# Robust personnel rostering: how accurate should absenteeism predictions be?

arXiv: https://arxiv.org/abs/2406.18119 · HTML: https://mlsanity.com/paper/2406.18119/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 20 references that are in the map.
- First posted on arXiv 26 Jun 2024.

## Builds on (most cited first, 1 of 1)

- [Smart "Predict, then Optimize"](https://mlsanity.com/paper/1710.08005/) (2017)

## Built on by (most cited first, 2 of 2)

- [Prediction accuracy versus rescheduling flexibility in elective surgery management](https://mlsanity.com/paper/2507.15566/) (2025)
- [Simulating Classification Models for Ex-Ante Evaluation of Predict-Then-Optimize Methods](https://mlsanity.com/paper/2509.02191/) (2025)

## Abstract (arXiv)

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.