Agent-Based Decision Support and Simulation for Wood Products Manufacturing
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
A rough mill is a manufacturing plant where lumber of approximate dimensions is cut into components of specific sizes, priorities, and qualities to fill customer orders for wood products such as furniture, doors, and window frames. Lumber is a valuable natural resource that is a significant expense to the company. By improving the processes in the rough mill, cost can be reduced and waste of natural materials is decreased. We present an overview of research in agent-based manufacturing systems. The operations in a rough mill are described and the decisions that operators take are identified. A rough mill decision support and simulation system is designed and implemented. An agent ontology for rough mill operations is developed. A prototype system is implemented to demonstrate the architecture and interagent communication. This prototype is extended to two lines of production and the negotiation protocol is presented. Extension of the approach to multiple lines of production is discussed. The prototype system is used to implement a decision support and simulation system that is validated with historical data. Finally, we present a comparison of the advantages and disadvantages of using the multiagent paradigm in rough mill decision support.
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 | 0.000 | 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 it