Trustworthy and insightful algorithms for industrial decision making
This joint PhD project will be based at The University of Melbourne with a minimum 12 month stay at KU Leuven
Please note: this position has been filled and will no longer accept applications.
Some of the most critical decision-making challenges in industry take the form of mathematical optimisation problems, which seek to efficiently determine optimal decisions from a huge number of choices. Often these problems have difficult and conflicting constraints that make even finding an acceptable solution challenging, let alone a provably optimal one. Complicating matters further, there are often several conflicting goals that must be considered to trade-off or balance economic, social and environmental outcomes.
The industrial optimisation problems tackled in this project include:
- Personnel scheduling problems, such as nurse rostering in hospitals (satisfying employment conditions and hospital requirements while maximising staff preferences);
- Cutting stock problems, such as optimal cutting of shapes in sheet metal for manufacturing (minimising wasted materials and thereby reducing economic and environmental costs);
- Packing problems, such as loading of items into container ships (minimising wasted space while satisfying safety constraints).
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.
Industry needs support from academic experts in optimisation in order to cast their industrial decision-making challenges onto a mathematical optimisation framework, and to access state-of-the-art optimisation technologies in the form of mathematical models and algorithms to find optimal solutions. However, it is critically important that the algorithms developed for one industry partner’s problem are rigorously stress-tested to:
- Establish the bounds of trust
- Understand robustness under future uncertainty
- Understand strengths and weaknesses of an algorithm under various conditions
- Gain insights into new algorithm ideas suited to particular conditions
By rigorously stress testing any algorithm, well beyond showing trust and reliability on the initial motivating industrial case study, there is an opportunity to develop innovative algorithms that generalise well to suit to a broader range of industry partners, and to achieve further impact
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.
The project will be complemented by the KU Leuven based project and the collaboration will ensure a successful completion of the project.
Principal Investigators (PIs)
Co-Principal Investigators (co-PIs)