Summary of Robust Personnel Rostering: How Accurate Should Absenteeism Predictions Be?, by Martina Doneda et al.
Robust personnel rostering: how accurate should absenteeism predictions be?
by Martina Doneda, Pieter Smet, Giuliana Carello, Ettore Lanzarone, Greet Vanden Berghe
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed methodology uses a predict-then-optimize approach to evaluate the robustness of employee rosters generated by machine learning models. By assuming a predetermined prediction performance level and simulating model predictions based on characterization of performance, the method identifies the minimum required model performance for outperforming non-data-driven policies in nurse rostering problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary To help employees’ working hours adapt to last-minute changes caused by absenteeism, researchers propose a predict-then-optimize approach that uses machine learning models to schedule reserve shifts. The goal is to maximize roster robustness, and the method assumes a predetermined prediction performance level. By simulating model predictions based on performance characterization, the methodology evaluates the robustness of generated rosters and identifies the minimum required model performance for outperforming non-data-driven policies. |
Keywords
» Artificial intelligence » Machine learning
