Real-time ambulance relocation: Assessing real-time redeployment strategies for ambulance relocation

Abstract

Providers of Emergency Medical Services (EMS) are typically concerned with keeping response times short. A powerful means to ensure this, is to dynamically redistribute the ambulances over the region, depending on the current state of the system. In this paper, we provide new insight in how to optimally (re)distribute ambulances. We study the impact of (1) the frequency of redeployment decision moments, (2) the inclusion of busy ambulances in the state description of the system, and (3) the performance criterion on the quality of the distribution strategy. In addition, we consider the influence of the EMS crew workload, such as (4) chain relocations and (5) time bounds, on the execution of an ambulance relocation. To this end, we use trace-driven simulations based on a real-life dataset of ambulance providers in the Netherlands. In doing so, we differentiate between rural and urban regions, which typically face different challenges when it comes to EMS. Our results show that: (1) taking the classical 0-1 performance criterion for assessing the fraction late arrivals only differs slightly from taking expert-opinion based S-curve for evaluating *

An online real-time matheuristic algorithm for dispatch and relocation of ambulances

“…The literature on the dynamic ambulance operation management could be divided by their orientation into three categories (van Barneveld et al, 2018): periodic redeployment as well as offline and online real-time ambulance management. However, it must be noted that many of these works are based on developments in the classic ambulance location problem.…”

“…Instead, the authors allow for several movements; for example, the ambulance that is at the point of origin can be moved to an intermediate location while, at the same time, the ambulance at the intermediate location moves to the destination. These movements are computed by a mathematical model of the Linear Bottleneck Assignment Problem and later are defined as chain relocations (van Barneveld et al, 2018). The tool was validated using real data and different cost functions for the computation of the unpreparedness index.…”

“…Van Barneveld et al (2018) improved the algorithm proposed by Jagtenberg et al (2015) by using the characteristics of the work of van Barneveld et al (2016). The effects of incorporating one or several of those characteristics in the new algorithm in both rural and urban areas were studied.…”

Compliance tables for an EMS system with two types of medical response units

“…Moreover, in order to get a more realistic idea about the effect of applying relocation policies, such as compliance tables, it is of utmost importance to perform simulation experiments, as stated by Van Barneveld et al [32] . Although objective values in a mathematical model serve as an approximation of the performance of the EMS system, ambulance service providers are far more interested in the relocation policy itself rather than in theoretically computed numbers.…”

Ambulance Service for Hospital Selection: Optimization With Discrete-Event Simulation Application for Yozgat Province of Turkey

“…The world-renowned ambulance service system is perceived as a healthcare unit that provides patients with medical assistance and the fastest access to the hospital. The ambulances must reach the patient locations (or emergency areas) and the hospitals in a short time [1]. Therefore, it is an action planned to be deployed where ambulance locations are likely to experience an emergency [2].…”