Adaptive ambulance redeployment via multi-armed bandits
Author
Şahin, Ümitcan
Advisor
Tekin, Cem
Date
2019-09Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
Emergency Medical Services (EMS) provide the necessary resources when there
is a need for immediate medical attention and play a signi cant role in saving
lives in the case of a life-threatening event. Therefore, it is necessary to design an
EMS system where the arrival times to calls are as short as possible. This task
includes the ambulance redeployment problem that consists of the methods of deploying
ambulances to certain locations in order to minimize the arrival time and
increase the coverage of the demand points. As opposed to many conventional
redeployment methods where the optimization is primary concern, we propose
a learning-based approach in which ambulances are redeployed without any a
priori knowledge on the call distributions and the travel times, and these uncertainties
are learned on the way. We cast the ambulance redeployment problem
as a multi-armed bandit (MAB) problem, and propose various context-free and
contextual MAB algorithms that learn to optimize redeployment locations via
exploration and exploitation. We investigate the concept of risk aversion in ambulance
redeployment and propose a risk-averse MAB algorithm. We construct a
data-driven simulator that consists of a graph-based redeployment network and
Markov tra c model and compare the performances of the algorithms on this
simulator. Furthermore, we also conduct more realistic simulations by modeling
the city of Ankara, Turkey and running the algorithms in this new model. Our
results show that given the same conditions the presented MAB algorithms perform
favorably against a method based on dynamic redeployment and similarly
to a static allocation method which knows the true dynamics of the simulation
setup beforehand.
Keywords
Ambulance redeploymentOnline learning
Multi-armed bandit problem
Contextual multi-armed bandit problem
Risk-aversion