Robust intelligent scenario planning for industrial systems
Uncertainty about the future significantly impacts on the planning capacities of organisations. Scenario planning provides such organisations with an opportunity to be aware of the consequences of their future plans. By developing plausible scenarios, scenario planning methodologies assist decision-makers to make systematic and effective decisions for the future. This research aims to review existing scenario planning methodologies and develop a new framework to overcome the shortcomings of previous methodologies. The new framework has two major phases: a ?scenario generation phase? and an ?intelligent robust optimisation phase?. The scenario generation phase creates future scenarios by applying fuzzy logic and Artificial Neural Network (ANN) concepts. With these concepts, it is possible to deal with qualitative data and also learn from expert data. The intelligent robust optimisation phase identifies the best strategic option which is suitable for working with the most probable scenarios. This second phase includes fuzzy programming and robust optimisation methods to deal with uncertain and qualitative data which usually exists in generated scenarios. The case study for this thesis focuses on Western Australia?s power capacity expansion needs and demonstrates the application of this new methodology in managing the uncertainties associated with future electricity demand. Scenarios which are generated based on different future population trends and industrial growth are used as the basis of determining the best strategic option for the expansion in WA?s electricity industry. Furthermore, transition to renewable energy and technological constraints for WA?s electricity industry are considered in the proposed framework. The result of this case study is an investment plan that satisfies WA?s electricity demand growth and responds to technological and environmental constraints. The new intelligent robust scenario planning framework has the potential to deal with uncertainties in business environments and provides a strategic option that has the ability to work with plausible scenarios for the future.
|Year of publication:||
|Other Persons:||Bahri, Parisa (contributor)|
|Type of publication:||Book / Working Paper|
|Type of publication (narrower categories):||Thesis|
Moayer, Sorousha <http://researchrepository.murdoch.edu.au/view/author/Moayer, Sorousha.html> (2009) Robust intelligent scenario planning for industrial systems. PhD thesis, Murdoch University.
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