An approximation method to analyse polling models of pull-type production systems
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a polling model with finite queues to analyse a production system that is operating under a pull-type control mechanism is studied. The polling model is analysed under three well-known service policies namely, exhaustive, gated and g-limited. These policies determine when and how machines switch from one part to another. These polling models can be solved by an exact approach that requires the complete characterisation of the system as a Markov chain. However, the state space increases dramatically with queue capacity and the number of parts manufactured. Hence an approximation approach that uses the regenerative property of the system is devised. Our numerical study shows that the approximation approach is not only very efficient in solving the problem but also very effective in finding accurate estimations of the system performance measures. [Received 13 December 2006, Revised 21 Febrauary 2007, Accepted 7 March 2007].
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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