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A comparative study of Artificial Intelligence methods for project duration forecasting
Wauters, Mathieu Vanhoucke, Mario
Wauters, Mathieu
Vanhoucke, Mario
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Publication Type
Journal article with impact factor
Editor
Supervisor
Publication Year
2016
Journal
Expert Systems with Applications
Book
Publication Volume
46
Publication Issue
March
Publication Begin page
249
Publication End page
261
Publication Number of pages
Collections
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.
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Keywords
Project Management, Earned Value Management, Prediction, Artificial Intelligence