The intelligent Traffic Management System for Emergency Medical Service Station Location and Allocation of Ambulances.


Journal

Computational intelligence and neuroscience
ISSN: 1687-5273
Titre abrégé: Comput Intell Neurosci
Pays: United States
ID NLM: 101279357

Informations de publication

Date de publication:
2022
Historique:
received: 22 04 2022
revised: 10 06 2022
accepted: 16 06 2022
entrez: 18 7 2022
pubmed: 19 7 2022
medline: 20 7 2022
Statut: epublish

Résumé

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.

Identifiants

pubmed: 35845873
doi: 10.1155/2022/2340856
pmc: PMC9283018
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

2340856

Informations de copyright

Copyright © 2022 Ezzatollah Asgharizadeh et al.

Déclaration de conflit d'intérêts

The authors declare that they have no conflicts of interest.

Références

Prehosp Disaster Med. 2003 Jan-Mar;18(1):29-35; discussion 35-7
pubmed: 14694898
Environ Sci Pollut Res Int. 2021 Jan 21;:
pubmed: 33474670
Eng Appl Artif Intell. 2021 Apr;100:104188
pubmed: 33619424
PLoS One. 2022 Mar 8;17(3):e0264186
pubmed: 35259170

Auteurs

Ezzatollah Asgharizadeh (E)

Department of Industrial Management, University of Tehran, Tehran, Iran.

Mahsa Kadivar (M)

Department of Business Management, Payame Noor University, Varamin Branch, Tehran, Iran.

Mohammad Noroozi (M)

Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran.

Vahid Mottaghi (V)

Department of IT Management, Qeshm Branch, Islamic Azad University, Qeshm, Iran.

Hamed Mohammadi (H)

Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran.

Adel Pourghader Chobar (AP)

Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

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Classifications MeSH