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A nearest neighbour extension to project duration forecasting with artificial intelligence
Wauters, Mathieu Vanhoucke, Mario
Wauters, Mathieu
Vanhoucke, Mario
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Publication Type
Journal article with impact factor
Editor
Supervisor
Publication Year
2017
Journal
European Journal of Operational Research
Book
Publication Volume
259
Publication Issue
3
Publication Begin page
1097
Publication End page
1111
Publication NUmber of pages
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Abstract
In this paper, we provide a Nearest Neighbour based extension for project control forecasting with Earned Value Management. The k-Nearest Neighbour method is employed as a predictor and to reduce the size of a training set containing more similar observations. An Artificial Intelligence (AI) method then makes use of the reduced training set to predict the real duration of a project. Additionally, we report on the forecasting stability of the various AI methods and their hybrid Nearest Neighbour counterparts. A large computer experiment is set up to assess the forecasting accuracy and stability of the existing and newly proposed methods. The experiments indicate that the Nearest Neighbour technique yields the best stability results and is able to improve the AI methods when the training set is similar or not equal to the test set. Sensitivity checks vary the amount of historical data and number of neighbours, leading to the conclusion that having more historical data, from which the a relevant subset can be selected by means of the proposed Nearest Neighbour technique, is preferential.
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Keywords
Project Management, Artificial Intelligence, Earned Value Management, Prediction