Optimizing 2D Cutting: A Bin Packing Approach to Minimize Scraps and Maximize Their Reusability
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 industrial settings, cutting predefined pieces from one or multiple sheets of material is a common optimization challenge. This problem can be formulated as a variant of the 2D bin packing problem, where the edges of the pieces define the cut lines. This paper presents a constraint programming model developed in collaboration with an industrial partner in construction to minimize scrap waste generated when cutting insulation pieces. The model introduces an objective function designed to maximize the reusability of leftover material. To fully leverage the model’s efficiency, an initial process transforms irregular insulation pieces into rectangles using one of four processing methods. A comparative analysis is conducted to evaluate the impact of these methods, as well as to benchmark the model’s results against the partner’s manual approach.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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