Location and relocation problems in the context of the emergency medical service systems: a case study

Abstract

In this paper, we address the dynamic emergency medical service (EMS) systems. A dynamic location model is presented for locating and relocating a fleet of ambulances. The proposed model can control the movements and locations of ambulances in order to provide a better coverage of the demand points. The model can keep this ability under different fluctuation patterns that may happen during a given period of time. A number of numerical experiments have been carried out by using some real-world data sets. They have been collected through the French EMS system at the Hospital Henri Mondor, France. Finally, we present a comparison between the results of the introduced model and the outputs of a classical EMS dynamic location model. According to the observations, the introduced model provides a better coverage of the EMS demands.

Relocalización de vehículos en servicios de emergencias médicas: una revisión

“…La investigación en el campo de los problemas de relocalización es reciente y hasta el momento teniendo en cuenta la forma como se aplique la política de relocalización se hace referencia a dos tipos de problemas de relocalización: dinámicos y multi-periodo [7], [18].…”

Optimization for the Locations of Ambulances under Two‐Stage Life Rescue in the Emergency Medical Service: A Case Study in Shanghai, China

“…The new ambulance station construction cost is high and the ambulance vehicles are expensive (Su et al [4]). Each service vehicle is completely equipped with all necessary emergency facilities that medical team may need in their missions (Moeini et al [5]). Hence, with a limited number of ambulance vehicles, we need to have a balance between the ambulance stations and the desired objective.…”

A new model for planning the distributed facilities locations under emergency conditions and uncertainty space in relief logistics

“…The 1970s decade can be considered the commencement of modeling of emergency facilities locating. Moeini et al (2015) proposed a model to detect the most emergency locations. Other used different objectives.…”