A simulation study to improve the performance of an emergency medical service: Application to the French Val-de-Marne department

Aboueljinane, Lina; Şahin, Evren; Jemaı̈, Zied; Marty, J.

doi:10.1016/j.simpat.2014.05.007

Cited by 53 publications

(46 citation statements)

References 29 publications

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“…Sometimes simulation tools are used to validate models. An extensive review on simulation models in EMS can be found in Aboueljinane (2014) [1]. DAM relocates available ambulances in such a way that the regions coverage stays optimal.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Buuren

Mei

Bhulai

2018

Operations Research for Health Care

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Governments deal with increasing health care demand and costs, while budgets are tightened. At the same time, ambulance providers are expected to deliver high-quality service at affordable cost. Maximum reliability and minimal availability models guarantee a minimal performance level at each demand point, in contrast to the majority of facility location and allocation methods that guarantee a minimal performance that is aggregated over the entire ambulance region. As a consequence, existing models generally lead to overstaffing, particularly in 'mixed' regions with both urban and rural areas, which leads to unnecessarily high costs. This paper addresses this problem. First, we introduce the concept of demand projection to give fundamental insight into why this overstaffing takes place. Next, we overcome the overstaffing by the so-called adjusted queuing (AQ) solution that provides generalizations of the existing models. We provide mathematical proofs for the correctness of the AQ solution. Finally, to assess the performance of the AQsolution we have performed extensive numerical experimentation, using real data from four ambulance regions in the Netherlands. The results show that in all cases the AQ-solution indeed leads to better ambulance care than the existing solutions, while reducing staffing cost.

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Section: Literature Reviewmentioning

confidence: 99%

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

Buuren

Mei

Bhulai

2018

Operations Research for Health Care

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“…Aboueljinane et al (2012) utilize DES to analyze seven scenarios, which comprises dynamic changes in existing Val-de-Marne department's emergency medical service with the aim of improving response time and compare results with the original organization. In a different study, Aboueljinane et al (2014) apply DES to improve the process of the same system, which aims to respond calls in 20 min, with five different strategies. Similarly, Nogueira et al (2016) devote their efforts to minimize response times of emergency medical service (EMS) with DES.…”

Section: Related Workmentioning

confidence: 99%

An ILP and simulation model to optimize search and rescue helicopter operations

Karataş

Razi

Günal

2017

Journal of the Operational Research Society

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Maritime search and rescue (SAR) operations, conducted for rendering aid to the victims in need of help at sea, play a crucial role in dropping the number of causalities. Therefore, it is of high importance to organize SAR operations properly. In this paper, we compose a hybrid methodology which combines optimization and simulation to allocate SAR helicopters. First, we build an integer linear programming (ILP) model to provide an effective deployment plan and use it as an input to a simulation model which includes constraints that the ILP model cannot tackle. Next, using a rule-based algorithm, we generate alternative solutions and seek better plans that exist in the vicinity of the ILP model solution. We perform our methodology on the historical incident data in the Aegean Sea region. Results show that the hybrid methodology we adopted leads to a more effective utilization of resources than the optimization model alone.

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“…Por otro lado, en los problemas de relocalización multi-periodo además de aplicar políticas de relocalización cuando hay una variación en el tamaño de la flota, también se aplican políticas de relocalización teniendo en cuenta factores externos al SEM, como son: las variaciones de la velocidad en el trascurso del día, las actividades que se realizan durante el día (horario laboral vs horario de descanso), la semana (días de semana vs fines de semana), el año (días laborales vs días festivos) y el comportamiento del tráfico durante horas pico y no pico [1], [29], [30], [31], [20], [15]. Aunque se ha reportado que se obtienen mejores resultados cuando se estudian los problemas de relocalización de forma multi-periodo [15], [22], la mayor parte de los estudios revisados consideran los problemas de relocalización de forma dinámica.…”

Section: Clasificación De Los Problemas De Relocalizaciónunclassified

Relocalización de vehículos en servicios de emergencias médicas: una revisión

2016

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ResumenEste artículo presenta una revisión de la literatura de los estudios realizados sobre problemas de relocalización de vehículos de Servicios de Emergencia Médica, (SEM). En particular se presenta una descripción básica de las actividades que se realizan en un SEM, la forma como son clasificados este tipo de problemas, los principales componentes que se tienen en cuenta en el diseño de los modelos, los métodos de solución que se han implementado a la fecha y las medidas de desempeño para evaluar las soluciones. Además se discuten los softwares utilizados para su desarrollo, las estrategias para la recolección de los datos que se tienen en cuenta y las técnicas de verificación y validación. Finalmente, se presentan las políticas de relocalización más evaluadas e implementadas.Palabras clave: servicios de emergencia médicos; relocalización; optimización; simulación de eventos discretos.

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A simulation study to improve the performance of an emergency medical service: Application to the French Val-de-Marne department

Cited by 53 publications

References 29 publications

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

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

An ILP and simulation model to optimize search and rescue helicopter operations

Relocalización de vehículos en servicios de emergencias médicas: una revisión

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