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The intelligent Traffic Management System for Emergency Medical Service Station Location and Allocation of Ambulances
In the present study, the optimization of medical services considering the role of intelligent traffic management is of concern. In this regard, a two-objective mathematical model of a medical emergency system is assessed in order to determine the location of emergency stations and determine the req...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283018/ https://www.ncbi.nlm.nih.gov/pubmed/35845873 http://dx.doi.org/10.1155/2022/2340856 |
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author | Asgharizadeh, Ezzatollah Kadivar, Mahsa Noroozi, Mohammad Mottaghi, Vahid Mohammadi, Hamed Chobar, Adel Pourghader |
author_facet | Asgharizadeh, Ezzatollah Kadivar, Mahsa Noroozi, Mohammad Mottaghi, Vahid Mohammadi, Hamed Chobar, Adel Pourghader |
author_sort | Asgharizadeh, Ezzatollah |
collection | PubMed |
description | In the present study, the optimization of medical services considering the role of intelligent traffic management is of concern. In this regard, a two-objective mathematical model of a medical emergency system is assessed in order to determine the location of emergency stations and determine the required number of ambulances to be allocated to the station. The objective functions are the maximization of covering the emergency demands and minimization of total costs. Moreover, the use of an intelligent traffic management system to speed up the ambulance is addressed. In this regard, the proposed two-objective mathematical model has been formulated, and a robust counterpart formulation under uncertainty is applied. In the proposed method, the values of the objective function increase as the problem becomes wider and, with a slight difference in large dimensions, converge in terms of the solution. The numerical results indicate that, as the problem's complexity increases, the robust optimization method is still effective because, with the increasing complexity of the problem, it can still solve large-scale problems in a reasonable time. Moreover, the difference between the value of the objective function in the proposed method and the presence of uncertainty parameters is very small and, in large dimensions, is quite logical and negligible. The sensitivity analysis shows that, with increasing demand, both the number of ambulances required and the amount of objective function have increased significantly. |
format | Online Article Text |
id | pubmed-9283018 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-92830182022-07-15 The intelligent Traffic Management System for Emergency Medical Service Station Location and Allocation of Ambulances Asgharizadeh, Ezzatollah Kadivar, Mahsa Noroozi, Mohammad Mottaghi, Vahid Mohammadi, Hamed Chobar, Adel Pourghader Comput Intell Neurosci Research Article In the present study, the optimization of medical services considering the role of intelligent traffic management is of concern. In this regard, a two-objective mathematical model of a medical emergency system is assessed in order to determine the location of emergency stations and determine the required number of ambulances to be allocated to the station. The objective functions are the maximization of covering the emergency demands and minimization of total costs. Moreover, the use of an intelligent traffic management system to speed up the ambulance is addressed. In this regard, the proposed two-objective mathematical model has been formulated, and a robust counterpart formulation under uncertainty is applied. In the proposed method, the values of the objective function increase as the problem becomes wider and, with a slight difference in large dimensions, converge in terms of the solution. The numerical results indicate that, as the problem's complexity increases, the robust optimization method is still effective because, with the increasing complexity of the problem, it can still solve large-scale problems in a reasonable time. Moreover, the difference between the value of the objective function in the proposed method and the presence of uncertainty parameters is very small and, in large dimensions, is quite logical and negligible. The sensitivity analysis shows that, with increasing demand, both the number of ambulances required and the amount of objective function have increased significantly. Hindawi 2022-07-07 /pmc/articles/PMC9283018/ /pubmed/35845873 http://dx.doi.org/10.1155/2022/2340856 Text en Copyright © 2022 Ezzatollah Asgharizadeh et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Asgharizadeh, Ezzatollah Kadivar, Mahsa Noroozi, Mohammad Mottaghi, Vahid Mohammadi, Hamed Chobar, Adel Pourghader The intelligent Traffic Management System for Emergency Medical Service Station Location and Allocation of Ambulances |
title | The intelligent Traffic Management System for Emergency Medical Service Station Location and Allocation of Ambulances |
title_full | The intelligent Traffic Management System for Emergency Medical Service Station Location and Allocation of Ambulances |
title_fullStr | The intelligent Traffic Management System for Emergency Medical Service Station Location and Allocation of Ambulances |
title_full_unstemmed | The intelligent Traffic Management System for Emergency Medical Service Station Location and Allocation of Ambulances |
title_short | The intelligent Traffic Management System for Emergency Medical Service Station Location and Allocation of Ambulances |
title_sort | intelligent traffic management system for emergency medical service station location and allocation of ambulances |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283018/ https://www.ncbi.nlm.nih.gov/pubmed/35845873 http://dx.doi.org/10.1155/2022/2340856 |
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