Personnel staffing and scheduling during COVID-19 pandemic: A contact network-based analysis

The COVID-19 pandemic has disrupted global operations, compromising people's health and safety. Several organizations, in particular, have been forced to shift their operations to a hybrid system (working from home) to prevent the spread of the virus and ensure employee safety. Although working from home is effective for some organizations, others need to find a balance between workplace occupancy and risk of infection to keep their operations functioning efficiently. We address this issue through contact network analysis by investigating the impact of employee interactions on virus spread in closed environments. We develop a staffing model for the scheduling of employees, considering contact networks. The goal is to maximize occupancy while minimizing the risk of infection. We aim to find the optimal composition of staff differing by priority to be allocated over a specified discrete-time horizon. We propose a Mixed Integer Non-Linear Programming (MINLP) model considering a Microscopic Markov Chain Approach (MMCA) to determine the probability of infection in a contact network based on the employee’s interactions. We assess the effectiveness of the approach through simulation, considering several contact network structures and interventions such as testing, vaccination, and personal protection. Through extensive computational analysis, we show that workplace occupancy can be efficiently balanced while keeping safety in the workplace.