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<p>The escalating frequency and intensity of natural hazards necessitate robust strategies for enhancing community resilience and mitigating disaster risks. This paper provides a comprehensive review spanning January 2014 to May 2025 that analyzes the application of optimization models as decision-support tools in community resilience planning, intended as a resource for researchers and policymakers. We systematically categorize optimization techniques (including linear/non-linear, stochastic, robust, multi-objective, multi-level, and decentralized approaches) and examine their use across critical domains such as interdependent infrastructure management, building portfolio mitigation, evacuation logistics, and post-disaster relief distribution. The review confirms the significant utility of optimization in handling complex decision-making involving resource allocation, multi-actor coordination, and uncertainty. Contemporary resilience research increasingly integrates crucial socio-economic vulnerability factors and detailed building inventory data into assessments; correspondingly, optimization models are progressively being adapted to incorporate these dimensions. However, this synthesis identifies persistent challenges and frontiers for optimization methodologies in this context. Key areas requiring further advancement include: developing optimization frameworks that can effectively manage deep uncertainty and dynamic adaptation in complex, integrated socio-physical systems; enhancing the scalability of sophisticated multi-agent models for interdependent network restoration, particularly concerning the operational functionality of buildings; and ensuring optimization objectives and constraints adequately promote equitable outcomes in mitigation and recovery. Future research should therefore focus on creating more adaptive, scalable, and practically implementable optimization approaches within interdisciplinary frameworks to address these specific challenges, thereby advancing the development of truly comprehensive and equitable community resilience strategies.</p>

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Systems optimization for enhancing community resilience to natural hazards: a review of integrated modeling approaches

  • 期刊论文

  • 发表日期：

    2026年3月6日

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  • Himadri Sen Gupta,
  • Andrés D. González

摘要

The escalating frequency and intensity of natural hazards necessitate robust strategies for enhancing community resilience and mitigating disaster risks. This paper provides a comprehensive review spanning January 2014 to May 2025 that analyzes the application of optimization models as decision-support tools in community resilience planning, intended as a resource for researchers and policymakers. We systematically categorize optimization techniques (including linear/non-linear, stochastic, robust, multi-objective, multi-level, and decentralized approaches) and examine their use across critical domains such as interdependent infrastructure management, building portfolio mitigation, evacuation logistics, and post-disaster relief distribution. The review confirms the significant utility of optimization in handling complex decision-making involving resource allocation, multi-actor coordination, and uncertainty. Contemporary resilience research increasingly integrates crucial socio-economic vulnerability factors and detailed building inventory data into assessments; correspondingly, optimization models are progressively being adapted to incorporate these dimensions. However, this synthesis identifies persistent challenges and frontiers for optimization methodologies in this context. Key areas requiring further advancement include: developing optimization frameworks that can effectively manage deep uncertainty and dynamic adaptation in complex, integrated socio-physical systems; enhancing the scalability of sophisticated multi-agent models for interdependent network restoration, particularly concerning the operational functionality of buildings; and ensuring optimization objectives and constraints adequately promote equitable outcomes in mitigation and recovery. Future research should therefore focus on creating more adaptive, scalable, and practically implementable optimization approaches within interdisciplinary frameworks to address these specific challenges, thereby advancing the development of truly comprehensive and equitable community resilience strategies.