The Work in Process (WIP) Control Model and Its Application Simulation in Small-batch and Multi-varieties Production Mode
Bibliographic record
Abstract
This paper aimed at the phenomena of the volume of work in process (WIP) great and uneven distribution, which is caused by small batches, various breeds, the complex scheduling, and long production cycle and so on in machine manufacturing industry. The machine manufacturing industry production workshop is taken as the research object in this paper. A work in process (WIP) control model based on the limited capacity is put forward by analyzing the characteristics of multi-varieties of small-batch production, the factors of the state of workshop equipment, equipment parameters (breakdown rate and maintenance rate), delivery deadline, product process similarity and so on. Taking a gear production line of a state-owned large-scale speed reducer’s factory as an example, Witness2003 is used to simulate and optimize the work in process control model of the gear production line. The case study proves that in the manufacturing of small-batch and multi-varieties production mode, WIP volume control problems can be effectively solved by the WIP control model. And the WIP control model provides an effective, workable solution for small-batch and multi-varieties production under the control of the production mode.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".