Architecture for Decision Logic Unit in Agile Manufacturing Planning and Control Systems
Bibliographic record
Abstract
Manufacturing control systems typically include layers of logic and heuristics making them difficult to develop. Furthermore, in agile manufacturing planning and control (MPC) environment, the dynamics of decision-making interactions between agile MPC system components are poorly understood and can be undependable because of the absence of a master controller or optimizer. This paper addresses these challenges by developing a multi-layer architecture for a supervisory decision logic unit (DLU) of an agile MPC system. After presenting a dynamic model for the system, a decision logic unit for that system that links the operational level with the higher enterprise level strategy is described. The architecture of the DLU is composed of three layers where the first layer is responsible for dynamically managing the selection of the different MPC policies that suits the market strategy. The second layer describes an algorithm for optimal parameters settings for each of the MPC policies. Finally the third layer of the architecture is responsible for the automatic on-line control of manufacturing system to maintain required production, work-in- process and inventory levels.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".