A Bayesian Neural Network Approach to Synthesising Data for Supply Chain Optimisation and Simulation Modelling
Access Status
Fulltext not available
Embargo Lift Date
2027-05-05
Date
2022Supervisor
Louis Caccetta
Yong Wu
Type
Thesis
Award
PhD
Metadata
Show full item recordFaculty
Science and Engineering
School
Department of Mathematics & Statistics
Collection
Abstract
The Bayesian learning model developed in this research addressed a need for technology by which complex industry datasets may be synthesised and represents a fundamentally novel approach to this problem than those previously explored in literature. This approach enables the characterisation of complex stochastic phenomena from historical and current observations under the influence of industry forecasts, to yield datasets that are sufficiently lifelike for use in optimisation and simulation modelling, when real data is scarce.