Duration forecasting in resource constrained projects: A hybrid risk model combining complexity indicators with sensitivity measures

Izel Ünsal Altuncan and Mario Vanhoucke

European Journal of Operational Research, 2025, vol. 325, issue 2, 329-343

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.

Keywords: Project management; Forecasting; Project scheduling; Risk models (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221725002097
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:325:y:2025:i:2:p:329-343

DOI: 10.1016/j.ejor.2025.03.012

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().