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Journals / Gazi Mühendislik Bilimleri Dergisi / 2026 / Volume: 9 Issue: 4 Special Issue

Genetic Algorithm-Based Optimization for Nurse Scheduling Problem

Gürcan Çetin(MUĞLA SITKI KOÇMAN ÜNİVERSİTESİ)0000-0003-3186-2781
Osman Özkaraca(MUĞLA SITKI KOÇMAN ÜNİVERSİTESİ)0000-0002-0964-8757
Ercüment Güvenç(MUGLA SITKI KOCMAN UNIVERSITY)0000-0003-0053-9623
Murat Sakal(MUGLA SITKI KOCMAN UNIVERSITY)0000-0001-6490-4795
Pages
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Publication Date
2023-12-31
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As a result, a software solution was presented that can successfully achieve an optimal nurse assignment, enabling the complete fulfillment of patients' care requirements in a given clinic.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The nursing workforce problem is essentially a scheduling problem.
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Key Findings & Data:The Genetic Algorithm-based software operates on a population basis, seeking to find the best schedule that satisfies various tasks and constraints.
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Conclusion & Impact:As a result, a software solution was presented that can successfully achieve an optimal nurse assignment, enabling the complete fulfillment of patients' care requirements in a given clinic.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:genetic algorithm · nurse scheduling

Abstract

The nursing workforce problem is essentially a scheduling problem. Scheduling problems involve the efficient planning and sequencing of specific resources, aiming to find the best time schedule that meets all constraints. Genetic Algorithm can be utilized to solve scheduling problems effectively. In this study, taking into account the success of the Genetic Algorithm in scheduling problems, a software has been developed in the Python environment to ensure the optimal assignment of nurses in clinics. The Genetic Algorithm-based software operates on a population basis, seeking to find the best schedule that satisfies various tasks and constraints. During the study, the planning of nursing staff considered the possibility of different clinics within the hospital, each dealing with patients requiring different care durations. It was assumed that a nurse works according to legal restrictions. Furthermore, a 4-week period was taken into consideration during the scheduling process, and the program was executed for a total of 28 days (a total working time of 160 hours). As a result, a software solution was presented that can successfully achieve an optimal nurse assignment, enabling the complete fulfillment of patients' care requirements in a given clinic.

Özet

The nursing workforce problem is essentially a scheduling problem. Scheduling problems involve the efficient planning and sequencing of specific resources, aiming to find the best time schedule that meets all constraints. Genetic Algorithm can be utilized to solve scheduling problems effectively. In this study, taking into account the success of the Genetic Algorithm in scheduling problems, a software has been developed in the Python environment to ensure the optimal assignment of nurses in clinics. The Genetic Algorithm-based software operates on a population basis, seeking to find the best schedule that satisfies various tasks and constraints. During the study, the planning of nursing staff considered the possibility of different clinics within the hospital, each dealing with patients requiring different care durations. It was assumed that a nurse works according to legal restrictions. Furthermore, a 4-week period was taken into consideration during the scheduling process, and the program was executed for a total of 28 days (a total working time of 160 hours). As a result, a software solution was presented that can successfully achieve an optimal nurse assignment, enabling the complete fulfillment of patients' care requirements in a given clinic.

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Cite This Article

Çetin, G., Özkaraca, O., Güvenç, E., Sakal, M. (2023). Genetic Algorithm-Based Optimization for Nurse Scheduling Problem. Gazi Mühendislik Bilimleri Dergisi, Volume: 9 Issue: 4 Special Issue. https://ajindex.com/articles/1397454

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