On-line scheduling and control of flexible manufacturing cells using automata theory
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
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. Keywords: Discrete-event-systemsAutomata theorySupervisory controlOn-line routing and scheduling Acknowledgements This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC). Mr Golmakani was also supported by a scholarship from the Ministry of Research, Science, and Technology of Iran (MRST).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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