A Survey of the Nurse Rostering Solution Methodologies : The State-of-the-Art and Emerging Trends

• Main Authors: Man Ngoo, Chong, Leng Goh, Say, San Nah, Sze, Nasser R, Sabar, Salwani, Abdullah, Graham, Kendall
• Format: Article
• Language: English
• Published: IEEE 2022
• Subjects: QA Mathematics
• Online Access: http://ir.unimas.my/id/eprint/39052/1/A%20Survey%20-%20Copy.pdf; http://ir.unimas.my/id/eprint/39052/; https://ieeexplore.ieee.org/document/9780256
• Institution: Universiti Malaysia Sarawak

This paper presents an overview of recent advances for the Nurse Rostering Problem (NRP) based on methodological papers published between 2012 to 2021. It provides a comprehensive review of the latest solution methodologies, particularly computational intelligence (CI) approaches, utilized in benchmark and real-world nurse rostering. The methodologies are systematically categorised (Heuristics, Meta-heuristics, Hyper-heuristics, Mathematical Optimisation, Matheuristics and Hybrid Approaches). The NRP benchmark repositories and the respective state-of-the-art methods are also presented. A distinctive feature of this survey is its focus on the emerging trends in terms of solution methodologies and benchmark datasets. Meta-heuristics are the most popular choices in addressing NRP. Matheuristics, one of most popular methodologies in addressing the NRP, has been an emerging trend in recent years (2018 onwards). The INRC-I dataset is the most popular benchmark currently in use by researchers to test their algorithms. An indepth discussion on the challenges and research opportunities is provided. The summary and analysis of the recently published NRP methodological papers in this survey is valuable for the CI and Operational Research (OR) communities especially early career researchers seeking to find gaps and identify emerging trends in this fast-developing, important research area.