Optimizing post-disruption response and recovery operations to improve resilience of critical infrastructure systems

Abstract

Critical infrastructure systems (CIS) underpin almost every aspect of the modern society by providing the essential functions through overlaying service networks. In the case of a disruption, the functionality of these networks are degraded, which adversely affects the daily lives and economic productivity of communities. Thus, after an extreme event, in order to minimize the negative impact to society, it is crucial to restore the disrupted services provided by CIS as soon as possible and improve resilience of the CIS. In this dissertation, we focused on disruptions created by natural hazards on transportation CIS, and developed mathematical models and methods to efficiently plan the post-disaster response and recovery operations. However the problem can be generalized to any type of a disruption and applied to any type of CIS. In the aftermath of a natural disaster, the transportation network is disrupted due to the debris blocking the roads and obstructing the flow of relief aid and search-and-rescue teams between critical facilities and disaster sites. In the first few days following a disaster, in order to deliver aid to those in need, blocked roads should be cleared by pushing the debris to the sides. In this context, we defined the road network recovery problem (RNRP) as finding a schedule to clear the roads with limited resources such that all the service demanding locations are served, in the shortest possible time. In the first chapter, we tackle the deterministic RNRP and propose a novel network science inspired measure to quantify the criticality of the components within a disrupted service network and develop a restoration heuristic. In the second chapter we tackle RNRP with stochastic demand and propose an approximate dynamic programming approach for identifying an effective policy under uncertainty. After the immediate effects of the disaster have passed, the debris accumulated on the road sides during the response phase are collected and transported to processing sites by multiple contractors. In this context, we defined the debris collection problem as finding an assignment of roads for each contractor such that the assigned workload among different subcontractors is balanced, the time to complete all debris collection operations is minimized, and the overlap between the assigned regions to different contractors is minimized creating a operationally practical solution. We propose an interactive optimization scheme and explore the effectiveness of human and computer collaboration through game based experiments in solving hard optimization problems with a spatial objective in the context of post-disaster recovery.