ABSTRACT
This study tackles the challenge of accurately predicting the earliest possible completion time for civil engineering projects, a crucial aspect of construction project management. Traditional scheduling methods often struggle to address the uncertainties and risks these projects face, such as unpredictable weather, fluctuating resource availability, and unexpected site conditions, all of which can lead to delays. To overcome these limitations, this research introduces a simulation model that integrates Monte Carlo simulation and agent-based modeling. The model simulates various scenarios, accounting for both deterministic and stochastic variables to better reflect the complexities of real-world projects. By running multiple simulations, the model generates a probability distribution of potential completion times, providing a more nuanced understanding of risks and possible delays. The study’s methodology involved gathering and analyzing historical project data from diverse sources to ensure that the model is based on practical, real-world conditions. This data was crucial in fine-tuning the model to reflect the specific characteristics of civil engineering projects. The model’s predictions were validated by comparing them with actual project outcomes, demonstrating its improved accuracy and reliability compared to traditional methods. In addition to offering more precise forecasts, the simulation model acts as a valuable decision-support tool for project managers. It allows for dynamic adjustments to resource allocation and scheduling, helping to mitigate potential delays and optimize timelines. Overall, the findings contribute to the broader literature on construction project management by providing an innovative, integrated approach to schedule optimization that better addresses the uncertainties inherent in complex civil engineering projects. This new approach not only enhances project outcomes but also supports more efficient and effective decision-making for stakeholders at all levels.
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