Simulation optimization in security screening systems subject to budget and waiting time constraints
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Motivated by the NEXUS program between the US‐Canada border, we consider a security screening process where travelers such as cars and trucks are assigned to a selectee or nonselectee lane depending on whether their threat values exceed a threshold level. The goal is to design a two‐tier system to maximize the probability of detecting a threat subject to the waiting time and operating cost constraints. We formulate the problem as an optimization program, in which the objective function and some variables can be estimated only by simulation. We then develop algorithms to compute the global optimum, within a proposed bounding region, for the Markovian and non‐Markovian systems, respectively. The convergence analysis and extensive numerical results are provided to demonstrate the algorithms' promising performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it