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3332. Optimizing Post-Disaster Recovery Strategies in Telecommunication Network Infrastructure
Invited abstract in session WA-3: Data Science and Optimization, stream Data Science Meets Optimization.
Wednesday, 8:30-10:00Room: 1005 (building: 202)
Authors (first author is the speaker)
| 1. | Mohamed Saâd El Harrab
|
| Industrial Engineering Department, École nationale des ponts et chaussées | |
| 2. | Michel Nakhla
|
| CGS-I3-MINES-ParisTech-Université ParisSaclay-AgroParisTech |
Abstract
In the wake of an increasing number of natural disasters such as storms and cyclones, overhead electricity and telecommunications infrastructures are exposed to significant risks, with damage to pylons, antennas and poles disrupting essential services.
This study introduces a hybrid model that integrates machine learning and discrete optimization to optimize emergency response. By analyzing storm damage to electricity and telecoms networks, we apply machine learning to cluster affected areas based on storm severity, customer reports and wind speed. This data-driven insight informs a unique mixed-integer programming model, treating the challenge as a variant of the capacity expert routing problem. Our solution employs computational experiments and the Branch and Price algorithm, with a detailed case study of a storm event in France serving as a practical illustration.
The results demonstrate the effectiveness of our hybrid approach in optimizing emergency team deployment strategies, adapted to the varying requirements of different storm severities. This contribution enriches the emergency operations management and disaster relief domain, presenting a novel perspective on combinatorial optimization for crisis mitigation.
Keywords
- Telecommunications
- Artificial Intelligence
- Vehicle Routing
Status: accepted
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