Journal of Robotics, Networking and Artificial Life
Online ISSN : 2352-6386
Print ISSN : 2405-9021
Q-learning approach for Nurse Rostering: Addressing Variations in Work Patterns and Visualization of Results
Masato Nagayoshi Hisashi Tamaki
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JOURNAL OPEN ACCESS

2025 Volume 11 Issue 1 Pages 6-10

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Abstract
Creating a duty roster that meets all the various requirements of nurse rostering is extremely challenging. Consequently, many researchers have studied nurse rostering. Despite these efforts, the shift schedules generated by these studies are often not practical in their initial form, as they require adjustments to accommodate various constraints and evaluation criteria. Thus, we have proposed a method for revising duty roster using Q-learning in a constructive nurse rostering. This paper explores the potential for developing a practical duty roster that accommodates nurses with varying duty plan valuations. This involves considering each nurse's lifestyle. Additionally, we visualize the duty plan valuations of the revised rosters we obtain.
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© 2025 ALife Robotics Corporation Ltd.

この記事はクリエイティブ・コモンズ [表示 - 非営利 4.0 国際]ライセンスの下に提供されています。
https://creativecommons.org/licenses/by-nc/4.0/deed.ja
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