The multiarmed bandit problem (MAB) is a classic problem in which a finite amount of
resources must be allocated among competing choices with the aim of identifying a policy …

In classical multiarmed bandit problem, the aim is to find a policy maximizing the expected
total reward, implicitly assuming that the decision-maker is risk-neutral. On the other hand …

Proactive ambulance management is constructive to improve the response efficiency for
emergency medical service (EMS) systems under uncertainty. In this paper, we present a …

This study aims to construct an efficient heuristic, referred to as RA, for a riskaverse
Markovian multi-armed bandit problem (MAB) with multiple plays. The RA incorporates risk …

Improving a country's emergency medical services results in serving more calls on time and
saving more lives in return. The ambulance redeployment problem, which is a part of the …

In this study, ambulance redeployment is performed by using reinforcement learning
methods. The objective in the ambulance redeployment problem is to redeploy the limited …