Using Schedule Risk Analysis with resource constraints for project control

Schedule Risk Analysis (SRA) has shown to provide reliable activity sensitivity information for taking corrective actions during project control. More precisely, by selecting a small subset of activities with high sensitivity values for taking corrective actions, the project outcome can be improved. In resource constrained projects, disrupted activities can affect both their successors as well as other activities when resource conflicts are induced. Since SRA focuses solely on the project network to determine the sensitivity of activities, the traditional SRA metrics do not accurately reflect the activity sensitivity for resource constrained projects. In this paper, the traditional SRA metrics are extended for resource constrained projects, and a novel resource-based sensitivity metric is introduced (RC-SRA metrics). A computational experiment is conducted to investigate the ability of the RC-SRA metrics to identify activities with higher sensitivity values. In addition, two activity selection strategies, defined as the normal strategy and sequential strategy, are designed to select activities for taking corrective actions. Further, two types of corrective actions are proposed to reduce the activity duration or resource demand in case of delays, respectively. Finally, the impact of dynamically updating the RC-SRA metrics during project execution is examined. The computational results show that the normal activity selection strategy is recommended for serial projects, while the sequential strategy is preferred for parallel projects. The results also indicate that reducing the activity durations performs better than reducing the resource demand of activities. Finally, it is shown that updating the RC-SRA metrics dynamically during project execution improves the efficiency of the corrective action taking process. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

Schedule Risk Analysis (SRA) provides reliable activity sensitivity information for corrective actions during project control. This study extends traditional SRA metrics with RC-SRA metrics for resource constrained projects, and introduces new resource-based sensitivity metrics. Computational experiments show that the RC-SRA metrics can identify activities with higher sensitivity values. Activity selection strategies and corrective actions are proposed, with findings suggesting that reducing activity durations is more effective than reducing resource demand, especially when updated dynamically during project execution.