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Trustworthy and insightful algorithms for industrial decision making

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. 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. This project will be primarily tackling cutting and packing problems, including the bin packing and container loading problems.

Date:15 Mar 2021  →  Today
Keywords:optimization, comparative analysis, algorithm selection, performance prediction, heuristics, bin packing
Disciplines:Operations research and mathematical programming
Project type:PhD project