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Truck and trailer scheduling in a real world, dynamic and heterogeneous context. (2016)
Journal Article
REGNIER-COUDERT, O., MCCALL, J., AYODELE, M. and ANDERSON, S. 2016. Truck and trailer scheduling in a real world, dynamic and heterogeneous context. Transportation research, part E: logistics and transportation review [online], 93, pages 389-408. Available from: https://doi.org/10.1016/j.tre.2016.06.010

We present a new variant of the Vehicle Routing Problem based on a real industrial scenario. This VRP is dynamic and heavily constrained and uses time-windows, a heterogeneous vehicle fleet and multiple types of job. A constructive solver is develope... Read More about Truck and trailer scheduling in a real world, dynamic and heterogeneous context..

Machine learning for improved pathological staging of prostate cancer: a performance comparison on a range of classifiers. (2011)
Journal Article
REGNIER-COUDERT, O., MCCALL, J., LOTHIAN, R., LAM, T., MCCLINTON, S. and N'DOW, J. 2012. Machine learning for improved pathological staging of prostate cancer: a performance comparison on a range of classifiers. Artificial intelligence in medicine [online], 55(1), pages 25-35. Available from: https://doi.org/10.1016/j.artmed.2011.11.003

Objectives: Prediction of prostate cancer pathological stage is an essential step in a patient's pathway. It determines the treatment that will be applied further. In current practice, urologists use the pathological stage predictions provided in Par... Read More about Machine learning for improved pathological staging of prostate cancer: a performance comparison on a range of classifiers..