Integrating resource early warning in buffer management for project schedule risk control using BP neural networks

Abstract

Buffer management is an effective way for projects to cope with schedule risk and variability. However, current research on buffer management methods mainly focuses on optimizing time buffer sizing, overlooking the impact of resource buffer as an early warning mechanism for improving the efficiency and accuracy of project buffer management. In this study, we propose a method for determining the timing of resource buffer early warnings based on a BP neural network. First, BP neural network theory is introduced into the field of critical chain buffer management, analyzing its application in the early warning processes of project resource buffer and its predictive capabilities. Subsequently, the BP neural network is used to dynamically forecast actual execution deviations of activity resources by utilizing project risk variations and buffer deviation data related to resource execution. Based on these dynamic deviation predictions, a model for determining the timing of resource buffer early warnings is developed. The experimental results demonstrate that, compared to the traditional buffer setting method, the proposed approach reduces the project duration by 19.81% and lowers the project cost by 17.59%, achieving optimization of both project duration and cost without compromising the project completion probability.