Enterprise AI Analysis

Data-Driven Multi-Mode Time-Cost Trade-Off Optimization for Construction Project Scheduling Using LightGBM

Large infrastructure projects frequently experience schedule slippage and cost escalation; however, time-cost planning still relies on expert-assigned activity parameters that fail to reflect the variability induced by construction modes, resource supply, and on-site conditions. This study focuses on activity-level multi-mode time-cost trade-off planning and its dynamic correction during project execution. The proposed methodology is intended for project-level short-term operational scheduling and rolling re-scheduling within a finite project execution horizon, rather than long-term strategic or portfolio-level scheduling. A predict-optimize-update framework is proposed, where light gradient boosting machine (LightGBM) is employed to predict the duration and direct cost of activity-mode pairs using unified features extracted from BIM/IFC records, schedule-resource ledgers, and cost-settlement data, covering engineering quantities, mode and resource decisions, and contextual factors. These predicted parameters are then fed into a time-indexed bi-objective mixed-integer linear program (MILP), which minimizes both project makespan and total cost (including indirect cost) to generate an interpretable Pareto frontier via a weighted-sum approach. Meanwhile, real-time monitoring updates refresh the predictors and re-solve the remaining project network to ensure dynamic adaptability. Validated on a desensitized proprietary enterprise multi-source dataset comprising 25 completed infrastructure projects and 5258 activity-mode samples, the proposed method achieves a mean absolute error (MAE) of 2.7 days and a coefficient of determination (R2) of 0.89 for duration prediction, as well as an MAE of 7.4 × 104 CNY and an R2 of 0.91 for direct-cost prediction. The generated Pareto set exhibits a diminishing return trend: as the project duration is relaxed from 101 to 146 days, the total cost decreases from 45.10 to 40.27 million CNY. A weather-triggered update case demonstrates that the completion forecast is revised from 133 to 128 days, with the total cost reduced from 53.05 to 52.75 million CNY. This framework enables explainable schedule-cost co-control, thereby effectively aiding decision-making for the planning and control of large infrastructure projects.