Evaluating alternative manufacturing control strategies using a benchmark system
Why this work is in the frame
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Bibliographic record
Abstract
This paper describes an investigation of the effects of dynamic job routing and job sequencing decisions on the performance of a distributed control system and its adaptability against disturbances. This experimental work was carried out to compare the performance of alternative control strategies in various manufacturing environments and to investigate the relationship between the ‘control’ and ‘controlled’ systems. The experimental test-bed presented in this paper consists of an agent-based control system (implemented in C++) and a discrete-event simulation model. Using this test-bed, various control strategies were tested on a benchmark manufacturing system by varying production volumes (to model the production system with looser/tighter schedules) and disturbance frequencies. It was found that hybrid strategies that combine reactive agent mechanisms (and allocation strategies such as the contract net) with appropriate job sequencing heuristics provide the best performance, particularly when job congestion increases on a shop-floor.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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