Hierarchical Planning for Resource Allocation in Emergency Response Systems

TL;DR

This paper introduces a hierarchical planning framework for resource allocation in city-scale cyber-physical systems, specifically applied to emergency response, demonstrating improved performance over existing methods using real-world data.

Contribution

The paper presents a novel hierarchical approach that decomposes large resource allocation problems into smaller, manageable subproblems, enhancing scalability and effectiveness in emergency response systems.

Findings

Outperforms state-of-the-art emergency response methods

Successfully scales to large city-level problems

Validated with real-world data from Nashville, Tennessee

Abstract

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.