On the Traveling Salesman Problem with Hierarchical Objective Function
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Bibliographic record
Abstract
We address a novel variant of the wellknown Traveling Salesman Problem (TSP) called the Traveling Salesman Problem with Hierarchical Objective (TSPHO). In this problem, the customers are divided in to several groups with decreasing priority levels, i.e., the first group is more important than the second one and the second one is more important than the third one, and so on. The difference between TSPHO and the classical TSP lies in the objective function. The Hierarchical Objective does not minimize the total travel cost, but aims to minimize the completion time of the first group then the completion time of the second group, etc. A transformation of the TSPHO into an equivalent Asymmetric TSP is first proposed from which one can use efficient TSP solvers such as Concorde or Lin-Kernighan-Helsgaun (LKH) to solve the problem. A genetic algorithm is also developed as an alternative solution. Computational results show the performance of our methods.
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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