An efficient heuristic for real-time ambulance redeployment

Abstract

We address the problem of dynamic ambulance repositioning, in which the goal is to minimize the expected fraction of late arrivals. The decisions on how to redeploy the vehicles have to be made in real time, and may take into account the status of all other vehicles and accidents. This is generally considered a difficult problem, especially in urban areas, and exact solution methods quickly become intractable when the number of vehicles grows. Therefore, there is a need for a scalable algorithm that performs well in practice.We propose a polynomial-time heuristic that distinguishes itself by requiring neither assumptions on the region nor extensive state information. We evaluate its performance in a simulation model of emergency medical services (EMS) operations. We compare the performance of our repositioning method to so-called static solutions: a classical scenario in which an idle vehicle is always sent to its predefined base location. We show that the heuristic performs better than the optimal static solution for a tractable problem instance. Moreover, we perform a realistic urban case study in which we show that the performance of our heuristic is a 16.8% relative improvement on a benchmark static solution. The studied problem instances show that our algorithm fulfils the need for real-time, simple redeployment policies that significantly outperform static policies.

Demand-point constrained EMS vehicle allocation problems for regions with both urban and rural areas

“…At the operational level dynamic ambulance management (DAM) is widely used [15,19,35,38,33,34,42,2,24,46,47]. Sometimes simulation tools are used to validate models.…”

Modelling Ambulance Deployment with CarmaCARMA

“…This section describes the mathematical model that Jagtenberg et al [14] propose, together with their heuristic for best real-time redeployment of ambulances. This model considers a scenario where there is a fairly sparse network of roads between cities and towns (as shown in Figure 5), and hence is slightly more appropriate for a non-urban situation, where there are not many routes between each point of interest.…”

“…The proportion of ambulances that do not reach the incident within a fixed time period (denoted T ) after being allocated to that incident, is often used [14] and will be used here. In the case of static deployment where each ambulance has a fixed base, simulation can be used to assess the best distribution of ambulances, in the planning stages of a system.…”

“…Different variants of the maximum expected covering location problem (MEX-CLP) [6] have been used to tackle this task. The disadvantage of the static approach is that it ignores real-time information that can be used to provide better coverage [14].…”

“…Jagtenberg et al have proposed a new approach to ambulance deployment [14]. The general goal of such systems is to minimise the time it takes to respond to medical incidents by ensuring good base locations for ambulances together with a distribution of ambulances over bases that leads to fast response.…”