Optimization of an appointment scheduling problem for healthcare systems based on the quality of fairness service using whale optimization algorithm and NSGA-II.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
06 10 2021
Historique:
received: 04 08 2021
accepted: 08 09 2021
entrez: 7 10 2021
pubmed: 8 10 2021
medline: 31 12 2021
Statut: epublish

Résumé

Effective appointment scheduling (EAS) is essential for the quality and patient satisfaction in hospital management. Healthcare schedulers typically refer patients to a suitable period of service before the admission call closes. The appointment date can no longer be adjusted. This research presents the whale optimization algorithm (WOA) based on the Pareto archive and NSGA-II algorithm to solve the appointment scheduling model by considering the simulation approach. Based on these two algorithms, this paper has addressed the multi-criteria method in appointment scheduling. This paper computes WOA and NSGA with various hypotheses to meet the analysis and different factors related to patients in the hospital. In the last part of the model, this paper has analyzed NSGA and WOA with three cases. Fairness policy first come first serve (FCFS) considers the most priority factor to obtain from figure to strategies optimized solution for best satisfaction results. In the proposed NSGA, the FCFS approach and the WOA approach are contrasted. Numerical results indicate that both the FCFS and WOA approaches outperform the strategy optimized by the proposed algorithm.

Identifiants

pubmed: 34615890
doi: 10.1038/s41598-021-98851-7
pii: 10.1038/s41598-021-98851-7
pmc: PMC8494746
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

19816

Informations de copyright

© 2021. The Author(s).

Références

Artif Intell Med. 2010 Jan;48(1):61-70
pubmed: 19833489
Crit Care Med. 2018 Dec;46(12):1998-2009
pubmed: 30095499
Health Technol (Berl). 2021 Apr 10;:1-25
pubmed: 33868893

Auteurs

Ali Ala (A)

Department of Industrial Engineering & Management, Shanghai Jiao Tong University, Shanghai, 200240, China.

Fawaz E Alsaadi (FE)

Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

Mohsen Ahmadi (M)

Department of Industrial Engineering, Urmia University of Technology, Urmia, Iran.

Seyedali Mirjalili (S)

Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Brisbane, QLD, 4006, Australia. ali.mirjalili@torrens.edu.au.
Yonsei Frontier Lab, Yonsei University, Seoul, South Korea. ali.mirjalili@torrens.edu.au.

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Classifications MeSH