Abstract：
:The COVID-19 pandemic is viewed as the most basic worldwide disaster that humankind has observed since the second World War. There is no report of any clinically endorsed antiviral medications or antibodies that are successful against COVID-19. It has quickly spread everywhere, presenting tremendous well-being, financial, ecological, and social difficulties to the whole human populace. The COVID flare-up is seriously disturbing the worldwide economy. Practically all the countries are battling to hinder the transmission of the malady by testing and treating patients, isolating speculated people through contact following, confining huge social affairs, keeping up total or incomplete lockdown, etc. Proper scheduling of nursing workers and optimal designation of nurses may significantly affect the quality of clinical facilities. It is delivered by eliminating unbalanced workloads or undue stress, which could lead to decreased nurse performance and potential human errors., Nurses are frequently asked to leave while caring for all sick patients. However, regular scheduling formulas are not thought to consider this possibility because they are out of scheduling control in typical scenarios. In this paper, a novel model of the Hybrid Salp Swarm Algorithm and Genetic Algorithm (HSSAGA) is proposed to solve nurses' scheduling and designation. The findings of the suggested test function algorithm demonstrate that this algorithm has outperformed state-of-the-art approaches.
journal_name
Appl Soft Computjournal_title
Applied soft computingauthors
Abadi MQH,Rahmati S,Sharifi A,Ahmadi Mdoi
10.1016/j.asoc.2021.107449keywords:
["COVID-19","Designation","Genetic algorithm","Nurse","Salp swarm","Scheduling"]subject
Has Abstractpub_date
2021-09-01 00:00:00pages
107449eissn
1568-4946issn
1872-9681pii
S1568-4946(21)00372-0journal_volume
108pub_type
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