Heuristics to Solve a Real-world Asymmetric Vehicle Routing Problem with Side Constraints
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Bibliographic record
Abstract
To maintain patients at home as most as possible, healthcare services are nowadays quite diversified. We present the case of a public medical clinic offering activities, mostly to the elderly, at a daycare center. Users are brought into the daycare center by bus or by taxi. The global problem consists in defining routes to pick up users while assigning them to time slots in the week. At first sight this problem could be viewed as an asymmetric multiple vehicle routing problem. However many additional constraints must be considered. In this paper, we propose a metaheuristic including two phases to solve the problem. In the first phase, the initial solution is determined using one of two proposed constructed algorithms and is improved using tabu search in the second phase. These algorithms are tested on 10 problem instances. Experimental results are presented.
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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 | 0.001 |
| 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