Trustworthy and insightful algorithms for industrial decision making
This joint PhD project will be based at KU Leuven with a minimum 12 month stay at The University of Melbourne.
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.
Cutting and packing problems are difficult optimization problems frequently occurring within logistics and production. These problems consider the cutting of smaller items from larger items (or packing smaller items within larger items) in one, two or three dimensions. Items can be regular or irregular in shape. Well known examples include the cutting of material (glass, wood, metal, …) and container/pallet loading. Although considerable research on this type of problems has been performed, the current state-of-the-art is far from optimal and many algorithmic challenges still exist. Especially, more insights on the general and industrial applicability of the existing algorithms are needed.
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).
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 impactThis doctoral research project will employ the promising methodology of Instance Space Analysis to cutting and packing problems. It will provide insights in the existing datasets from the literature as well as many real-world datasets obtained from Belgian and Australian companies active in the cutting and packing industry. In addition, this methodology will provide insights in the performance of algorithms on these datasets and will help to further improve them.
Problem modelling and algorithms will be studied at KU Leuven, while for the instance space analysis we rely on the expertise of the University of Melbourne.
The project will be complemented by The University of Melbourne based project and the collaboration will ensure a successful completion of the project
Principal Investigators (PIs)
Co-Principal Investigators (co-PIs)