Near-optimal FIPP p-cycle network designs using general path-protecting p-cycles and combined GA-ILP methods
Bibliographic record
Abstract
Recent work on failure independent path-protecting p-cycles (FIPP) has revealed some new, relatively simple and possibly cost-effective approaches for FIPP p-cycle network design. The first step of the proposed strategy consists of solving a more general path-protecting p-cycle (GPP) problem in which the constraint of failure independence is relaxed. The second step consists of imposing the failure independence constraint onto the GPP solution and identifying the working paths that become unprotected as a result. A FIPP p-cycle solution is extracted by capacitating additional cycles to protect these paths, the number of which the results revealed to never exceed three. Another contribution of this work is the adaptation of the novel combination of genetic algorithms with integer linear programming (GA-ILP) to the GPP concept, which allowed us to solve large GPP problem instances. GA-ILP solutions were typically within 1% of optimality for smaller networks for which the exact solutions were known. The GPP and FIPP solutions obtained with the assistance of GA-ILP were considerably better (by as much as 23%) than those obtained by the FIPP disjoint route set (DRS) method. Furthermore, the results obtained in this paper also showed that relaxing the disjoint route set constraint in FIPP p-cycle networks can result in as much as 9% decrease in spare capacity cost. Also in this paper, we ventured to provide a true comparison of span-protecting p-cycles with FIPP p-cycles, from the capacity efficiency perspective.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".