Hierarchical Planning for Resource Allocation in Emergency Response Systems
This work provides a more scalable solution for resource allocation in emergency response systems, which could lead to improved efficiency and outcomes for city planners and first responders.
This paper addresses the challenge of resource allocation under uncertainty in city-scale cyber-physical systems, specifically for emergency response. The authors propose a hierarchical planning approach that decomposes large problems into smaller, manageable ones, demonstrating superior performance over existing state-of-the-art methods.
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.