Management, 146(1), p.04019085.

Copyright

Copyright © 2025 Aman Raj Singh, Dr. Sourabh Singh, Dr. Deepak Prajapat. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.