The transportation group also conducts path-breaking research on humanitarian logistics, an area of profound human impact, to determine the most effective ways to conduct post-disaster humanitarian logistics. The research on humanitarian logistics taps into the knowledge of social sciences to build more realistic mathematical models of humanitarian logistics The transportation group has pioneered the multidisciplinary study of post-disaster humanitarian logistic operations, and has: identified the key lessons learned from the response to the largest disasters of recent times; translated these lessons into actionable policy recommendations; and shared these suggestions with disaster response agencies. As part of the field work, his research group has conducted detailed analyses of the most prominent disasters of recent times, including: Hurricane Katrina, the Port-au-Prince earthquake, the tornadoes in Joplin and Alabama, Hurricane Irene, and the Tohoku disasters in Japan.

Cyber Enabled Discovery System for Advanced Multidisciplinary Study of Humanitarian Logistics for Disaster Response

This project is concerned with the development of an integrative “Cyber Enabled Discovery System for Advanced Multidisciplinary Study of Humanitarian Logistics for Disaster Response.” As part of the work, transportation, computer, mathematical, and social scientists will collaborate to:

  1. create new paradigms of humanitarian logistic (HL) models that explicitly consider two key aspects not studied by current techniques: deprivation costs (DC), and material convergence (MC);
  2. develop appropriate models to represent human suffering as a DC;
  3. explicitly consider DC in the key HL decision models;
  4. develop analytical models to quantify/influence the amount, type, and arrival patterns of donations;
  5. gain insight into the links between media framing of needs and MC;
  6. define mechanisms to modify donor behavior; and,
  7. develop algorithms and heuristics to solve the formulations developed.

Remote Sensing Decision Support System for Optimal Access Restoration in Post Disaster Environments

The project’s main goal is to develop a state-of-the-art Decision Support System (DSS) that, using network condition and disaster impact assessments provided by Commercial Remote Sensing (CRS), will compute optimal Access Restoration Plans (ARP). This will help responders optimally use their scarce resources to orchestrate the road openings, road repairs, and other similar actions; and subdivide the work so that multiple groups could seamlessly cooperate to reach maximum effectiveness. The DSS will process multi-modal temporal data feeds (GIS, social media feeds, etc.) to update estimates of disaster impacts, and will use modern optimization procedures to update the ARP as new data become available. To achieve the overall goal, the team will collaborate to:

  1. Develop CRS techniques that produce rapid assessments of network conditions and disaster impacts integrating multi-modal / multi temporal data
  2. Develop mathematical models to produce ARP using appropriate priority metrics
  3. Create a DSS that is smoothly transitioned to practice, fully validated, and useful to responders.
  4. Develop appropriate procedures for integration of outside assistance.