Dynamic modelling of reconfigurable manufacturing planning and control systems using supervisory control
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
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.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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