Optimizing Construction Project Scheduling and Resource Allocation Using Machine Learning Techniques

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.