Dynamic modelling of reconfigurable manufacturing planning and control systems using supervisory control
Bibliographic record
Abstract
This research is concerned with studying the dynamic performance of reconfigurable Manufacturing Planning and Control (MPC) systems. Such goal requires two main tasks. The first task is to develop a dynamic MPC system model that has the ability to reconfigure to different MPC policies. The second task is to design a supervisory control unit that has as input the high level strategic market decisions and constraints together with a feedback of the current manufacturing system state and then select the optimal suitable operation mode or policy at these conditions. This paper addresses the first task of the proposed research and presents and analyses a dynamic reconfigurable MPC model. The response of the developed model to sudden demand changes under different parameters settings is analysed. In addition, the stability limits of the system are also studied. The results give a better understanding of the dynamics of reconfigurable MPC systems and the different trade-off decisions required when selecting an MPC policy and the limits for parameters settings. These results represent the first step towards designing the supervisory control unit which will be responsible for managing the reconfiguration of the whole system.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".