Decision support for emergency response in interdependent infrastructure systems

In recent years, extreme events, such as hurricanes, earthquakes, floods and fires, occur more frequently and at a higher intensity. The growing complexity and interdependence of modern infrastructure systems makes them vulnerable to such events. Emergency response is the process of implementing appropriate actions to reduce human and economic losses following these events. Efficient response requires an understanding of the existing infrastructure systems and their interdependencies. In this thesis, we propose a decision support system for helping emergency responders in making efficient decisions during extreme events. Fires are chosen as an example of the extreme events and firefighting operations as the emergency response to these events. Everyday, fire managers are faced with making increasingly complex manpower decisions; trying to minimize costs and risk levels. The effectiveness of firefighting operations is crucial in minimizing both cost of suppression and economic losses. The contributions of this thesis focus on two levels of fire management plans: operational and strategic. We first develop a methodology to optimize the allocation process of firefighting resources in multiple-fire incidents. The developed methodology employs reinforcement learning, a machine learning algorithm that optimizes the allocation of firefighting units to minimize the total economic losses in the long run. To consider the concept of infrastructure interdependencies in evaluating the economic impact of the incidents, we model a large petrochemical complex using the Infrastructure Interdependency Simulator (i2Sim). In addition, a capacity planning methodology is developed to investigate the impact of manpower investment on the effectiveness of firefighting operations. The