< Back to previous page

Project

Trustworthy and insightful algorithms for industrial decision making

Efficient algorithms for complex personnel scheduling problems are critical for ensuring organisations can provide a suitably qualified workforce at minimal cost while satisfying a wide variety of strict regulations meeting occupational health and safety requirements, as well as employee preferences. This PhD project will focus on nurse rostering, where nurses must be optimally allocated to work shifts in a manner that ensures their specialised expertise is available when required to support scheduled surgeries and other activities, while satisfying rules about shift lengths, leave days, and accommodating as much as possible their individual preferences. The University of Melbourne has strong expertise in tackling related scheduling problems, but will benefit enormously from the specific expertise of the KU Leuven team developed over several decades in advancing models and algorithms for the nurse rostering problem. The partnership will enable new collaborative links with Melbourne based hospitals to be forged to support this PhD project, via the new ARC Training Centre in Optimisation Technologies, Integrated Methodologies and Applications (OPTIMA), in which the PhD student will be based. The KU Leuven team will also benefit from application of the University of Melbourne’s Instance Space Analysis methodology to gain deep insights into the strengths and weaknesses of nurse rostering algorithms, and ensure that the project’s newly developed algorithmic advances are rigorously “stress tested” to understand their robustness and reliability for a wide range of hospital settings in both Australia and Belgium.

Date:19 Nov 2021 →  30 Sep 2022
Keywords:Algorithms, Instance Space Analysis, Cutting and Packing, Nurse Rostering
Disciplines:Operations research and mathematical programming, Visual data analysis
Project type:PhD project