Locating a cycle in a transportation or a telecommunications network
Bibliographic record
Abstract
Abstract Several problems arising in transportation and telecommunications can be cast in terms of optimally locating a cycle in a graph. This paper proposes a classification of cycle location problems under two main headings. In Hamiltonian problems, all vertices of the graph must belong to the cycle. The most important cases are the traveling salesman problem (TSP), the TSP with precedence constraints, the clustered TSP, the TSP with backhauls, the TSP with time windows, several classes of pickup and delivery problems, and stochastic TSPs. In non‐Hamiltonian problems, only a subset of vertices must be visited. These problems include the generalized TSP, the covering tour problem, the median cycle and ring star problems, and several cycle location problems with revenues. These problems are modeled within a unified framework and algorithmic strategies are provided, together with computational results. Several applications are also described. © 2007 Wiley Periodicals, Inc. NETWORKS, Vol. 50(1), 92–108 2007
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".