Deadlock-free scheduling and control of flexible manufacturing cells using automata theory
Bibliographic record
Abstract
This paper presents a novel method for the scheduling and control of flexible manufacturing cells (FMCs). The approach employs automata, augmented by time labels proposed herein, for the modeling of machines, transportation devices, buffers, precedence constraints, and part routes. Ramadge-Wonham's supervisory-control theory is then used to synthesize a deadlock-free controller that is also capable of keeping track of time. For a given set of parts to be processed by the cell, A/sup */ search algorithm is subsequently employed using a proposed heuristic function. Three different production configurations are considered: Case 1) each part has a unique route; Case 2) parts may have multiple routes, but same devices in each route; and Case 3) parts may have multiple routes with different devices. The proposed approach yields optimal deadlock-free schedules for the first two cases. For Case 3, our simulations have yielded effective solutions but in practice, optimal deadlock-free schedules may not be obtainable without sacrificing computational time efficiency. One such nontime-efficient method is included in this paper. The proposed approach is illustrated through three typical manufacturing-cell simulation examples; the first adopted from a Petri-net-based scheduling paper, the second adopted from a mathematical-programming-based scheduling paper, and the third, a new example that deals with a more complex FMC scenario where parts have multiple routes for their production. These and other simulations clearly demonstrate the effectiveness of the proposed automata-based scheduling methodology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".