Analysis of production authorization card schemes using simulation and neural network metamodels
Why this work is in the frame
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Bibliographic record
Abstract
We have developed a framework to model and analyze the performance of complex manufacturing systems operating under a variety of production control strategies. This framework involves a production authorization card scheme, which enables emulation of many popular strategies such as kanban or Base Stock systems. A discrete-event simulation model of the manufacturing system produces estimates of the multiple system performance measures, such as average work-in-process inventory and customer service rates, for combinations of control parameters. Finally, neural network metamodels are trained to approximate the expected value of these system performance measures, using a subset of parameter combinations and the corresponding performance estimates generated by the simulation model. We will show that this framework provides a flexible means of conducting analysis of the impact of parameter settings on the performance of the system, and is a viable alternative to simulation optimization.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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