A stochastic programming approach to enhance the resilience of infrastructure under weather‐related risk
A stochastic programming approach to enhance the resilience of infrastructure under weather‐related risk
复制标题
提高基础设施在天气相关风险下的恢复能力的随机规划方法
DOI：
10.1111/mice.12843
复制
发表时间：
2022
影响因子：
9.6
通讯作者：
Alipour, Alice
中科院分区：
文献类型：
--
作者：
Zhang, Ning;Alipour, Alice
所提出的方法导致在与天气相关的风险下的最佳组合的弹性导向的资源配置。事件前的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。事后恢复规划可增强系统快速恢复的能力，提高网络弹性。考虑到问题的复杂性，由于危险的不确定性，以及事件前的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，目标是在事件前缓解和事件后恢复阶段的直接建设投资和间接成本最小化。在该模型中，第一阶段优先考虑将被改造或维修，以提高系统的鲁棒性和冗余的桥梁组。第二阶段阐述在任何可能的网络地点发生的具有任何潜在强度的一类自然灾害的不确定性。网络的受损状态取决于第一阶段缓解工作的决策。虽然已经有研究解决了事件前或事件后工作的优化，但在同一框架中解决两个阶段的研究数量有限。即使这样的研究是有限的，在他们的应用，由于考虑到小网络的资产数量有限。NTSSP模型解决了这一差距，并建立了一个大规模的数据驱动的仿真环境。为了有效求解NTSSP模型，采用了一种进化策略与高性能并行计算相结合的混合启发式方法，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一个试验台交通网络中实现。结果表明，NTSSP模型在预算投资内平衡了经济性和风险缓解效率，同时在整个两阶段过程中不断提供弹性系统。
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.
登录
查看更多内容
影响因子：
1.7
作者：
H. Adeli;O. Kamal
通讯作者：
O. Kamal
不确定性和风险评估
影响因子：
4.3
作者：
C. Carrington;P. Bolger
通讯作者：
P. Bolger
交通网络防洪策略优化的多步评估框架
影响因子：
5
作者：
Ning Zhang;A. Alipour
通讯作者：
A. Alipour
如何解决它：现代启发法
影响因子：
6
作者：
Dr. Zbigniew Michalewicz;D. Fogel
通讯作者：
D. Fogel
通过快速损坏评估和响应策略增强桥梁对极端事件的抵御能力
影响因子：
1.7
作者：
A. Alipour
通讯作者：
A. Alipour