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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.23251 |
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| _version_ | 1866916666128990208 |
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| author | Saei, S. Tajik, N. |
| author_facet | Saei, S. Tajik, N. |
| contents | Infrastructure networks are increasingly vulnerable to natural hazards and design flaws, making resilience assessment essential. This paper presents a scenario-based framework to evaluate network vulnerability by combining local measures and topological analysis, assessing each node's role in maintaining network integrity during disruptions. The framework identifies optimization opportunities by comparing structural properties with established standards. Traffic flow is modeled using the Bureau of Public Roads (BPR) function to improve disruption resilience. A two-stage stochastic model captures uncertainties, ensuring robust network performance across diverse scenarios. The approach balances risk-neutral and risk-averse strategies, emphasizing the importance of strengthening critical nodes to prevent cascading failures. The proposed method enhances resilience by minimizing undelivered demand and optimizing overall performance under uncertainty. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_23251 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scenario-Based Optimization of Network Resilience: Integrating Vulnerability Assessments and Traffic Flow Saei, S. Tajik, N. Applications Probability Infrastructure networks are increasingly vulnerable to natural hazards and design flaws, making resilience assessment essential. This paper presents a scenario-based framework to evaluate network vulnerability by combining local measures and topological analysis, assessing each node's role in maintaining network integrity during disruptions. The framework identifies optimization opportunities by comparing structural properties with established standards. Traffic flow is modeled using the Bureau of Public Roads (BPR) function to improve disruption resilience. A two-stage stochastic model captures uncertainties, ensuring robust network performance across diverse scenarios. The approach balances risk-neutral and risk-averse strategies, emphasizing the importance of strengthening critical nodes to prevent cascading failures. The proposed method enhances resilience by minimizing undelivered demand and optimizing overall performance under uncertainty. |
| title | Scenario-Based Optimization of Network Resilience: Integrating Vulnerability Assessments and Traffic Flow |
| topic | Applications Probability |
| url | https://arxiv.org/abs/2503.23251 |