Dynamic Optimization of Drone Dispatch for Substance Overdose Rescue

Xiaoquan Gao, Nan Kong, Paul M. Griffin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Opioid overdose rescue is very time-sensitive. Hence, drone-delivered naloxone has the potential to be a transformative innovation due to its easily deployable and flexible nature. We formulate a Markov Decision Process (MDP) model to dispatch the appropriate drone after an overdose request arrives and to relocate the drone to its next waiting location after having completed its current task. Since the underlying optimization problem is subject to the curse of dimensionality, we solve it using ad-hoc state aggregation and evaluate it through a simulation with higher granularity. Our simulation-based comparative study is based on emergency medical service data from the state of Indiana. We compare the optimal policy resulting from the scaled-down MDP model with a myopic policy as the baseline. We consider the impact of drone type and service area type on outcomes, which offers insights into the performance of the MDP suboptimal policy under various settings.

Original languageEnglish (US)
Title of host publicationProceedings of the 2020 Winter Simulation Conference, WSC 2020
EditorsK.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, R. Thiesing
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages830-841
Number of pages12
ISBN (Electronic)9781728194998
DOIs
StatePublished - Dec 14 2020
Event2020 Winter Simulation Conference, WSC 2020 - Orlando, United States
Duration: Dec 14 2020Dec 18 2020

Publication series

NameProceedings - Winter Simulation Conference
Volume2020-December
ISSN (Print)0891-7736

Conference

Conference2020 Winter Simulation Conference, WSC 2020
CountryUnited States
CityOrlando
Period12/14/2012/18/20

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Computer Science Applications

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