Artificial Intelligence for projects

New publication:

A comparative study of Artificial Intelligence methods for project duration forecasting

In our most recent publication in the Expert Systems with Applications, a comparison between various machine learning methods has been made to improve the accuracy of project duration forecasting. It is a follow-up study of the previous study using Support Vector Machines.

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.

Click on the picture to download the PDF from the journal website

Cite as: Wauters, M. and Vanhoucke, M., 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).

See also our related article in the Automation in Construction.