Abstract
In this paper, an analytic review of the recent methodologies tackling the problem of dynamic allocation of ambulances was carried out. Considering that state-of-the-art is moving to deal with more extensive and dynamic problems to address in a better way real-life instances, this research looks to identify the evolution and recent applications of this kind of problem once the basic models are explored. This extensive review allowed us to identify the most recent developments in this problem and the most critical gaps to be addressed. In this sense, it is essential to point out that the dynamic location of emergency medical services (EMS) is nowadays a relevant topic considering its impact on the healthcare system outcomes. Issues related to forecasting, simulation, heterogeneous fleets, robustness, and solution speed for real-life problems, stand out in the identified gaps. Applications of machine learning the deployment challenges during epidemic outbreaks such as SARS and COVID-19 were also explored. At the same time, a proposed notation tries to tackle the fact that the word problem in this kind of work refers to a model on many occasions. The proposed notation eases the comparison between the different model proposals found in the literature.
Highlights
Academic Editor: Wolfgang KainzDiseases such as stroke and heart arrest result in high costs to the economy alongside prolonged periods of stay in clinics, hospitals, and care homes
We constructed the query strings; the second stage focused on gathering potential results in the Web of Science (WoS) database; the third focused on excluding and including results based on criteria
Toregas [23] proposed an integer linear formulation for emergency medical services (EMS) location based on the principle that EMS should meet a response time threshold value to maximize the survival probability of injured people. The objective of this formulation was to minimize the number of facilities required to meet the demand within the response time threshold value. This model is known as the Location Set Covering Problem (LSCP)
Summary
Diseases such as stroke and heart arrest result in high costs to the economy alongside prolonged periods of stay in clinics, hospitals, and care homes. This situation has turned the global attention to healthcare systems, considering that its outcome has a significant impact on the quality of life of people, a nation’s productivity, and a nation’s finances [1]. Ambulances’ fast response is a crucial factor in the good outcomes in ER, impacting both the probability of survival and having disabilities [13], making it essential for all countries [14,15,16,17] In this sense, different reviews have been undertaken previously.
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