Bibliographic record
Abstract
Much interest exists in broadband network services to deliver a variety of products to consumers, such as Internet access, telephony, interactive TV, and video on demand. Due to its cost efficiency, Hybrid Fiber Coaxial (HFC) technology is currently being considered by most Telcos and cable companies as the technology to deliver these products. The topological HFC network design problem as implemented by several major companies is a form of the capacitated tree-star network design problem. We propose a new formulation for this problem and present a heuristic based on hierarchical decomposition of the problem. The proposed solution methodology exploits an Adaptive Reasoning Technique (ART), embedded as a meta-heuristic over specialized heuristics for the subproblems. In this context, we demonstrate the dynamic use of an exact solution technique within ART. The generalizability of the proposed solution methodology is demonstrated by applying it to a second problem, the Traveling Salesman Problem (TSP). Computational results are presented for both the HFC network design problem and the TSP, indicating high-quality solutions expending a very modest computational effort. The proposed solution method is found to be effective, and is shown to be easily adaptable to new problems without much crafting, and as such, has a broad appeal to the general operations research community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".