Veröffentlichung

Titel:

Short-term deployment of nursing staff in outpatient clinics: A pool-based allocation approach

AutorInnen:

Kirstin Omlor
Markus Hilbert
Andreas Kleine

Kategorie:

Gesamtverzeichnis

Forschungsthema:

Anwendungen des Operations Research

 

Auflage:

In Operations Research, Data Analytics and Logistics, Volume 47, December 2026, 200509

Abstract:

This article analyzes the hourly allocation of nursing staff from a central pool to support a regional outpatient clinic network treating patients who receive serial care, specifically in situations of short-term staff shortages. Unlike in hospitals, the pool concept has seen little application in outpatient settings, even though limited human resources in these environments often lead to staff shortages and overtime. A central pool can cushion these effects by providing targeted, short-notice support. The novelty of our approach lies in a pool concept explicitly designed to flexibly compensate for short-term absences at the hourly level, based on individually declared staff availability and the clinics’ day-specific requirements.

To solve the resulting allocation problem, we develop, compare, and evaluate two binary optimization models with different index structures: A 2-index assignment-based model and a 3-index time-indexed model that match supply and demand while maximizing the total number of covered demanded hours. In a numerical study, we compare the models in terms of applicability, computational effort, and scalability, and we discuss techniques to improve computational performance, such as a preprocessing-and-fixing strategy as well as a divide-and-conquer decomposition approach. Possible model extensions, such as accounting for travel between outpatient clinics or re-optimizing with respect to social welfare, are discussed. The results indicate that the 3-index model, when combined with one of these techniques, is particularly well suited to cope with increasing problem complexity while retaining the flexibility needed for further algorithmic developments. Our contribution thus offers a conceptual and methodological basis for more flexible workforce planning in outpatient healthcare.

Download:

https://doi.org/10.1016/j.ordal.2026.200509