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A Hybrid Genetic Algorithm for Nurse Scheduling Problem considering the Fatigue Factor

Nowadays and due to the pandemic of COVID-19, nurses are working under the highest pressure benevolently all over the world. This urgent situation can cause more fatigue for nurses who are responsible for taking care of COVID-19 patients 24 hours a day. Therefore, nurse scheduling should be modified...

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Detalles Bibliográficos
Autores principales: Amindoust, Atefeh, Asadpour, Milad, Shirmohammadi, Samineh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8034424/
https://www.ncbi.nlm.nih.gov/pubmed/33868622
http://dx.doi.org/10.1155/2021/5563651
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author Amindoust, Atefeh
Asadpour, Milad
Shirmohammadi, Samineh
author_facet Amindoust, Atefeh
Asadpour, Milad
Shirmohammadi, Samineh
author_sort Amindoust, Atefeh
collection PubMed
description Nowadays and due to the pandemic of COVID-19, nurses are working under the highest pressure benevolently all over the world. This urgent situation can cause more fatigue for nurses who are responsible for taking care of COVID-19 patients 24 hours a day. Therefore, nurse scheduling should be modified with respect to this new situation. The purpose of the present research is to propose a new mathematical model for Nurse Scheduling Problem (NSP) considering the fatigue factor. To solve the proposed model, a hybrid Genetic Algorithm (GA) has been developed to provide a nurse schedule for all three shifts of a day. To validate the proposed approach, a randomly generated problem has been solved. In addition, to show the applicability of the proposed approach in real situations, the model has been solved for a real case study, a department in one of the hospitals in Esfahan, Iran, where COVID-19 patients are hospitalized. Consequently, a nurse schedule for May has been provided applying the proposed model, and the results approve its superiority in comparison with the manual schedule that is currently used in the department. To the best of our knowledge, it is the first study in which the proposed model takes the fatigue of nurses into account and provides a schedule based on it.
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spelling pubmed-80344242021-04-16 A Hybrid Genetic Algorithm for Nurse Scheduling Problem considering the Fatigue Factor Amindoust, Atefeh Asadpour, Milad Shirmohammadi, Samineh J Healthc Eng Research Article Nowadays and due to the pandemic of COVID-19, nurses are working under the highest pressure benevolently all over the world. This urgent situation can cause more fatigue for nurses who are responsible for taking care of COVID-19 patients 24 hours a day. Therefore, nurse scheduling should be modified with respect to this new situation. The purpose of the present research is to propose a new mathematical model for Nurse Scheduling Problem (NSP) considering the fatigue factor. To solve the proposed model, a hybrid Genetic Algorithm (GA) has been developed to provide a nurse schedule for all three shifts of a day. To validate the proposed approach, a randomly generated problem has been solved. In addition, to show the applicability of the proposed approach in real situations, the model has been solved for a real case study, a department in one of the hospitals in Esfahan, Iran, where COVID-19 patients are hospitalized. Consequently, a nurse schedule for May has been provided applying the proposed model, and the results approve its superiority in comparison with the manual schedule that is currently used in the department. To the best of our knowledge, it is the first study in which the proposed model takes the fatigue of nurses into account and provides a schedule based on it. Hindawi 2021-03-31 /pmc/articles/PMC8034424/ /pubmed/33868622 http://dx.doi.org/10.1155/2021/5563651 Text en Copyright © 2021 Atefeh Amindoust 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
Amindoust, Atefeh
Asadpour, Milad
Shirmohammadi, Samineh
A Hybrid Genetic Algorithm for Nurse Scheduling Problem considering the Fatigue Factor
title A Hybrid Genetic Algorithm for Nurse Scheduling Problem considering the Fatigue Factor
title_full A Hybrid Genetic Algorithm for Nurse Scheduling Problem considering the Fatigue Factor
title_fullStr A Hybrid Genetic Algorithm for Nurse Scheduling Problem considering the Fatigue Factor
title_full_unstemmed A Hybrid Genetic Algorithm for Nurse Scheduling Problem considering the Fatigue Factor
title_short A Hybrid Genetic Algorithm for Nurse Scheduling Problem considering the Fatigue Factor
title_sort hybrid genetic algorithm for nurse scheduling problem considering the fatigue factor
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8034424/
https://www.ncbi.nlm.nih.gov/pubmed/33868622
http://dx.doi.org/10.1155/2021/5563651
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