Integrating human factors into discrete event simulations of parallel flow strategies
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
This article demonstrates an approach to integrating human factors (HF) into a discrete event simulation (DES) study of semi-parallelised production strategies. Operationalised HF included operators’ autonomy at work, a known workplace health factor; and reduced operator capability, a factor for new or injured operators. These HF were tested in scenarios of serial flow and two degrees of semi-parallelised flow. The parallel production systems demonstrated better productivity than serial flow in all conditions tested and were less affected by either of the HF tested. It is concluded that HF can be integrated into DES which facilitates early consideration of operator risk and system vulnerabilities. Parallel production approaches, although less common in practice, appear to have advantages over serial flow in terms of productivity, injury risk and the accommodation of operators with temporarily reduced capacity. Further research should expand the range and test the validity of HF integrated into DES modelling procedures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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