Constraint-Based Risk and Revenue Optimization for Network Disaster Recovery
Bibliographic record
Abstract
Telecommunication networks are highly vulnerable to disasters, disrupting critical services and causing significant economic losses. This paper presents a constraintbased Integer Linear Programming (ILP) model for disaster recovery that jointly optimizes risk-aware routing and revenue maximization under bandwidth constraints. Unlike traditional heuristic approaches, our model dynamically prioritizes highvalue, low-risk services, ensuring optimal bandwidth allocation while mitigating network failures. The risk function accounts for link failure probabilities and service priority levels, influencing path selection in real time. By enforcing strict bandwidth and survivability constraints, the ILP model strategically schedules fewer but more profitable flows, optimizing economic efficiency without compromising resilience. Simulation results demonstrate that our approach reduces service disruptions, minimizes high-risk routing, and improves revenue efficiency compared to Dijkstra-based routing. These findings offer a scalable framework for network operators to enhance disaster resilience while maintaining economic viability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".