The use of optimisation and simulation models by ore producers, to support critical strategic supply chain management decisions is becoming standard industry practice. The performance of such models is crucial for meeting global demand, whilst maximising the profitability of a supply chain across its lifespan. However, their performance also often hinges on inputs which require varying levels of user intervention. This increases the risk of introducing errors which can significantly impact the performance of a model. Typical examples of this may be observed in bulk material export shipping operations where, for certain supply chain models, the inputs required consists of ship stem data. To improve the predictive value of these models, users often intervene to modify the data to reflect that which is anticipated in the future. In this research, we explore the use of machine-learning to assist in the generation of semi-synthetic data. We present a hybrid optimisation-neural-network based decision support tool that builds on current methods employed by bulk material exporters and mitigates the risk to model-mediated decisions associated with user intervention.
PhD Candidate, Curtin University
Herbert is a PhD candidate at Curtin University currently working on machine learning algorithms for generating synthetic data sets for industrial modelling applications. Prior to his PhD candidature, Herbert was awarded a Master of Science (Industrial Engineering) with distinction from Curtin University. He recently completed an AMSI internship where he worked on an optimisation-based modelling support tool. Herbert has also worked in the pharmaceutical and biotech sectors and has over 7 years of experience in manufacturing process control and design.