Journals / Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi / 2020 / Cilt: 38 - Sayı: 1

NURSE SCHEDULING AND RESCHEDULING UNDER UNCERTAINTY

BELİRSİZLİK VARKEN HEMŞİRE ÇİZELGELEME PROBLEMİ

Pages
75–95
Publication Date
2020-03-31
DOI
—
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AI Summary & Key Takeaways

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

The computational results show that the value of the stochastic solution can be more than 9% in some problem instances, and hence healthcare providers can benefit from using models that capture uncertainty rather than deterministic models.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Nurse planning decisions play a critical role on hospital budgeting, quality of nursing services and job satisfaction of nurses.
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Key Findings & Data:In this study, the integrated problem of nurse scheduling and rescheduling under demand uncertainty is considered.
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Conclusion & Impact:The computational results show that the value of the stochastic solution can be more than 9% in some problem instances, and hence healthcare providers can benefit from using models that capture uncertainty rather than deterministic models.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Hemşire Çizelgeleme · İki Aşamalı Rassal Programlama · Nurse Scheduling

Abstract

Nurse planning decisions play a critical role on hospital budgeting, quality of nursing services and job satisfaction of nurses. Nurse planning in a hospital is composed of four main phases: nurse budgeting, nurse scheduling (rostering), nurse rescheduling (staffing) and nurse assignment. In this study, the integrated problem of nurse scheduling and rescheduling under demand uncertainty is considered. The problem is formulated and solved as a two-stage stochastic integer program. The value of the stochastic solution is estimated using realistic problem instances over a monthly planning horizon generated based on the data provided by a private healthcare provider in Ankara, Turkey. The computational results show that the value of the stochastic solution can be more than 9% in some problem instances, and hence healthcare providers can benefit from using models that capture uncertainty rather than deterministic models.

Özet

Hemşire planlama, hastane bütçelemesinde, hastalara verilen hizmetin kalitesinde ve hemşirelerin iş memnuniyetinde kilit rol oynar. Bir hastanedeki hemşire planlama süreci temel olarak dört başlıktan oluşur: Bütçeleme, çizelgeleme, çizelgelerin güncellenmesi ve hemşire atama. Bu çalışmada, aylık çizelgelerin oluşturulması ve güncellenmesi problemi talep belirsizliği altında incelenmektedir. Bu entegre problem, iki aşamalı stokastik tamsayılı program olarak modellenmiş ve çözülmüştür. Aylık planlama ufkunu gözeten gerçekçi problem örnekleri kullanılarak gerçekleştirilen sayısal çalışmalarla belirsizliği modellemenin değeri ölçülmüştür. Elde edilen sonuçlar, belirsizliği modellemenin değerinin %9’u geçebileceğini ve bu nedenle stokastik modelleri kullanmanın oluşturulan çizelgelerin performansını önemli ölçüde iyileştirebileceğini göstermektedir.

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Cite This Article

BATUN, S., KARPUZ, E. (2020). NURSE SCHEDULING AND RESCHEDULING UNDER UNCERTAINTY. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Cilt: 38 - Sayı: 1, 75–95. https://ajindex.com/articles/670176

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