A Multiagent-Based Decision-Making System for Semiconductor Wafer Fabrication With Hard Temporal Constraints
Bibliographic record
Abstract
This paper presents a decision-making system for semiconductor wafer fabrication facilities, or wafer fabs, with hard interoperation temporal constraints. The decision-making system is developed based on a multiagent architecture that is composed of scheduling agents, workcell agents, machine agents, and product agents. The decision-making problem is to allocate lots into each workcell to satisfy both logical and temporal constraints. A dynamic planning-based approach is adopted for the decision-making mechanism so that the dynamic behaviors of the wafer fab such as aperiodic lot arrivals and reconfiguration can be taken into consideration. The scheduling agents compute quasi-optimal schedules through a bidding mechanism with the workcell agents. The proposed decision-making mechanism uses a concept of temporal constraint sets to obtain a feasible schedule in polynomial steps. The computational complexity of the decision-making mechanism is proven to be, where is the number of operations of a lot and is the cardinality of the temporal constraint set.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".