A new research paper entitled “A Framework to Integrate Social Media and Authoritative Data for Disaster Relief Detection and Distribution Optimization“ has been published in the International Journal of Disaster Risk Reduction. In this paper, the BDEM researchers propose an interdisciplinary approach to (natural) disaster relief management. Our framework combines dynamic and static databases, which consist of social media and authoritative data of an afflicted region, respectively, to model rescue demand during a disaster situation. Using Global Particle Swarm Optimization and Mixed-Integer Linear Programming, we then determine the optimal amount and locations of temporal rescue centers. Furthermore, our disaster relief system identifies an efficient distribution of supplies between hospitals and rescue centers and rescue demand points. By leveraging the temporal dimension of the social media data, our framework manages to iteratively optimize the disaster relief distribution.
It is a joint work of Timothy Schempp (CICS, San Diego State University), Haoran Zhang (The University of Tokyo), Alexander Schmidt (Uni Hohenheim), Minsung Hong and Rajendra Akerkar (Vestlandsforsking).
Full article can be found at the journal website.