Mixed Integer Programming Modelling Approaches for Clean Energy Systems and Cybersecure Manufacturing




Keith, Kolton

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Network based mixed integer linear programming models (MILPs) have to potential to contribute to two major issues: clean energy systems and cyber-secure manufacturing. For the former, biomass is an abundant resource for energy production and it has gained attention as a mainstream option to meet our increasing energy demands. In this research, stochastic Hub-and-spoke networks, mathematical models, optimization algorithms and novel solution procedures are proposed in service of the pursuit of the two aforementioned objectives. For clean energy, supply chain network design for the conversion of biomass to various bio-products are discussed. These bioenergy supply chain (BSC) models decisions include biomass routing and the opening of pre-processing and conversion facilities (depots and biorefineries, respectively). The models are tested with realistic state-wide, county level case studies. For cyber-secure manufacturing, directed attack graph (AG) MILPs are formulated and applied to a case study considering a semi-conductor wafer fabrication facility. This model helps with enhancing the visibility of Information Technology and Operational Technology networks and helping decision makers to identify an appropriate budget level to protect such network.


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Biofuel Supply Chain, Cybersecurity, Machine Learning, Mixed Integer Linear Programming, Stochastic Programming



Mechanical Engineering