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Optimal Scheduling of Nursing Shifts : A Case Study on Work Scheduling at Haukeland University Hospital

Bergsvik, Natalie Alsaker; Bjøru, Linnéa Marie Wahlman
Master thesis
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URI
https://hdl.handle.net/11250/3054540
Date
2022
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  • Master Thesis [4657]
Abstract
This thesis presents a nurse scheduling problem tailored to characteristics common of

Norwegian hospitals. The problem involves allocating nurses to specific shifts to ensure

coverage of demand, while respecting work regulations and accounting for balance of

workload, general nurse preferences and fairness. We formulate models for the nurse

scheduling problem in line with the scheduling principles at Haukeland University Hospital

and solve them using mathematical programming techniques. The purpose lies in the attempt

to present a more efficient approach to the problem, compared to the manual scheduling

approach currently utilized at the hospital.

The method involves formulating a mixed integer programming model, which is implemented

computationally in an optimization software. Multiple decision models are produced to

represent two different forms of schedules, one cyclical and one calendar based. The model

for the cyclical schedule can be optimized directly using the solver of the software. The model

for the calendar-based schedule is more complex and is therefore solved by designing a

decomposition heuristic approach to find a good solution in a reasonable computational time.

The conclusion is that the schedules derived from the decision models are viable, with

emphasis on the considerable time savings compared to the current scheduling approach at

Haukeland. Currently, the hospital uses a manual method which takes approximately four to

six weeks to create a schedule, whereas the models proposed in this thesis are able to derive

an optimal solution within two hours. The models manage to effectively account for many

criteria, including work regulations, fairness, balance of workload and preferred practices. The

work in this thesis has been conducted through close cooperation with a representative from

the staffing department at Haukeland, and the solutions derived from the models are able to

capture their considerations in practice to a large extent. Our work has contributed to giving

staff at Haukeland insight on how optimization and computational tools can be used to deal

with their complex work scheduling problem.

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