Geoffrey Pettet, Ayan Mukhopadhyay, Mykel Kochenderfer, Abhishek Dubey
Proceedings of the 12th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2021, Nashville, TN, USA 2021
Emergency response systems must allocate resources across large geographic areas while responding to random incident arrivals and uncertain response requirements. This work is innovative because it provides hierarchical planning approaches that exploit spatial structure to achieve scalability without requiring complete centralization. The framework demonstrates how to balance coordination needs with the benefits of decentralization.
This paper presents hierarchical planning approaches for dynamic resource allocation in emergency response systems under uncertainty. The framework decomposes the overall resource allocation problem into regional sub-problems to improve scalability while maintaining coordination across spatial areas. The approach integrates high-level planning with low-level local decision-making using both centralized and decentralized variants.
Evaluation on real emergency response data from major metropolitan areas demonstrated that the hierarchical approach scales significantly better than centralized approaches. The decentralized variant achieved comparable response times to centralized planning while requiring less global coordination. The framework successfully handled both static resource pre-positioning decisions and dynamic response to incident arrivals.
@inproceedings{iccps2021,
author = {Pettet, Geoffrey and Mukhopadhyay, Ayan and Kochenderfer, Mykel and Dubey, Abhishek},
booktitle = {Proceedings of the 12th {ACM/IEEE} International Conference on Cyber-Physical Systems, {ICCPS} 2021, Nashville, TN, USA},
title = {Hierarchical Planning for Resource Allocation in Emergency Response Systems},
year = {2021},
acceptance = {26},
abstract = {A classical problem in city-scale cyber-physical systems (CPS) is resource allocation under uncertainty. Spatial-temporal allocation of resources is optimized to allocate electric scooters across urban areas, place charging stations for vehicles, and design efficient on-demand transit. Typically, such problems are modeled as Markov (or semi-Markov) decision processes. While online, offline, and decentralized methodologies have been used to tackle such problems, none of the approaches scale well for large-scale decision problems. We create a general approach to hierarchical planning that leverages structure in city-level CPS problems to tackle resource allocation under uncertainty. We use 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 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. },
contribution = {lead},
keywords = {emergency response, resource allocation, hierarchical planning, cyber-physical systems, scalability},
project = {smart-cities,smart-emergency-response},
tag = {ai4cps,decentralization,incident}
}