Forest vehicle routing problem solved by New Insertion and meta-heuristics
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
The main objective of the paper is to propose a mathematical method, based on New Insertion technique and meta-heuristics to solve forest transportation routing problem. To perform this work, firstly a mathematical model is proposed; secondly a New Insertion algorithm is used to build an initial solution and thirdly the extended great deluge and reactive tabu search are used to improve this solution. The objective is to minimize the total cost by respecting the time window of all customers, which is sometimes important in this field. Finally, the experimental results obtained with the extended great deluge for the named vehicle routing problem are showed, discussed and compared to its reactive tabu search results obtained using the same initial solution. The reactive tabu search is quicker than the extended great deluge; but instead of only one parameter to control in the extended great deluge, we have to control six parameters in reactive tabu search.
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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 | 0.000 | 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