Optimally configuring a measurement system to detect diversions from a nuclear fuel cycle

Benjamin L. Johnson (), Aaron T. Porter (), Jeffrey C. King () and Alexandra M. Newman ()
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Benjamin L. Johnson: Colorado School of Mines
Aaron T. Porter: Colorado School of Mines
Jeffrey C. King: Colorado School of Mines
Alexandra M. Newman: Colorado School of Mines

Annals of Operations Research, 2019, vol. 275, issue 2, No 7, 393-420

Abstract: Abstract The civilian nuclear fuel cycle is an industrial process that produces electrical power from the nuclear fission of uranium. Using a measurement system to accurately account for possibly dangerous nuclear material, such as uranium, in a fuel cycle is important because of its possible loss or diversion. A measurement system is defined by a set of measurement methods, or “devices,” used to account for material flows and inventory values at specific locations at facilities in the fuel cycle. We develop a simulation-optimization algorithm and an integer-programming model to find the best, or near-best, resource-limited measurement system with a high degree of confidence. The simulation-optimization algorithm minimizes a weighted sum of false positive and false negative diversion-detection probabilities while accounting for material quantities and measurement errors across a finite, discrete time horizon in hypothetical non-diversion and diversion contexts. In each time period, the estimated cumulative material unaccounted for is compared to a fixed or an optimized threshold value to assess if a “significant amount of material” is lost from a measurement system. The integer-programming model minimizes the population variance of the estimated material loss, i.e., material unaccounted for, in a measurement system. We analyze three potential problems in nuclear fuel cycle measurement systems: (i) given location-dependent device precisions, find the configuration of n devices at n locations ( $$n=3$$ n = 3 ) that provides the lowest corresponding objective values using the simulation-optimization algorithm and integer-programming model, (ii) find the location at which improving device precision reduces objective values the most using the simulation-optimization algorithm (given the device accuracy is 100%), and (iii) determine the effect of measurement frequency on measurement system configurations and objective values using the simulation-optimization algorithm. We obtain comparable results for each problem at least an order of magnitude faster than existing methods do. Using an optimized, rather than a fixed, detection threshold in the simulation-optimization algorithm reduces the weighted sum of false positive and false negative probabilities.

Keywords: Integer programming; Simulation optimization; Resource allocation; Nuclear safeguards (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10479-018-2940-x

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