Scholarlink

<p>Critical infrastructure networks are susceptible to disruptions, and an imbalanced distribution of component importance can significantly amplify their vulnerability to cascading failures or targeted attacks. This study introduces a novel bi-objective optimization framework designed to enhance network resilience by strategically balancing component importance in multi-commodity spatial networks. The first objective minimizes the total deviation of flow-based, commodity-specific component importance measures (derived from single-link interdiction scenarios) from their respective averages, achieved through targeted capacity augmentations. The second objective minimizes the network’s overall proportional unmet demand, reflecting operational performance. The <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\epsilon \)</EquationSource> </InlineEquation>-constraint method is utilized to generate Pareto-optimal solutions, explicitly quantifying the trade-offs between achieving importance balance and maintaining unmet demands. We demonstrate this approach using a comprehensive case study of the Swedish multi-commodity railway network. The results reveal distinct commodity-specific vulnerability profiles and show that strategic capacity additions can substantially improve importance balance, with the analysis detailing the performance trade-offs under various capacity augmentation limits.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Balanced Design of Important Components in Multi-Commodity Networks

  • 期刊论文

  • 发表日期：

    2026年5月20日

查看全文
  • Himadri Sen Gupta,
  • Kash Barker,
  • Andrés D. González,
  • Mick B. Christensen,
  • Jonas Johansson,
  • Enrico Zio

摘要

Critical infrastructure networks are susceptible to disruptions, and an imbalanced distribution of component importance can significantly amplify their vulnerability to cascading failures or targeted attacks. This study introduces a novel bi-objective optimization framework designed to enhance network resilience by strategically balancing component importance in multi-commodity spatial networks. The first objective minimizes the total deviation of flow-based, commodity-specific component importance measures (derived from single-link interdiction scenarios) from their respective averages, achieved through targeted capacity augmentations. The second objective minimizes the network’s overall proportional unmet demand, reflecting operational performance. The \(\epsilon \) -constraint method is utilized to generate Pareto-optimal solutions, explicitly quantifying the trade-offs between achieving importance balance and maintaining unmet demands. We demonstrate this approach using a comprehensive case study of the Swedish multi-commodity railway network. The results reveal distinct commodity-specific vulnerability profiles and show that strategic capacity additions can substantially improve importance balance, with the analysis detailing the performance trade-offs under various capacity augmentation limits.