Collaborative Research: Advancing Supply Prepositioning for Emergency Preparedness by Social Sensing

Foreseen disasters, such as hurricanes, snowstorms and droughts, can be detected several days prior to their landfall. To ameliorate the consequences of these disasters, humanitarian and governmental agencies can utilize forecast information to conduct preparedness activities. However, one unresolved challenge is to obtain accurate estimation of spatial-temporal heterogeneous supply demand for supply prepositioning. To tackle this challenge, this research plans an approach which integrates data from social media and simulation to predict demand for humanitarian supplies. Resulting models and prototype tools are expected to contribute to support for improving communication with the public, for optimizing supply prepositioning strategies, and for facilitating decision-making to help prevent or ameliorate the impacts of extreme disasters. This research will also lead to new educational modules and software useful for teaching undergraduate and graduate students how to design, analyze and implement a humanitarian logistics system. This research will investigate the potential for integrating social media data and agent-based simulation to reduce uncertainty associated with the demand for post-disaster relief supplies. This project will develop a new social sensing approach based on the integration of social media analysis and agent-based simulation. Trustworthy information will be extracted from social media by a new spatio-temporal Bayesian method. Second, this project will build a new forecast-driven multi-commodity supply prepositioning model that incorporates Markovian and decision theoretic perspectives within a stochastic optimization framework. Third, the outcome of the supply prepositioning will be realized by social delivery, an emerging delivery scheme that leverages delivery power from both fleet and volunteers' vehicles. Different optimization decomposition techniques will be examined and developed to handle the large-scale nature of the considered problem. To test the validity of the approaches, a range of case studies will be conducted.