Staff Scheduling During a Pandemic

Abstract

The year 2019 revealed that some of the policies which have shaped the core structure of many organizations in different industries for a long time, could result in an absolute failure in an unprecedented crisis like the COVID-19 pandemic. In the light of such changes, the interaction between the people is a determining factor to limit an outbreak among the staff members of an organization to prevent any disruption in the process of the service/product they provide. Thus, an effective staff scheduling policy can be the clincher to achieve this goal. In this work, we consider a staff scheduling problem with the goal of minimizing the expected number of staff replacements that happens as a result of getting infected during a pandemic. In this days-off scheduling problem, we discuss a two-stage optimization approach where we first, determine the optimal scheduling patterns for the staff members and next, we will assign them to different resources so that the interaction between the staff members is minimized. In the proposed mathematical formulation for the problem, we consider the characteristics of the disease and the situation of the public health at different stages of the pandemic such as the incubation period, the probability of getting infected on a working day versus a rest-day, and the availability of swab tests. We design a column generation algorithm to solve the optimization model which requires up to 70% less computational power compared to the traditional algorithms that solve the problem when all available patterns are generated. A simulation model is also designed to compare the effectiveness of our suggested policies with the traditional scheduling policies. We examine our findings using data from the Grand River Regional Cancer Centre (GRRCC), which is a comprehensive cancer treatment and research centre located in Kitchener, Ontario. Particularly, we worked closely with the Department of Medical Physics and Radiation Oncology who plans and delivers radiation therapy treatments to cancer patients and treats over 2000 new patients annually. Our results show that depending on the different stages of a pandemic, the proposed staff scheduling policies can lead up to 20% less full-time equivalent staff replacements which have a significant impact on the availability of the centre's resources as well as the patient flow in long-term.