Tacit knowledge in production sequencing: a Seq2Seq-LSTM approach
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
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.
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 | 0.001 |
| 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