Disruptions to personnel rosters caused by absenteeism often necessitate
last-minute adjustments to the employees' working hours. A common strategy to
mitigate the impact of such changes is to assign employees to reserve shifts:
special on-call duties during which an employee can be called in to cover for
an absent employee. To maximize roster robustness, we assume a
predict-then-optimize approach that uses absence predictions from a machine
learning model to schedule an adequate number of reserve shifts. In this paper
we propose a methodology to evaluate the robustness of rosters generated by the
predict-then-optimize approach, assuming the machine learning model will make
predictions at a predetermined prediction performance level. Instead of
training and testing machine learning models, our methodology simulates the
predictions based on a characterization of model performance. We show how this
methodology can be applied to identify the minimum performance level needed for
the model to outperform simple non-data-driven robust rostering policies. In a
computational study on a nurse rostering problem, we demonstrate how the
predict-then-optimize approach outperforms non-data-driven policies under
reasonable performance requirements, particularly when employees possess
interchangeable skills.

通过预测 - 优化方法评估人员排班的鲁棒性，以确定机器学习模型达到何种性能水平才能优于简单的非数据驱动的排班策略。