As data science pervades modern society, companies are now shifting their focus from predictive analytics (when will something happen?) to prescriptive analytics (what should I do?). The prescriptive analytics challenges in industry often relate to scheduling multiple inter-related events in a complex network of systems, aiming to achieve seamless integration so that the right entities are in the right places at the right times. These problems are typically of enormous scale, involving thousands of inter-related variables and constraints and multiple conflicting objectives. Finding an optimal schedule requires developing efficient optimisation algorithms that can handle the enormous dimensions encountered in practice.
This project will focus on developing mathematical optimisation algorithms for a range of industrial applications. The selected PhD candidate will be embedded within a team of applied mathematicians who have extensive experience working with industry. In the past several years, the team has worked with a range of industry partners across the agriculture, mining, oil and gas, and manufacturing sectors. Some of the team�s prior work includes:
This PhD project will involve further developing the mathematical theory and optimisation algorithms underpinning these applications. The project is part of Professor Ryan Loxton's ARC Future Fellowship project.
For more information and to apply, click here.