A comparative study of Artificial Intelligence methods for project duration forecasting
- Author
- Mathieu Wauters (UGent) and Mario Vanhoucke (UGent)
- Organization
- Abstract
- This paper presents five Artificial Intelligence (AI) methods to predict the final duration of a project. A methodology that involves Monte Carlo simulation, Principal Component Analysis and cross-validation is proposed and can be applied by academics and practitioners. The performance of the AI methods is assessed by means of a large and topologically diverse dataset and is benchmarked against the best performing Earned Value Management/Earned Schedule (EVM/ES) methods. The results show that the AI methods outperform the EVM/ES methods if the training and test sets are at least similar to one another. Additionally, the AI methods report excellent early and mid-stage forecasting results. A robustness experiment gradually increases the discrepancy between the training and test sets and demonstrates the limitations of the newly proposed AI methods.
- Keywords
- NETWORKS, SENSITIVITY, PREDICTION, MODEL, EARNED VALUE MANAGEMENT, Earned Value Management, Project management, INFORMATION, PERFORMANCE, REGRESSION, RANDOM FORESTS, SUPPORT VECTOR MACHINE, Artificial Intelligence, prediction
Downloads
-
(...).pdf
- full text
- |
- UGent only
- |
- |
- 561.05 KB
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-6976924
- MLA
- Wauters, Mathieu, and Mario Vanhoucke. “A Comparative Study of Artificial Intelligence Methods for Project Duration Forecasting.” EXPERT SYSTEMS WITH APPLICATIONS, vol. 46, 2016, pp. 249–61, doi:10.1016/j.eswa.2015.10.008.
- APA
- Wauters, M., & Vanhoucke, M. (2016). A comparative study of Artificial Intelligence methods for project duration forecasting. EXPERT SYSTEMS WITH APPLICATIONS, 46, 249–261. https://doi.org/10.1016/j.eswa.2015.10.008
- Chicago author-date
- Wauters, Mathieu, and Mario Vanhoucke. 2016. “A Comparative Study of Artificial Intelligence Methods for Project Duration Forecasting.” EXPERT SYSTEMS WITH APPLICATIONS 46: 249–61. https://doi.org/10.1016/j.eswa.2015.10.008.
- Chicago author-date (all authors)
- Wauters, Mathieu, and Mario Vanhoucke. 2016. “A Comparative Study of Artificial Intelligence Methods for Project Duration Forecasting.” EXPERT SYSTEMS WITH APPLICATIONS 46: 249–261. doi:10.1016/j.eswa.2015.10.008.
- Vancouver
- 1.Wauters M, Vanhoucke M. A comparative study of Artificial Intelligence methods for project duration forecasting. EXPERT SYSTEMS WITH APPLICATIONS. 2016;46:249–61.
- IEEE
- [1]M. Wauters and M. Vanhoucke, “A comparative study of Artificial Intelligence methods for project duration forecasting,” EXPERT SYSTEMS WITH APPLICATIONS, vol. 46, pp. 249–261, 2016.
@article{6976924, abstract = {{This paper presents five Artificial Intelligence (AI) methods to predict the final duration of a project. A methodology that involves Monte Carlo simulation, Principal Component Analysis and cross-validation is proposed and can be applied by academics and practitioners. The performance of the AI methods is assessed by means of a large and topologically diverse dataset and is benchmarked against the best performing Earned Value Management/Earned Schedule (EVM/ES) methods. The results show that the AI methods outperform the EVM/ES methods if the training and test sets are at least similar to one another. Additionally, the AI methods report excellent early and mid-stage forecasting results. A robustness experiment gradually increases the discrepancy between the training and test sets and demonstrates the limitations of the newly proposed AI methods.}}, author = {{Wauters, Mathieu and Vanhoucke, Mario}}, issn = {{0957-4174}}, journal = {{EXPERT SYSTEMS WITH APPLICATIONS}}, keywords = {{NETWORKS,SENSITIVITY,PREDICTION,MODEL,EARNED VALUE MANAGEMENT,Earned Value Management,Project management,INFORMATION,PERFORMANCE,REGRESSION,RANDOM FORESTS,SUPPORT VECTOR MACHINE,Artificial Intelligence,prediction}}, language = {{eng}}, pages = {{249--261}}, title = {{A comparative study of Artificial Intelligence methods for project duration forecasting}}, url = {{http://doi.org/10.1016/j.eswa.2015.10.008}}, volume = {{46}}, year = {{2016}}, }
- Altmetric
- View in Altmetric
- Web of Science
- Times cited: