Heavy equipment distribution framework for urban search and rescue operations and critical infrastructure systems restoration in disaster response

摘要

During disaster response, it is imperative to timely provide the rescuers with the adequate equipment to facilitate lifesaving operations and restoration of critical infrastructures. How- ever, management of high demand equipment is usually inefficient during disaster response.Prioritization of limited resources is one of the greatest challenges in decision making for facilities management. Meanwhile, management of geographically distributed resources has been recognized as one of the most important but difficult tasks in large scale disasters. When disasters occur, resources outside of the disaster affected zone converge into the area to assist the response operations. This is known as the convergence phenomenon that often makes the already complex task of resource coordination even more challenging. Although there are difficulties on managing the converging volunteers and organizations, due to their ability to be deployed immediately without the appropriate skills and training, construction equipment and its operators are specialized professional entities that have great potential to be deployed immediately. The effectiveness of their deployment in the disaster response operations could be improved through decentralization of decision making. Through decen- tralization, the convergence of resources could potentially be mitigated and the coordination of heavy construction equipment could be more efficiently utilized for lifesaving operations.This doctoral dissertation investigates and implements Information Technology (IT) approaches to facilitate decision making for lifesaving operations. IT systems for resource inventory, disaster condition reporting, structural integrity assessment and resource requests strengthen situational awareness of response agencies. With the collected information, deci- sion making for heavy equipment coordination is proposed. The challenges, implementation, and results are presented for a centralized truck scheduling and a decentralized truck coor- dination. The dissertation concludes with the contribution and future recommendations.