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Record W3133252064 · doi:10.1002/nav.21976

Simulation optimization in security screening systems subject to budget and waiting time constraints

2021· article· en· W3133252064 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueNaval Research Logistics (NRL) · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicSimulation Techniques and Applications
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of Science and Technology, Taiwan
KeywordsBounding overwatchComputer scienceNexus (standard)Mathematical optimizationConvergence (economics)Process (computing)Markov processOperations researchBudget constraintEngineeringMathematicsArtificial intelligenceStatistics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.035
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.893
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.306
GPT teacher head0.515
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it