Tacit knowledge in production sequencing: a Seq2Seq-LSTM approach
Bibliographic record
Abstract
In an increasingly complex production environment, production scheduling is more critical than ever to ensure productivity and profitability. Generally treated as an optimization problem, production scheduling faces a gap between theory and practice, but tacit knowledge on the shopfloor can influence production sequencing and scheduling. Recurrent neural networks (RNNs) are known for their ability to extract useful information from the sequential context. Seq2Seq architecture using RNNs, and specifically Long Term Memory Cells (LSTM), is known for its ability to predict discrete event sequences from sequential context. We propose a six-step methodology based on a Seq2Seq-LSTM architecture to predict the most likely scenarios used by the production manager to sequence the production, considering the actual state of the shop floor. This prediction allows the evaluation of a finite number of plausible scenarios based on past production experiences. This approach aims to bridge the gap between theoretical and practical scheduling since the historical data include the tacit knowledge present on the shopfloor.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".