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    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
    Authors
    Taco Arana, Herbert Isaac
    Date
    2022
    Supervisor
    Louis Caccetta
    Yong Wu
    Type
    Thesis
    Award
    PhD
    
    Metadata
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    Faculty
    Science and Engineering
    School
    Department of Mathematics & Statistics
    URI
    http://hdl.handle.net/20.500.11937/97703
    Collection
    • Curtin Theses
    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.

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