Policies for the Operation of an Ambulance Fleet under Uncertainty based on a New Preparedness Metric

TL;DR

This paper introduces a new preparedness metric for ambulance fleet management under uncertainty, enabling optimized decision-making for ambulance selection and reassignment to improve emergency response capabilities.

Contribution

It proposes a novel preparedness metric and an optimization-based decision framework, validated with real city data, outperforming existing methods.

Findings

The new metric effectively quantifies emergency response preparedness.

The proposed method outperforms 9 existing approaches in simulations.

Decision-making based on the metric improves ambulance response times.

Abstract

Two important decisions in the management of an ambulance fleet are ambulance selection decisions and ambulance reassignment decisions. Ambulance selection decisions determine what to do when an emergency call arrives (such as choosing what ambulance to dispatch to the emergency or putting the emergency in a queue of emergencies waiting for an ambulance to be dispatched). Ambulance reassignment decisions determine where to send an ambulance next when it has finished service for an emergency. Making good ambulance selection decisions and ambulance reassignment decisions is challenging because a decision made at a point in time affects the ability of the emergency medical service to respond to future emergencies (that are typically not known when the decision is made). We propose a new preparedness metric that quantifies the ability of the emergency medical service to respond to future emergencies. The preparedness metric can be used to make ambulance selection decisions and ambulance reassignment decisions by solving a tractable optimization problem each time that a decision has to be made. We compare the performance of the resulting method with 9 methods that have been proposed in the literature, based on data from a real emergency medical service for a large city.