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

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

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.

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

The COVID-19 pandemic - as a massive disruption - has significantly increased the need for medical services putting an unprecedented strain on health systems. This study presents a robust location-allocation model under uncertainty to increase the resiliency of health systems by applying alternative resources, such as backup and field hospitals and student nurses. A multi-objective optimization model is developed to minimize the system's costs and maximize the satisfaction rate among medical staff and COVID-19 patients. A robust approach is provided to face the data uncertainty, and a new mathematical model is extended to linearize a nonlinear constraint. The ICU beds, ward beds, ventilators, and nurses are considered the four main capacity limitations of hospitals for admitting different types of COVID-19 patients. The sensitivity analysis is performed on a real-world case study to investigate the applicability of the proposed model. The results demonstrate the contribution of student nurses and backup and field hospitals in treating COVID-19 patients and provide more flexible decisions with lower risks in the system by managing the fluctuations in both the number of patients and available nurses. The results showed that a reduction in the number of available nurses incurs higher costs for the system and lower satisfaction among patients and nurses. Moreover, the backup and field hospitals and the medical staff elevated the system's resiliency. By allocating backup hospitals to COVID-19 patients, only 37% of severe patients were lost, and this rate fell to less than 5% after establishing field hospitals. Moreover, medical students and field hospitals curbed the costs and increased the satisfaction rate of nurses by 75%. Finally, the system was protected from failure by increasing the conservatism level. With a 2% growth in the price of robustness, the system saved 13%.

Ergonomic Risk Minimization in the Portuguese Wine Industry: A Task Scheduling Optimization Method Based on the Ant Colony Optimization Algorithm

In the wine industry, task planning is based on decision-making processes that are influenced by technical and organizational constraints as well as regulatory limitations. A characteristic constraint inherent to this sector concerns occupational risks, in which companies must reduce and mitigate work-related accidents, resulting in lower operating costs and a gain in human, financial, and material efficiency. This work proposes a task scheduling optimization model using a methodology based on the ant colony optimization approach to mitigate the ergonomic risks identified in general winery production processes by estimating the metabolic energy expenditure during the execution of tasks. The results show that the tasks were reorganized according to their degree of ergonomic risk, preserving an acceptable priority sequence of tasks with operational affinity and satisfactory efficiency from the point of view of the operationalization of processes, while the potential ergonomic risks are simultaneously minimized by the rotation and alternation of operative teams between those tasks with higher and lower values of metabolic energy required. We also verified that tasks with lower ergonomic-load requirements influence the reorganization of the task sequence by lowering the overall value of metabolic energy, which is reflected in the reduction of the ergonomic load.