Clustering and heuristics algorithm for the vehicle routing problem with time windows
Why this work is in the frame
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Bibliographic record
Abstract
This article presents a novel algorithm based on the cluster first-route second method, which executes a solution through K-means and Optics clustering techniques and Nearest Neighbor and Local Search 2-opt heuristics, for the solution of a vehicle routing problem with time windows (VRPTW). The objective of the problem focuses on reducing distances, supported by the variables of demand, delivery points, capacities, time windows and type of fleet in synergy with the model's taxonomy, based on data referring to deliveries made by a logistics operator in Colombia. As a result, good solutions are generated in minimum time periods after fulfilling the agreed constraints, providing high performance in route generation and solutions for large customer instances. Similarly, the algorithm demonstrates efficiency and competitiveness compared to other methods detailed in the literature, after being benchmarked with the Solomon instance data set, exporting even better results.
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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) | 0.000 | 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