Staff scheduling for residential care under pandemic conditions: The case of COVID-19

Moosavi, Amirhossein; Öztürk, Onur; Patrick, Jonathan

doi:10.1016/j.omega.2022.102671

Cited by 11 publications

(5 citation statements)

References 55 publications

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“…Their model helps the TGH managers to determine the right number and selection of resources on a daily basis, as well as the day's optimal overtime. Moosavi et al [41] examined a staff scheduling problem for residential care during a pandemic, advising that residential care facilities raise staffing capacity for future pandemics.…”

Section: Literature Reviewmentioning

confidence: 99%

A resilient, robust transformation of healthcare systems to cope with COVID-19 through alternative resources

Ardakani

Larimi

Nejad

et al. 2023

Omega

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Section: Literature Reviewmentioning

confidence: 99%

A resilient, robust transformation of healthcare systems to cope with COVID-19 through alternative resources

Ardakani

Larimi

Nejad

et al. 2023

Omega

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“…For residential care facilities, ref. [ 15 ] developed a task scheduling model to minimize the number of employees assigned to residents to control the spread of the virus. To solve the model, the authors proposed a population-based heuristic algorithm that guarantees solution quality against benchmark solution approaches.…”

Section: Related Workmentioning

confidence: 99%

Personnel Scheduling during the COVID-19 Pandemic: A Probabilistic Graph-Based Approach

et al. 2023

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Effective personnel scheduling is crucial for organizations to match workload demands. However, staff scheduling is sometimes affected by unexpected events, such as the COVID-19 pandemic, that disrupt regular operations. Limiting the number of on-site staff in the workplace together with regular testing is an effective strategy to minimize the spread of infectious diseases like COVID-19 because they spread mostly through close contact with people. Therefore, choosing the best scheduling and testing plan that satisfies the goals of the organization and prevents the virus’s spread is essential during disease outbreaks. In this paper, we formulate these challenges in the framework of two Mixed Integer Non-linear Programming (MINLP) models. The first model aims to derive optimal staff occupancy and testing strategies to minimize the risk of infection among employees, while the second is aimed only at optimal staff occupancy under a random testing strategy. To solve the problems expressed in the models, we propose a canonical genetic algorithm as well as two commercial solvers. Using both real and synthetic contact networks of employees, our results show that following the recommended occupancy and testing strategy reduces the risk of infection 25–60% under different scenarios. The minimum risk of infection can be achieved when the employees follow a planned testing strategy. Further, vaccination status and interaction rate of employees are important factors in developing scheduling strategies that minimize the risk of infection.

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“…The existing studies in the literature are focused on developing scheduling policies to prevent the spread of the virus in organizations and closed spaces. For residential care facilities, Moosavi et al (2022) developed a task scheduling model to minimize the number of employees assigned to residents to control the spread of the virus. To solve the model, the authors proposed a population-based heuristic algorithm that guarantees solution quality against benchmark solution approaches.…”

Section: Introductionmentioning

confidence: 99%

Personnel Scheduling and Testing Strategy during Pandemics: The case of COVID-19

Davoodi¹,

Batista²,

Senapati³

et al. 2023

Preprint

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Efficient personnel scheduling plays a significant role in matching workload demand in organizations. However, staff scheduling is sometimes affected by unexpected events, such as the COVID-19 pandemic, that disrupt regular operations. Since infectious diseases like COVID-19 transmit mainly through close contact with individuals, an efficient way to prevent the spread is by limiting the number of on-site employees in the workplace along with regular testing. Thus, determining an optimal scheduling and testing strategy that meets the organization's goals and prevents the spread of the virus is crucial during disease outbreaks. In this paper, we formulate these challenges in the framework of two Mixed Integer Non-linear Programming (MINLP) models. The first model aims to derive optimal staff occupancy and testing strategies to minimize the risk of infection among employees, while the second model aims at only optimal staff occupancy under a random testing strategy. To solve the problems expressed in the models, we propose a canonical genetic algorithm as well as two commercial solvers. Using both real and synthetic contact networks of employees, our results show that following the recommended occupancy and testing strategy reduces the risk of infection 25%-60% under different scenarios.

show abstract

Staff scheduling for residential care under pandemic conditions: The case of COVID-19

Cited by 11 publications

References 55 publications

A resilient, robust transformation of healthcare systems to cope with COVID-19 through alternative resources

A resilient, robust transformation of healthcare systems to cope with COVID-19 through alternative resources

Personnel Scheduling during the COVID-19 Pandemic: A Probabilistic Graph-Based Approach

Personnel Scheduling and Testing Strategy during Pandemics: The case of COVID-19

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