Automatic selection of the best performing control point approach for project control with resource constraints

During project execution, the actual project progress shows deviations from the baseline schedule due to uncertainty. To complete the project timely, project monitoring is performed at discrete control points to identify project opportunities/problems and take possible corrective actions. These control points affect the quality of project monitoring and corrective actions, but little guidance is available on identifying situations where the control points pay off the most in terms of project duration. This paper proposes new control point approaches considering the risk, the complexity of the network, and subnetwork information to determine the timing of project monitoring and action taking. Moreover, new parameters are proposed to model more realistic project characteristics. Subsequently, a classification model is developed to select the best performing control point approach given project characteristics. An extensive computational experiment is conducted on a set of 3,810 artificial projects with diverse project characteristics to evaluate the performance of the classification model and further validate it on empirical project data. The computational results indicate that the classification model outperforms the average performance of any proposed control point approaches. The results also show that the resource variability that indicates the resource usage deviations between project activities is the primary driver for detecting the best control point approach for projects with resource constraints. • The risk, network, and subnetwork control point approaches are proposed. • New project parameters are introduced to model realistic project features. • A classification model is built to select the best approach given project features. • The classification model outperforms any single proposed control point approach. • Resource variability is the main driver for detecting the best approach.