New publication:

A Nearest Neighbour extension to project duration forecasting with Artificial Intelligence

In our most recent publication in the European Journal of Operational Research, the well-known Earned Value Management techniques has been extended to a Nearest Neighbour search to improve the accuracy and stability of forecasts. The publication is a follow-up study of previous artificial intelligence studies on the same topic, as discussed earlier.

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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Wauters, M., and Vanhoucke, M., 2017, "A nearest neighbour extension to project duration forecasting with artificial intelligence", European Journal of Operational Research, 259(3), 1097–1111 (doi: 10.1016/j.ejor.2016.11.018)