Routing and wavelength assignment in single hop all optical networks with minimum blocking. Research Report G-2004-12, Université de Montréal, Groupe d’Etude et de Recherche en Analyse des Décisions
Bibliographic record
Abstract
wdm networks offer a technology that can transferred several optical signals into a single optical fiber. This allows for more efficient use of the huge capacity of optical fibers but it also poses new network design and management problems such as routing and wavelength planning. This paper discusses the rwa problem, i.e., the routing and wavelength assignment problem in wdm networks. Given a set of requests and the number of available wavelengths the fibers support, we are to find, through the network, a route for each connection and assign wavelengths to them. In this paper, we consider the objective of minimizing the number of connections that have to be denied. We propose a multi-phase heuristic algorithm called rwabou. It begins with an initialization step in which we compute for each connection, r-shortest paths from each source to their destination. It is followed by an algorithm with two interactive phases. The first phase integrates a Tabu Search heuristic for the wavelength assignment followed by and interacting with a partial rerouting heuristic phase for the blocked connections. Computational experience shows that
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".