SUNConferences, Southern African Institute of Industrial Engineering 2013

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Energy Supply Chain Risk Management Using Artificial Neural Networks
Oluyomi Babafemi Ajayi, Bernadette Patricia Sunjka

Last modified: 2013-06-28

Abstract


Energy supply is the backbone of almost all economic activities powering critical systems and infrastructures required for the functioning of our modern economies and societies. Regardless of where and how they are produced, energy must be delivered to the points of consumption and any disruptions in the chain can rapidly cascade into a national crisis. However, sustaining an undisrupted production and supply of this vital resource has been identified as a major challenge globally with issues like terrorism, spiralling and unstable energy prices, technology uncertainties and natural disasters posing serious threats to the supply chain. This study develops a risk management framework for the energy supply chain using artificial neural networks as the transformation and simulation engine. To get a reliable estimate of the health of the critical path in the chain, a few genetic algorithms were tested and the one with best performance chosen for this framework after a comparative analysis.


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