Assessing and minimizing adversarial risk in a nuclear material transportation network

Abstract

This thesis develops a simple method for evaluating adversarial risk within the transportation portion of the nuclear fuel cycle for commercial electric power generation, and develops models that can guide the reduction of that risk by such means as rerouting and decoy shipments. A conceivable, worst-case attack by an intelligent adversary will cause a localized release of radioactive material. A damage function is defined using the population in the vicinity of the attack. Using hypothetical, but realistic, transit routes be-tween fuel fabricators and power plants, we identify the worst-case locations for attack. Then we formulate and solve mixed-integer programs to either (a) redesign the network by changing supply contracts, or (b) optimally allocate a resource-constrained assignment of decoy shipments. We also demonstrate a greedy procedure for simple rerouting of individual shipments. Computational methods exploit standard geographical databases, and optimization software solves the models in seconds on a personal computer. Separate but similar analyses would apply to shipments of uranium hexafluoride, spent fuel being shipped for reprocessing, spent fuel being shipped to a repository, and other materials.