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Title: Hierarchical planning for resource allocation in emergency response systems
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.  more » « less
Award ID(s):
1814958 1640624
NSF-PAR ID:
10247921
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
12th ACM/IEEE International Conference on Cyber-Physical Systems,
Page Range / eLocation ID:
155 to 166
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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