Integrating data-driven forecasting and large-scale optimization to improve humanitarian response planning and preparedness
This dissertation investigates the advantages of optimization and machine learning algorithms to characterize, predict, and solve Response Planning and Preparedness problems in large-scale humanitarian organizations. Organizations often base their operational decisions on the staff's experienti...
| Published: |
|
|---|---|
| Online Access: | http://hdl.handle.net/2047/D20328704 |