Advancing Inspection Optimization Models for Enhanced Verification and Efficiency

1. An Argument for Applying Objective Based Optimization Models in the Design of Verification and Inspection Activities Stephen J. Walsh and Claude F. Norman January 24, 2012

2. The Context “As we (the IAEA) are not a research organization, we don’t know what we don’t know (regarding the state of technology and R&D developments that may be useful for us)” – SGCP member at last week’s long term R&D seminar

3. The Objective of the Paper • To attract the attention of technical researchers who solve an organization’s problems from a holistic perspective where the efficient use of technology is quantified. • To make them aware that we may have an interest in advancing the technical (probabilistic and statistical) formulations which underlie the design and implementation of standard inspection activities (Traditional Safeguards Activities). • To provide them with the context of how we wish to see the SOA advanced in a manner consistent with the overarching objectives of Information Driven Safeguards. “Innovative cultures are learning cultures”

4. Motivation:Inspection/Verification Algorithms SOI vs. SOT vs. SOA State of Implementation State of Technology State of the Art < < • Standard statistical approaches – circa < 1990 • Random inspections garnering more respect and implementation ~ 2005 • Minimal treatment of diversion strategy • Applied many approximations which were motivated by limited computing technology • Poor definition of detection vs. selection probability • Expand to Exact Formula and take advantage of cheap computing power • Thorough treatment of diversion strategy – Simulation • Advance standard statistical assumptions that are now know to be false. • Theory: some standard statistical approaches have been generalized to operational approaches. • Rigorous treatment of diversion strategy • Some objective based approaches are mature – conduct an inspection under an objective as opposed to criterion. “A culture of innovation can be a company’s primary source of competitive advantage and can pay off steadily over the years.”

5. The Target Audience (Researchers who think/work like this!) Operations Research: (decision science, management science) Formally referred to as Optimization Theory • An interdisciplinary mathematical science that focuses on the effectiveuse of technology by organizations contrasted with many other science and engineering disciplines which focus mainly on technology giving only secondary consideration to its use. • An application of the scientific method to solving an organization's operational problems. “Operations Research: The world’s most important invisible Profession.”

6. Operations Research: Mathematical Regimes and Techniques • Data Mining/Analysis • Decision Analysis • Engineering • Forecasting • Game Theory • Industrial Engineering • Logistics • Mathematical Modeling • Financial Engineering • Fuzzy logic • Mathematical Optimization (STR-261) • Probability and Statistics (Standard Inspection Design, STR-340, Material Balance Evaluation, Random Inspections) • (non-)Linear programing • Simulation • Statistical Decision Theory • Pattern Theory “In mathematics you don’t understand things, you just get used to them.”

7. Technical Aspects of Inspection/Verification Design • Stratification • Assumptions on the existence of ‘defects’ • Diversion Strategy • Random selection of items to verify • Selection vs. detection probability • Inference/How data gathered during inspection is analyzed • Effectiveness of the inspection as a deterrent to diversion

9. Conclusions (Summary of the Paper) • The purpose of the paper is to attract technical researchers with an OR philosophy to our problem space. • Some historical background on the development of probabilistic/statistical approaches to inspection/verification will be presented. • To communicate we are interested in advancements in inspection design that are consistent with the goals of information driven safeguards. • Illustrate our points by providing heuristic contrast of probabilistic approaches with optimization approaches.