Linear programming as an optimization tool in survivable optical networks
Bibliographic record
Abstract
For a source-destination pair to communicate in a connection-oriented wavelength-routed optical network, a connection in the optical layer between the two nodes must be established. This process, also known as Routing and Wavelength Assignment (RWS), is realized by selecting a path between the two end nodes and allocating a suitable wavelength. The aim of the RWA process is to find routes and assign wavelengths for connection requests in a way that minimizes the consumption of network resources, while at the same time ensuring that no two lightpaths are assigned the same wavelength on a shared fiber link. Routing and wavelength assignment in wavelength-routed WDM networks is a major design issue, especially when survivability is a requirement. To minimize resources in such networks operating under static traffic environment, the problems of routing and wavelength assignment must be solved jointly as a single problem. This study proposes a different approach to formulate the problems of routing and wavelength assignment as Integer Linear Programming (ILP) problem. Unlike other formulations, where the routing sub-problem and wavelength assignment sub-problem are considered separately, this approach addresses the RWA problem compounded. Although this approach increases the number of variables in the problem, it guarantees the optimal solution. Furthermore, the problem may in many cases be solved using simpler linear programming techniques.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".