Framework for Improving the Postdisaster Repair Sequence of Interdependent Critical Infrastructure Systems

Repair sequence scheduling is a critical step in the recovery planning of interdependent critical infrastructure systems (CIS) in the aftermath of a disaster. It is an important but challenging task that forms the basis for recovery planning and repair resources allocation by CIS managers. Despite the increasing number of studies analyzing repair sequence scheduling of CIS, existing approaches often struggle to model the complex behavior of CIS accurately under the dynamic impact of repair sequence. Consequently, they fail to fully utilize the detailed operational data of the system, hindering the solving efficiency of repair sequence decision-making models (RSDMMs). To overcome these limitations, this study proposes a new framework for solving RSDMMs. This framework introduces an advanced genetic algorithm-based method (GABM) which incorporates three rules that utilize detailed operational data of the damaged CIS. To obtain the detailed operational data needed to support the improvements in the advanced GABM, the framework leverages a high-level architecture (HLA)-based cosimulation approach to model the recovery process of CIS in detail. The cosimulation approach integrates domain-specific CIS models to capture the interdependencies among CIS and the detailed recovery process data of CIS under the dynamic impact of the repair sequence. To evaluate the effectiveness of the proposed framework, a case study involving two interdependent power and water systems was conducted. The results demonstrated that the proposed cosimulation approach can accurately model the dynamic impact of the repair sequence on the state of CIS. Furthermore, the advanced GABM exhibits significant advantages in terms of convergence speed and identification of the optimal repair sequence. Overall, the proposed framework enhances the ability to solve the RSDMM and supports CIS managers in efficiently responding to disasters.