The Nurse Rostering Problem: Recent Advances and Future Perspectives

The Nurse Rostering Problem, also referred to as Staff Scheduling or Personnel Timetabling in the literature, is a combinatorial optimization problem of great practical importance in many fields (healthcare, manufacturing, etc.) that has been addressed using a wide variety of methods over time. Solving this problem is essential for building cost-effective and balanced timetables that satisfy a diverse set of real-world constraints. This paper offers a comprehensive and in-depth analysis of the recent advancements and future research directions in the nurse rostering field, covering works published between 2019 and 2025 inclusively. While examining a substantial number of contributions, the study provides a special focus on emerging machine‑learning (ML) approaches, highlighting their role and performance in addressing this problem, their strengths in overcoming the limitations of state-of-the-art non-ML methods, as well as open challenges and opportunities related to their development and integration. To the best of our knowledge, this is the first survey that captures the emergence of machine‑learning methods in the nurse rostering field.