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    A nearest neighbour extension to project duration forecasting with artificial intelligence

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    Publication type
    Vlerick strategic journal article
    Author
    Wauters, Mathieu
    Vanhoucke, Mario
    Publication Year
    2017
    Journal
    European Journal of Operational Research
    Publication Volume
    259
    Publication Issue
    3
    Publication Begin page
    1097
    Publication End page
    1111
    
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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.
    Keyword
    Project Management, Artificial Intelligence, Earned Value Management, Prediction
    Knowledge Domain/Industry
    Operations & Supply Chain Management
    DOI
    10.1016/j.ejor.2016.11.018
    URI
    http://hdl.handle.net/20.500.12127/5706
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.ejor.2016.11.018
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