The ability to create nuclear weapons from U-235 and Pu-239 makes it imperative to closely account for these materials as they progress through a nuclear fuel cycle. Improved measurement systems provide more accurate estimates of material quantities and material unaccounted for (MUF). This paper provides examples of how two safeguards computational toolboxes can optimize and analyze hypothetical nuclear fuel cycle scenarios. The NUclear Measurement System Optimization (NUMS0) toolbox uses operations research techniques to find optimal solutions to safeguards measurement problems based on minimizing the variance of the estimated MUF. The SafeGuards Analysis (SGA) toolbox employs Monte Carlo techniques to analyze a given configuration of measurement methods and material flows to determine the probabilities of Type I (false detection) and Type II (missed detection) errors. Applying these toolboxes to a realistic fuel cycle scenario demonstrates the capability of NUMSO and SGA to address nuclear safeguards problems. Working in tandem, both toolboxes are able to determine how to quickly improve upon an existing safeguards measurement system and to calculate the resulting improvement in the error probabilities of the system. This information shows engineers not only how to develop new measurement systems but also how to improve existing systems in the most efficient manner.
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