On-line scheduling and control of flexible manufacturing cells using automata theory
Bibliographic record
Abstract
An effective flexible-manufacturing-cell (FMC) controller can be synthesized using classical automata and Ramadge–Wonham (R–W) supervisory-control theories. Such a discrete-event-system controller/supervisor would enable or disable (controllable) events for maximum deadlock-free, correct behaviour of the FMC. However, in cases of multiple part-routes, machine redundancy, etc. the R–W supervisor could include states with subsequent multiple controllable events. The question of choice arises at such states necessitating the use of an on-line decision-making agent, which, consequently, would determine the overall performance of the FMC. In the above context, this paper presents a novel methodology for the on-line, deadlock-free scheduling and control of FMCs. A two-phased method is proposed. During the off-line phase, the FMC is first modelled using time-augmented automata and a deadlock-free supervisor is synthesized using R–W control theory. Subsequently, an off-line decision-making plan is constructed. During the on-line phase, based on the latest state of the workcell and the off-line plan, the best-possible scheduling decisions are made using a real-time optimization search technique. The proposed novel approach is illustrated through a typical manufacturing-cell simulation example and compared with a random-based decision-making policy. It is clearly shown that significant improvement is achieved when using the proposed approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".