The presented methodology results in an optimal portfolio of resilience-oriented resource allocation under weather-related risks. The pre-event mitigations improve the capacity of the transportation system to absorb shocks from future natural hazards, contributing to risk reduction. The post-event recovery planning results in enhancing the system's ability to bounce back rapidly, promoting network resilience. Considering the complex nature of the problem due to uncertainty of hazards, and the impact of the pre-event decisions on post-event planning, this study formulates a nonlinear two-stage stochastic programming (NTSSP) model, with the objective of minimizing the direct construction investment and indirect costs in both pre-event mitigation and post-event recovery stages. In the model, the first stage prioritizes a bridge group that will be retrofitted or repaired to improve the system's robustness and redundancy. The second stage elaborates the uncertain occurrence of a type of natural hazard with any potential intensity at any possible network location. The damaged state of the network is dependent on decisions made on first-stage mitigation efforts. While there has been research addressing the optimization of pre-event or post-event efforts, the number of studies addressing two stages in the same framework is limited. Even such studies are limited in their application due to the consideration of small networks with a limited number of assets. The NTSSP model addresses this gap and builds a large-scale data-driven simulation environment. To effectively solve the NTSSP model, a hybrid heuristic method of evolution strategy with high-performance parallel computing is applied, through which the evolutionary process is accelerated, and the computing time is reduced as a result. The NTSSP model is implemented in a test-bed transportation network in Iowa under flood hazards. The results show that the NTSSP model balances the economy and efficiency on risk mitigation within the budgetary investment while constantly providing a resilient system during the full two-stage course.

中文翻译：

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一种增强基础设施在天气相关风险下的弹性的随机规划方法

所提出的方法在天气相关风险下产生了以弹性为导向的资源分配的最佳组合。事前缓解措施提高了交通系统吸收未来自然灾害冲击的能力，有助于降低风险。事后恢复计划提高了系统快速恢复的能力，提高了网络弹性。考虑到由于灾害的不确定性导致的问题的复杂性，以及事前决策对事后规划的影响，本研究制定了一个非线性两阶段随机规划（NTSSP）模型，目的是最小化直接影响事前缓解和事后恢复阶段的建设投资和间接成本。在模型中，第一阶段优先考虑将进行改造或维修的桥梁组，以提高系统的稳健性和冗余度。第二阶段阐述了在任何可能的网络位置具有任何潜在强度的一种自然灾害的不确定发生。网络的损坏状态取决于对第一阶段缓解工作所做的决策。虽然已经有研究解决事前或事后工作的优化，但在同一框架中解决两个阶段的研究数量有限。由于考虑的是资产数量有限的小型网络，即使是此类研究的应用也受到限制。NTSSP 模型弥补了这一差距，构建了一个大规模数据驱动的仿真环境。为了有效求解NTSSP模型，采用高性能并行计算的混合启发式进化策略方法，加速了进化过程，减少了计算时间。NTSSP 模型在爱荷华州洪水灾害下的试验台运输网络中实施。结果表明，NTSSP 模型在预算投资范围内平衡了风险缓解的经济性和效率，同时在整个两阶段过程中不断提供弹性系统。