Numerical Solution and Accuracy Improvement of Garment Size Matching Problem Based on Optimization Algorithm
Bibliographic record
Abstract
In the garment production industry, garment cutting size matching plan is an important step in the process, which plays a decisive role in production management and cost control.In this paper, we first model the size matching problem of garment cutting, then use the improved fast particle swarm algorithm (APSO) to optimize the multi-objective optimization solution, and finally verify the performance of the APSO algorithm and the actual effect of garment size matching with cases.Comparing the test results of APSO, PSO and LDWPSO algorithms in the six test functions of Griewank, Ackle, Levy, Rastrign, Schwefel and Sphere, it can be seen that: with the improvement of the problem dimensions, the APSO algorithm used in this paper can still maintain a better optimization accuracy, and the optimization accuracy and stability are significantly improved compared with the PSO and the LDWPSO algorithms.LDWPSO algorithms.In the actual case, the APSO algorithm is more reasonable in the size combination and the number of layers of fabric, for four different types of apparel orders have obtained a superior optimal solution set, cutting production error is far less than the enterprise requirements.At the same time, compared with other optimization methods, the APSO algorithm has better optimization accuracy and solving efficiency, and can obtain a more superior cutting and bed splitting scheme.The algorithm proposed in this paper can effectively optimize the cutting size matching process, reduce fabric waste and production equipment investment, and has good application value and reference significance.
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.
How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".