Data for Article "An Uncertainty-embedded Scheduling with Monte Carlo Simulation for Construction Projects"

Project scheduling plays a critical role in construction management, influencing cost, resource allocation, and timely delivery. Traditional methods such as the Critical Path Method (CPM) and Program Evaluation Review Technique (PERT) assume deterministic durations and static dependencies, making them inadequate for handling real-world uncertainties like weather disruptions, supply delays, and safety incidents. Recent research has shifted toward probabilistic and simulation-based approaches, integrating metaheuristic algorithms, Building Information Modelling (BIM), and multi-objective optimization to address dynamic conditions and resource constraints. This study proposes a Monte Carlo-based framework for uncertainty-embedded scheduling, which generates multiple schedule scenarios by sampling from probability distributions of task durations and start times. The model evaluates schedule reliability and cost profiles under user-defined confidence intervals, offering a more resilient planning tool compared to conventional methods. A case study demonstrates that while the proposed approach increases project duration and cost compared to CPM, it provides robust schedules without additional resources and supports proactive risk management.