A Markov Chain Model for an EMS System with Repositioning

Alanis, Ramon; Ingolfsson, Armann; Kolfal, Bora

doi:10.1111/j.1937-5956.2012.01362.x

Cited by 98 publications

(99 citation statements)

References 25 publications

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“…This shifted focus of research was accompanied with an increasing number of EMS systems using a dynamic allocation of vehicles to bases. Surveys of North American EMS operators showed that the percentage of operators who used a dynamic strategy increased from 23% in 2001 [3] to 37% in 2009 [19] (see also [1]). This indicates that the EMS community is becoming more aware that a dynamic policy can help them achieve greater service without increasing capacity.…”

Section: Dynamic Modelsmentioning

confidence: 99%

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An efficient heuristic for real-time ambulance redeployment

Jagtenberg

Bhulai

Mei

2015

Operations Research for Health Care

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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.

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Section: Dynamic Modelsmentioning

confidence: 99%

“…The models in this class are typically looking for an optimal configuration for each number of available ambulances. A recent example can be found in [1]. The job of steering the set of available vehicles towards this configuration is usually left to the dispatchers.…”

Section: Dynamic Modelsmentioning

confidence: 99%

An efficient heuristic for real-time ambulance redeployment

Jagtenberg

Bhulai

Mei

2015

Operations Research for Health Care

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“…The goal in their proposed approach is to position an entire fleet of ambulances to base locations in order to maximize the service level/quality if the emergency medical services system. A two-dimensional Markov chain model of an emergency medical services system is presented by Alanis et al [34]. This model relocates ambulances using a compliance table policy.…”

Section: Literature Reviewmentioning

confidence: 99%

An Optimization Model for Emergency Vehicle Location and Relocation with Consideration of Unavailability Time

2017

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The main purpose of emergency medical services is providing fast medical care, as well as transporting patients to the hospital, in the shortest possible time. Healthcare managers try to improve healthcare systems through reducing the response to demand time. In this paper, we seek to propose an optimization model in order to cover as much demand as possible in the shortest possible time using the available ambulance fleet. To do so, considering the response and service time, amount of demand during the time periods, limitation in the number of available emergency vehicles and the capacity of ambulance stations, we have proposed a mixed integer linear programming optimization model, aiming to minimize the total response time. In this paper we take into account fleet relocation and unavailability time, the time interval in which the vehicle is on its way or doing a service at a demand point. Then, a sensitivity analysis is conducted on the model by manipulating the parameters, so as to observe the effects on the outputs. In order to evaluate the model, several artificial test problems were generated and solved. The results depict the capability of the proposed model in dealing with emergency cases.

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“…There has been much research into different aspects of ambulance response time that use a graph to represent the road network. These have considered how many ambulances to use, the best locations for bases and how to distribute vehicles over bases [1,4,6,10,15,16]. The specific system modelled here has been chosen because of its time-based performance evaluation aspects and straightforward heuristic function.…”

Section: Introductionmentioning

confidence: 99%

Modelling Ambulance Deployment with CarmaCARMA

Galpin

2016

Lecture Notes in Computer Science

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Abstract. CARMA is process-algebra influenced language for the quantitative modelling of collective adaptive systems which involve collaboration and coordination. These systems consist of multiple components that interact to achieve certain goals and that adapt to changes in the environment. As a case study for the application of CARMA, this paper presents an ambulance deployment system where ambulances go to medical incidents and either treat patients at the scene or transfer them to hospital. The Eclipse CARMA Plug-in is used to simulate the system, and demonstrate its behaviour in different circumstances.

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A Markov Chain Model for an EMS System with Repositioning

Cited by 98 publications

References 25 publications

An efficient heuristic for real-time ambulance redeployment

An efficient heuristic for real-time ambulance redeployment

An Optimization Model for Emergency Vehicle Location and Relocation with Consideration of Unavailability Time

Modelling Ambulance Deployment with CarmaCARMA

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