Online cutting stock optimization with prioritized orders
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
Purpose To have all the required components of batches of product orders ready for timely assembly and delivery, the real time wood strip cutting patterns in a major solid wood furniture manufacturing plant has to be dynamically generated based on both the order priority and the minimum wood waste. Design/methodology/approach An adaptive fuzzy ranking method and a recursive function for pattern generation were integrated into an optimization procedure to solve the real time one‐dimensional multiple‐grade cutting stock problem when orders are prioritized. Findings The simulation results illustrate that the optimization algorithm produce considerably less waste than the current approach. If implemented in the industry, the saving in raw material could be in the range of 5‐10 percent. Research limitations/implications The optimization algorithm is for the cut‐to‐size decisions only with the consideration of the order priorities. The overall scheduling of the production shop floor is not addressed. Practical implications The algorithm can be used on the cutting machines as an online patterns generator and cutting optimizer. Originality/value There is no literature available for the real time one‐dimensional multiple‐grade cutting stock problem when orders are prioritized. The few commercial optimizers have unknown algorithms with unpredictable waste.
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