Personnel staffing and scheduling during disease outbreaks: A contact network-based analysis

Personnel scheduling in organizations can be disrupted by unforeseen events that require efficient planning. A recent example is the COVID-19 pandemic that disrupted global operations, compromising people's health and safety. Many organizations were forced to transition to full remote work to prevent the spread of the virus and ensure employee safety. Although working entirely remotely is effective for some organizations, others must balance workplace occupancy and infection risk to keep their operations functioning efficiently despite a global health crisis. We address this issue by developing a days-off scheduling model that captures employees' interactions through the underlying contact network. To solve the problem, we propose a Mixed Integer Linear Programming model considering a Microscopic Markov Chain Approach to determine the probability of infection in a contact network that mimics the employees' interactions. The model determines, during a given planning period, the optimal staffing mix to maximize occupancy while minimizing the risk of infection in the presence of testing protocols. We conduct sensitivity analysis to assess the approach's robustness while considering different contact networks and testing strategies. Through extensive computational analysis, we show that the degree of contact among employees is not the sole factor to consider when defining personnel scheduling policies during disease outbreaks. The decision-maker must balance the employee allocation with tailored testing interventions based on management's priorities to mitigate the effects while ensuring the desired occupancy at a lower risk.