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Professor John McCall


Predicting service levels using neural networks. (2017)
Conference Proceeding
AINSLIE, R., MCCALL, J., SHAKYA, S. and OWUSU, G. 2017. Predicting service levels using neural networks. In Bramer, M. and Petridis, M. (eds.) Artificial intelliegence XXXIV: proceedings of the 37th SGAI International innovative techniques and applications of artifical intelligence conference 2017 (AI 2017), 12-14 December 2017, Cambridge, UK. Lecture notes in computer science, 10630. Cham: Springer [online], pages 411-416. Available from: https://doi.org/10.1007/978-3-319-71078-5_35

In this paper we present a method to predict service levels in utility companies, giving them advanced visibility of expected service outcomes and helping them to ensure adherence to service level agreements made to their clients. Service level adher... Read More about Predicting service levels using neural networks..

Simulation and optimisation of the separation process in offshore oil and gas platforms. (2017)
Thesis
VELESHKI, S. 2017. Simulation and optimisation of the separation process in offshore oil and gas platforms. Robert Gordon University, MRes thesis.

Hydrocarbon separation in offshore oil and gas platforms is the process that transforms extracted crude oil into transportable oil and gas. Temperatures and pressures of the separation system can be adjusted to modify the separation of the hydrocarbo... Read More about Simulation and optimisation of the separation process in offshore oil and gas platforms..

Estimation of distribution algorithms for the multi-mode resource constrained project scheduling problem. (2017)
Conference Proceeding
AYODELE, M., MCCALL, J. and REGNIER-COUDERT, O. 2017. Estimation of distribution algorithms for the multi-mode resource constrained project scheduling problem. In Proceedings of the 2017 IEEE congress on evolutionary computation (CEC 2017), 5-8 June 2017, San Sebastian, Spain. New York: IEEE [online], article number 7969491, pages 1579-1586. Available from: https://doi.org/10.1109/CEC.2017.7969491

Multi-Mode Resource Constrained Project Problem (MRCPSP) is a multi-component problem which combines two interacting sub-problems; activity scheduling and mode assignment. Multi-component problems have been of research interest to the evolutionary co... Read More about Estimation of distribution algorithms for the multi-mode resource constrained project scheduling problem..

A random key based estimation of distribution algorithm for the permutation flowshop scheduling problem. (2017)
Conference Proceeding
AYODELE, M., MCCALL, J., REGNIER-COUDERT, O. and BOWIE, L. 2017. A random key based estimation of distribution algorithm for the permutation flowshop scheduling problem. In Proceedings of the 2017 IEEE congress on evolutionary computation (CEC 2017), 5-8 June 2017, San Sebastian, Spain. New York: IEEE [online], article number 7969591, pages 2364-2371. Available from: https://doi.org/10.1109/CEC.2017.7969591

Random Key (RK) is an alternative representation for permutation problems that enables application of techniques generally used for continuous optimisation. Although the benefit of RKs to permutation optimisation has been shown, its use within Estima... Read More about A random key based estimation of distribution algorithm for the permutation flowshop scheduling problem..

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