Duration forecasting in resource constrained projects: A hybrid risk model combining complexity indicators with sensitivity measures
Altuncan, Izel Ünsal Vanhoucke, Mario
Altuncan, Izel Ünsal
Vanhoucke, Mario
Publication Type
Journal article with impact factor
Editor
Supervisor
Publication Year
2025-09
Journal
European Journal of Operational Research
Book
Publication Volume
325
Publication Issue
2
Publication Begin page
511
Publication End page
527
Publication Number of pages
Collections
Abstract
This study combines complexity measures from the project scheduling literature and sensitivity measures from the risk analysis literature to improve project duration forecasts in resource constrained projects. A hybrid risk model is proposed incorporating project network measures, resource-related indicators, and risk sensitivity metrics. The hybrid risk model is then used for forecasting the duration of unseen projects. The study contributes to the existing literature by integrating newly proposed activity sensitivity metrics and network and resource related indicators in project forecasting. Additionally, it conducts a large-scale experiment to compare the accuracy of the hybrid risk model against benchmark methods, including Monte Carlo simulations and relevant machine learning algorithms. The results show that inclusion of resource-related variables within the hybrid risk model significantly improves the accuracy, validating recently proposed metrics. The hybrid risk model outperforms most of the benchmark methods in high-uncertainty projects, emphasizing the importance of accurately estimating the flexibility in activity start times. Furthermore, the hybrid risk model of this paper is particularly effective for parallel projects, demonstrating a better performance under various uncertainty and flexibility conditions. Finally, the results are validated using empirical project data.
Research Projects
Organizational Units
Journal Issue
Keywords
4005 Civil Engineering, 40 Engineering, Bioengineering, Machine Learning and Artificial Intelligence