Date of Graduation
Doctor of Philosophy in Engineering (PhD)
Second Committee Member
Third Committee Member
In this dissertation, we model three different security scenarios and propose solution methodologies to address each problem.
Chapter 2 presents a large-scale optimization approach for solving a dynamic bi-level network interdiction problem (NIP) in which interdiction activities must be scheduled in order to minimize the cumulative maximum flow over a finite time horizon. A logic-based decomposition (LBD) approach is proposed that utilizes constraint programming to exploit the scheduling nature of this dynamic NIP. Chapter 3 considers a set of centers to which content (e.g., data or smuggled items), are assigned to ensure availability. An interdictor (e.g., border security officials) attempts to determine which centers (e.g., border's checkpoints) to interdict in order to minimize the content availability. We present our efforts to model the problem as an Integer Programming formulation and show that the problem is NP-hard. We propose modeling improvements, which, in conjunction with a genetic algorithm is used to obtain quality solutions to the problem quickly. A comparison of the approaches is presented along with future research direction for the problem. Finally, Chapter 4 pursues a quantitative risk assessment of the complete poultry supply chain in China. This work is supported by collaborators in biological engineering, poultry science and numerous companies and universities throughout China. This effort considers contamination concerns from Salmonella for chicken broilers studied at the production steps in the supply chain as well as offering one of the first attempts to include the transportation, distribution, retail and consumption elements that complete the supply chain. Our quantitative risk assessment model makes use of preliminary data collected from a Chinese poultry company since Fall 2016.
Enayaty Ahangar, F. (2017). Models and Methodologies to Address Emerging Needs in Network and Supply Chain Optimization. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/2443