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A comparative study of Artificial Intelligence methods for project duration forecasting

Mathieu Wauters (UGent) and Mario Vanhoucke (UGent)
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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

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Please use this url to cite or link to this publication:

Chicago
Wauters, Mathieu, and Mario Vanhoucke. 2016. “A Comparative Study of Artificial Intelligence Methods for Project Duration Forecasting.” Expert Systems with Applications 46: 249–261.
APA
Wauters, Mathieu, & Vanhoucke, M. (2016). A comparative study of Artificial Intelligence methods for project duration forecasting. EXPERT SYSTEMS WITH APPLICATIONS, 46, 249–261.
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.
MLA
Wauters, Mathieu, and Mario Vanhoucke. “A Comparative Study of Artificial Intelligence Methods for Project Duration Forecasting.” EXPERT SYSTEMS WITH APPLICATIONS 46 (2016): 249–261. Print.
@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},
  keyword      = {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://dx.doi.org/10.1016/j.eswa.2015.10.008},
  volume       = {46},
  year         = {2016},
}

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