Smart and robust critical chain scheduling for construction projects using buffer extraction

In the field of construction project planning and management, scheduling is a crucial challenge. Construction projects are always undertaken in an uncertain environment and typically include numerous tasks. Uncertainties may raise the risk of delays, schedule mismatches between actual and desired schedules, and schedule management challenges to support site operations. However, the critical path method (CPM), a commonly employed approach in construction projects, frequently fails to cope with uncertain environments, resulting in project delays. Few studies have attempted to apply critical chain scheduling (CCS) to construction projects, as it is a viable alternative to developing a robust plan utilizing a buffer. They did not, however, reflect the diverse variety of construction activities or the scheduling approaches that are employed in practice. Previous studies adopting CCS for construction projects were insufficient to prove the practical and smart applicability of this method, as they did not account for the degree of uncertainty and the scheduling procedures common to construction projects. Therefore, we conduct a simulation while also applying CCS to construction projects in this study, taking into account a variety of practices. We also assess and analyze schedule performance outcomes in terms of robustness, referring to the smart capacity to deal with uncertainty. To achieve these goals, practitioners responded to a questionnaire to investigate the practices, and a Monte-Carlo simulation model was developed to evaluate schedule performance outcomes. As a result of research, it was proven that when applying CCS to construction projects, numerous uncertainties in construction activity and construction practices should be considered. When reflecting construction practices, we found that the probability of completing the project on time was stable and that buffer consumption patterns fluctuated according to the degree of uncertainty.