A resilience-based framework for decision making based on simulation-optimization approach

The functioning of infrastructure systems and buildings in the aftermath of a disaster is critical to the modern urban center. A system study of interdependent networks response and understanding the process of post-disaster recovery is the corner stone of an effective emergency management plan. Exposure to stressors such as chaotic circumstances, budgetary restrictions, time limitations, resource constraints, high-level uncertainty and complexity in the recovery process of a community, highlighting the necessity for comprehensive decision- making framework in the aftermath of disasters. A decision-making framework in the community-level needs to puzzle out large-scale stochastic scheduling problems. These types of problems are difficult stochastic control problems with large combinatorial decision spaces. This study introduces the use of approximate dynamic programming along with heuristics for the determination of optimal recovery actions. The proposed approach prevails the curse of dimensionality and manages multi-state, large-scale infrastructure systems. Further, the proposed approach is able to consider the current recovery policies of responsible companies and improves them along with limited resources. A testbed community coarsely modeled after Gilroy, California, is presented as an illustrative example to show how the proposed approach can be implemented efficiently and accurately to find the optimal decisions. The applied approach provides optimal policies for Electrical Power Network (EPN) and Water Network (WN) of Gilroy in the aftermath of a simulated severe earthquake. The methodology is well suited for any existing infrastructure system with potential extensions to any hazard.

Automated summary

The functioning of infrastructure and buildings after a disaster is crucial for modern urban centers, requiring an effective emergency management plan. Decision-making framework at the community level needs to address large-scale stochastic scheduling problems, managing complex infrastructure systems while avoiding the curse of dimensionality. The proposed approach can consider and improve company recovery policies, providing optimal decisions for post-disaster recovery.