  Disaster response is critical to save lives and reduce damages in the
aftermath of a disaster. Fundamental to disaster response operations is the
management of disaster relief resources. To this end, a local agency (e.g., a
local emergency resource distribution center) collects demands from local
communities affected by a disaster, dispatches available resources to meet the
demands, and requests more resources from a central emergency management agency
(e.g., Federal Emergency Management Agency in the U.S.). Prior resource
management research for disaster response overlooks the problem of deciding
optimal quantities of resources requested by a local agency. In response to
this research gap, we define a new resource management problem that proactively
decides optimal quantities of requested resources by considering both currently
unfulfilled demands and future demands. To solve the problem, we take salient
characteristics of the problem into consideration and develop a novel deep
learning method for future demand prediction. We then formulate the problem as
a stochastic optimization model, analyze key properties of the model, and
propose an effective solution method to the problem based on the analyzed
properties. We demonstrate the superior performance of our method over
prevalent existing methods using both real world and simulated data. We also
show its superiority over prevalent existing methods in a multi-stakeholder and
multi-objective setting through simulations.
