Design of an order acceptance and scheduling module in a unified framework with product and process features
Bibliographic record
Abstract
In manufacturing industry, advanced production planning becomes challenging when collaborative manufacturing is involved. First, manufacturing tasks are driven by the customer's requirements which are constantly changing. Therefore, order acceptance becomes a complicated problem because it is difficult to answer whether the order can be fulfilled or not. Further, each contractor's capacity becomes a factor to be considered constantly in the overall dynamic planning and scheduling activities. Such effort requires the mapping exercise between manufacturing tasks and contractors' capabilities with some detailed optimization. On the other hand, engineering configuration solutions for customers are constantly updated, so are the manufacturing tasks, and then supplier/contractor jobs. Customer-oriented manufacturing demands full integration of engineering design and production planning in an integrated environment. This paper proposes a generic framework in an advanced Enterprise Resource Planning (ERP) system for the unification of product and process models in order to fulfill the variety of customer orders. A feature-based production scheduling and capacity management data structure model is suggested accordingly. Based on the suggested framework, an order acceptance and scheduling (OAS) module within Visual Manufacturing (a commercial ERP system) is designed. Technologically, a new feature category, i.e. process features, including capacity and customer profile features, is introduced for further research and implementation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".