面向韧性提升的相互依赖关键基础设施网络灾后修复模型研究

Post-disruption Restoration Model for Enhancing Resilience of Interdependent Critical Infrastructure Networks

科学合理制定相互依赖关键基础设施网络(Interdependent Critical Infrastructure Network,ICINs)遭灾后毁坏组件的修复计划是其安全管理的至关重要内容。本文首先明确了ICINs的韧性测度,分析了其灾后修复策略;然后基于网路流理论,以最大化ICINs的韧性为目标,构建了在有限灾后修复资源约束下,ICINs的灾后修复任务选择与调度的混合整数规划模型,并设计了遗传算法进行求解;最后通过不同规模的用例实验对模型和遗传算法进行了测试。研究表明:(1)该模型具有解决相关问题的可行性与有效性;(2)设计的遗传算法能获得质量较高的满意解,且对于大规模问题,遗传算法的求解时间与求解结果优于Cplex软件;(3)将网络之间的功能与空间相互依赖同时纳入模型中,能使ICINs的韧性达到更高。研究可为ICINs的灾后修复决策提供辅助。

The reasonable post-disruption restoration plan of damaged components in interdependent critical infrastructure networks(ICINs)is the key issue of its safety management.Firstly,in this paper,the resilience metric of ICINs is defined and the post-disaster restoration strategy is analyzed.Then,aiming at maximizing the resilience of ICINs,a mixed integer programming model is formulated based on network flow theory for selection and sequencing of post-disaster restoration tasks of ICINs under the constraint of limited post-disaster restoration resources,and a genetic algorithm is developed to solve the problem.Finally,the proposed model and genetic algorithm are tested by applications with different sizes.The results show that:(1)the proposed model is feasible and effective;(2)the developed genetic algorithm can obtain high-quality solution,and the solution time and results are better than that of Cplex software for large size problems;(3)integrating the functional and spatial interdependencies between CINs into the proposed model can get higher resilience of ICINs.Our model can provide decision support for post-disaster restoration of ICINs.