Robust personnel rostering: how accurate should absenteeism predictions be?
<|reference_start|>Robust personnel rostering: how accurate should absenteeism predictions be?: 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.<|reference_end|>
@article{doneda2024robust,
    title={Robust personnel rostering: how accurate should absenteeism predictions
  be?},
    author={Martina Doneda, Pieter Smet, Giuliana Carello, Ettore Lanzarone and
  Greet Vanden Berghe},
    journal={arXiv preprint arXiv:2406.18119},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18119},
    primaryClass={cs.LG}
}