Evaluating the planning efficiency for repetitive construction projects using Monte Carlo simulation technique

Abstract

Efficient planning and scheduling are critical for the success of repetitive construction projects, particularly highway infrastructure, which underpins economic growth in developing regions. Traditional scheduling methods often rely heavily on planner experience, limiting their ability to manage uncertainties and resource fluctuations in large-scale projects. This study proposes a Monte Carlo simulation-based framework to enhance planning efficiency by systematically modeling activity prioritization, resource allocation, and schedule optimization. Eighteen hypothetical project cases were analyzed under varying conditions to capture a wide range of uncertainties. The results demonstrated substantial improvements in project duration and resource utilization efficiency compared to conventional methods. Validation using three real-world highway projects in Egypt confirmed the framework's practical applicability, achieving efficiency improvements of up to 80%. This research offers a data-driven, adaptable approach to repetitive project planning, providing planners with a robust tool to mitigate uncertainties and optimize project outcomes.