Browsing Articles by Subject "Schedule Risk Analysis"
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Performance comparison of activity sensitivity metrics in schedule risk analysisIn Schedule Risk Analysis (SRA), activity sensitivity metrics measure the importance of activities in a project schedule. Highly sensitive activities are those more likely to increase project duration variability and/or cause project duration extensions. Several activity sensitivity metrics have been proposed over the years, but a comparison of all of them has never been made. This has made it difficult to know which metrics perform better and under what circumstances. In this paper, an extensive comparison of all relevant SRA activity sensitivity metrics is performed using a set of 4100 artificial projects. Unlike previous studies, the comparison framework is decoupled from corrective actions (e.g. activity crashing) which allows the merits of each metric to be assessed individually. Additionally, a new metric that performs better for overall sensitivity ranking is proposed. Results show that most sensitivity metrics do not perform well unless they are applied iteratively (the sensitivity of the remaining scheduled activities has to be recalculated whenever the duration variability of at least one activity has been restricted). However, if applied iteratively, most metrics can enhance project monitoring and control, while significantly shortening project duration.
Using schedule risk analysis with resource constraints for project controlSchedule 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.