Robust personnel rostering: how accurate should absenteeism predictions be?

• URL: http://arxiv.org/abs/2406.18119v1
• Date: Wed, 26 Jun 2024 07:16:18 GMT
• Title: Robust personnel rostering: how accurate should absenteeism predictions be?
• Authors: Martina Doneda, Pieter Smet, Giuliana Carello, Ettore Lanzarone, Greet Vanden Berghe,
• Abstract summary: 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. We assume a predict-then-optimize approach that uses absence predictions from a machine learning model to schedule an adequate number of reserve shifts.
• Score: 2.265037251840661
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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