A simple evolutionary algorithm with self-adaptation for multi-objective nurse scheduling

Landa-Silva, Dario; Le, Khoi N.

Abstract

We present a multi-objective approach to tackle a real-world nurse scheduling problem using an evolutionary algorithm. The aim is to generate a few good quality non-dominated schedules so that the decision-maker can select the most appropriate one. Our approach is designed around the premise of 'satisfying individual nurse preferences' which is of practical significance in our problem. We use four objectives to measure the quality of schedules in a way that is meaningful to the decision-maker. One objective represents staff satisfaction and is set as a target. The other three objectives, which are subject to optimisation, represent work regulations and workforce demand. Our algorithm incorporates a self-adaptive decoder to handle hard constraints and a re-generation strategy to encourage production of new genetic material. Our results show that our multi-objective approach produces good quality schedules that satisfy most of the nurses' preferences and comply with work regulations and workforce demand. The contribution of this paper is in presenting a multi-objective evolutionary algorithm to nurse scheduling in which increasing overall nurses' satisfaction is built into the self-adaptive solution method. © 2008 Springer-Verlag Berlin Heidelberg.

Citation

Landa-Silva, D., & Le, K. N. (2008). A simple evolutionary algorithm with self-adaptation for multi-objective nurse scheduling. In Adaptive and Multilevel Metaheuristics (133-155). Springer Verlag. https://doi.org/10.1007/978-3-540-79438-7_7

• Publication Date: Jul 3, 2008
• Deposit Date: Feb 10, 2020
• Publisher: Springer Verlag
• Pages: 133-155
• Series Title: Studies in Computational Intelligence
• Series Number: 136
• Book Title: Adaptive and Multilevel Metaheuristics
• ISBN: 978-3-540-79437-0
• DOI: https://doi.org/10.1007/978-3-540-79438-7_7
• Public URL: https://nottingham-repository.worktribe.com/output/3088171
• Publisher URL: https://link.springer.com/chapter/10.1007%2F978-3-540-79438-7_7