Hierarchical Planning for Resource Allocation in Emergency Response
  Systems
  A classical problem in city-scale cyber-physical systems (CPS) is resource
allocation under uncertainty. Typically, such problems are modeled as Markov
(or semi-Markov) decision processes. While online, offline, and decentralized
approaches have been applied to such problems, they have difficulty scaling to
large decision problems. We present a general approach to hierarchical planning
that leverages structure in city-level CPS problems for resource allocation
under uncertainty. We use the emergency response as a case study and show how a
large resource allocation problem can be split into smaller problems. We then
create a principled framework for solving the smaller problems and tackling the
interaction between them. Finally, we use real-world data from Nashville,
Tennessee, a major metropolitan area in the United States, to validate our
approach. Our experiments show that the proposed approach outperforms
state-of-the-art approaches used in the field of emergency response.
