A Bayesian Neural Network Approach to Synthesising Data for Supply Chain Optimisation and Simulation Modelling
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 compl...
| Main Author: | |
|---|---|
| Format: | Thesis |
| Published: |
Curtin University
2022
|
| Online Access: | http://hdl.handle.net/20.500.11937/97703 |
| _version_ | 1848766307704504320 |
|---|---|
| author | Taco Arana, Herbert Isaac |
| author_facet | Taco Arana, Herbert Isaac |
| author_sort | Taco Arana, Herbert Isaac |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:49:04Z |
| format | Thesis |
| id | curtin-20.500.11937-97703 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:49:04Z |
| publishDate | 2022 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-977032025-05-09T00:42:11Z A Bayesian Neural Network Approach to Synthesising Data for Supply Chain Optimisation and Simulation Modelling Taco Arana, Herbert Isaac 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. 2022 Thesis http://hdl.handle.net/20.500.11937/97703 Curtin University restricted |
| spellingShingle | Taco Arana, Herbert Isaac A Bayesian Neural Network Approach to Synthesising Data for Supply Chain Optimisation and Simulation Modelling |
| title | A Bayesian Neural Network Approach to
Synthesising Data for Supply Chain
Optimisation and Simulation Modelling |
| title_full | A Bayesian Neural Network Approach to
Synthesising Data for Supply Chain
Optimisation and Simulation Modelling |
| title_fullStr | A Bayesian Neural Network Approach to
Synthesising Data for Supply Chain
Optimisation and Simulation Modelling |
| title_full_unstemmed | A Bayesian Neural Network Approach to
Synthesising Data for Supply Chain
Optimisation and Simulation Modelling |
| title_short | A Bayesian Neural Network Approach to
Synthesising Data for Supply Chain
Optimisation and Simulation Modelling |
| title_sort | bayesian neural network approach to
synthesising data for supply chain
optimisation and simulation modelling |
| url | http://hdl.handle.net/20.500.11937/97703 |