This study aims to find a way to improve the performance of building projects by offering an advanced buffer sizing strategy for critical chain project management (CCPM). The goal is to shorten the time it takes to complete a project and make it more cost-effective through a multi-objective optimization framework.
A multi-objective stochastic programming model is developed that incorporates probabilistic delay scenarios, resource constraints and decision-making criteria. The model extends the conventional root square error method to better deal with unstable schedules. The proposed methodology is tested on a real-world institutional construction project from among the construction projects between 2020 and 2024 in Pakistan, using both scenario-based optimization and Monte Carlo simulations.
The results reveal that the suggested method greatly increases project performance compared to the conventional RSEM and cut-and-paste methods. It achieved a 95.2% probability of on-time completion while lowering the risk of overestimating buffers and is quite similar to expert assessments but with proper buffer allocation technique.
This study is based on a single case application. Future research should extend the model to diverse project types, larger datasets and international contexts to enhance generalizability and test its performance under different scheduling environments.
The model offers a flexible tool for project managers to make more accurate buffer decisions under uncertainty. It aids in efficient resource planning, better time control and improved alignment with stakeholder expectations during construction scheduling.
Improved project delivery reliability enhances trust in public infrastructure programs, reduces disruption to communities and promotes efficient use of public funds in infrastructure development.
This study introduces a novel buffer allocation strategy that adapts to varying levels of project uncertainty. By integrating stochastic programming and multi-criteria decision-making, it improves CCPM's capacity to provide more realistic and economical scheduling solutions when there is uncertainty.
