# Hierarchical Planning for Resource Allocation in Emergency Response Systems

arXiv: https://arxiv.org/abs/2012.13300 · HTML: https://mlsanity.com/paper/2012.13300/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2020.

## Built on by (most cited first, 9 of 9)

- [An Online Approach to Solve the Dynamic Vehicle Routing Problem with Stochastic Trip Requests for Paratransit Services](https://mlsanity.com/paper/2203.15127/) (2022)
- [ADVISER: AI-Driven Vaccination Intervention Optimiser for Increasing Vaccine Uptake in Nigeria](https://mlsanity.com/paper/2204.13663/) (2022)
- [Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems](https://mlsanity.com/paper/2106.08307/) (2021)
- [Dynamic Simplex: Balancing Safety and Performance in Autonomous Cyber Physical Systems](https://mlsanity.com/paper/2302.09750/) (2023)
- [Designing Decision Support Systems for Emergency Response: Challenges and Opportunities](https://mlsanity.com/paper/2202.11268/) (2022)
- [Artificial Intelligence for Emergency Response](https://mlsanity.com/paper/2306.10068/) (2023)
- [Fairguard: Harness Logic-based Fairness Rules in Smart Cities](https://mlsanity.com/paper/2302.11137/) (2023)
- [Semi-Markovian Planning to Coordinate Aerial and Maritime Medical Evacuation Platforms](https://mlsanity.com/paper/2410.04523/) (2024)
- [Toward Template-Free Explainability for Monte Carlo Tree Search](https://mlsanity.com/paper/2605.16524/) (2026)

## Abstract (arXiv)

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.