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dc.contributor.advisorBarker, Kash
dc.contributor.authorKuttler, Emma
dc.date.accessioned2022-05-03T16:44:42Z
dc.date.available2022-05-03T16:44:42Z
dc.date.issued2022-05
dc.identifier.urihttps://hdl.handle.net/11244/335482
dc.description.abstractInterdependent critical infrastructure systems represent substantial financial investments and are vital to maintain a basic level of social and economic well-being, making them attractive targets for malevolent actors. Many of these systems carry multiple products, each with unique needs and importance to different stakeholders. Tri-level optimization models have been proposed to capture the scale of a system’s resilience, representing the optimal actions taken by a defender to harden the system, by an attacker to interdict the system, and then by the defender to assign work crews for restoration, all under a limited budget. However, most prior work focuses on networks with a single product. This work extends a tri-level protection-interdiction-restoration model from a single commodity to multiple commodities, solving the model with a Benders’ decomposition and set covering decomposition. We propose a method to limit unmet weighted demand across commodities, taking into account unique interdependencies between network components and commodity-specific capacity requirements. An optimal solution is found iteratively by alternately fixing protection and interdiction variables. This work is illustrated with a case study of interdependent Swedish power and railway systems. Results demonstrate the convergent behavior of the master and subproblems, the value of network hardening, and the non-uniform network recovery trajectory. The proposed model is easily adapted to different commodity types, attack and defense budgets, crew availability, and commodity weights.en_US
dc.languageen_USen_US
dc.subjectoptimizationen_US
dc.subjectresilienceen_US
dc.subjectcritical infrastructureen_US
dc.subjectinterdictionen_US
dc.titleResilience-Based Tri-Level Optimization for Multi-Commodity Networksen_US
dc.contributor.committeeMemberGonzalez Huertas, Andres
dc.contributor.committeeMemberRazzaghi, Talayeh
dc.date.manuscript2022
dc.thesis.degreeMaster of Scienceen_US
ou.groupGallogly College of Engineering::School of Industrial and Systems Engineeringen_US
shareok.nativefileaccessrestricteden_US


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